This managed AWS Kubernetes service scales, manages, and deploys containerized applications. Through EKS, you can run Kubernetes without installing or operating a control plane or worker nodes — significantly simplifying Kubernetes deployment on AWS.

So what does it all mean? What is the relationship between AWS and Kubernetes, what are the benefits of using Kubernetes with AWS, and what are the next steps when implementing AWS EKS? Let’s jump in.

Importance of container orchestration  

Container orchestration automates container movement, supervision, expansion, and networking. It can be used in every scenario where containers are used and will help you position the same applications across different environments. Today, Kubernetes remains the most popular container orchestration platform offered by Amazon Web Services (AWS), Google Cloud Platform, IBM Cloud, and Microsoft Azure.

As companies rapidly expand, the number of containerized applications they use also increases. However, managing them in larger quantities can become challenging. You’ll benefit from this process if your organization manages hundreds or thousands of containers. Data shows approximately 70% of developers use container orchestration tools. 

Due to its automation properties, container orchestration greatly benefits organizations. It reduces manhours, the number of employees needed, and the financial budget for containerized applications. It can also enhance the benefits of containerization, such as automated resource allocation and optimum use of computing resources.

An overview of Kubernetes

Often called K8s, Kubernetes is an open-source container orchestration tool and industry standard. Google developed this system to automate containerized applications- or microservices- development, management, and scaling. This platform was created for several reasons but was primarily developed with optimization in mind. Automating many DevOps processes, which developers once handled manually, has significantly simplified the work of software developers, allowing them to focus on more pressing, complex tasks.

Based on its applications, Kubernetes is the fastest-growing project in open-source software history after Linux. Data shows that from 2020 to 2021, the number of Kubernetes engineers skyrocketed by 67%, reaching 3.9 million. This figure represents 31% of all backend developers.

One of the main reasons Kubernetes is so popular is the increasing demand for businesses to support their microservice architecture. Kubernetes makes apps more flexible, productive, and scalable by providing load balancing and simplifying container management. 

Other benefits include:

• Container orchestration savings: Once Kubernetes is configured, apps run with minimal downtown while performing well.
• Increased efficiency among DevOps teams, allowing for faster development and deployment times.
• The ability to deploy workloads across several cloud services.
• Since Kubernetes is an open-source tool and community-led project, there is strong support for continuous improvement and innovation. A large ecosystem of tools has been designed to use with this platform.

What is EKS?

Data shows that of those running containers in the public cloud, 78% are using AWS, followed by Azure (39%), GCP (35%), IBM Cloud (6%), Oracle Cloud (4%), and Other (4%). AWS remains the dominant provider. 

AWS offers a commercial Kubernetes service — Amazon Elastic Kubernetes Service (EKS). This managed service allows you to run Kubernetes on AWS and on-premises, benefiting from the vast number of available services. By integrating with AWS services, you’ll benefit from supply scalability and security for your applications. For example, IAM is used for reliability, Elastic Load Balancer for load distribution, and Amazon ECR for container image. 

Adding a system like AWS EKS allows you to run Kubernetes applications on various systems, like AWS Fargate. Along with benefiting from greater performance, scalability, and reliability, you can integrate with AWS networking and security services such as AWS Virtual Private Cloud. It will enhance your Kubernetes system, which will optimize your business overall.  

AWS EKS can help you gain greater control over your servers or simplify cluster setup. 

Amazon EKS functionality

Amazon EKS simplifies Kubernetes management by handling the control plane while giving users flexibility over worker node configurations. Its architecture is designed for scalability, reliability, and seamless integration with the AWS ecosystem.

1. Core architecture

Amazon EKS operates through two primary components: the Kubernetes control plane and worker nodes.

• Kubernetes control plane: This plan is managed entirely by AWS and includes Kubernetes API servers and management services spread across multiple AWS Availability Zones, ensuring high availability.
• Worker nodes: These are deployed within a customer’s Amazon VPC, allowing full administrative control over scaling, upgrades, and security configurations.

2. Deployment options

Amazon EKS supports several deployment models to meet varying business needs:

• Managed node groups: AWS provisions, scales, and automatically manages worker nodes.
• Self-managed nodes: Users deploy and manage their own worker nodes with complete customization.
• Fargate: Serverless Kubernetes deployment where AWS manages both the control plane and the underlying infrastructure, enabling container execution without EC2 instances.
• Hybrid deployments: Kubernetes clusters can be extended to on-premises infrastructure using Amazon EKS Anywhere.

3. AWS service integrations

Amazon EKS integrates with a broad range of AWS services for enhanced functionality:

• Networking: Amazon VPC provides isolated networking environments, Elastic Load Balancing ensures traffic distribution, and AWS PrivateLink secures data exchange.
• Storage: Amazon EBS is used for persistent storage, Amazon S3 is used for object storage, and Amazon EFS is used for file storage.
• Security: IAM manages user access, AWS Key Management Service (KMS) secures sensitive data, and AWS Shield protects against DDoS attacks.
• Monitoring and logging: Amazon CloudWatch collects performance metrics, AWS CloudTrail tracks activity logs, and AWS X-Ray provides distributed tracing.

How does AWS EKS work with Kubernetes?

AWS EKS supplies an expandable and available Kubernetes control panel. For optimum performance, it runs this control panel across three availability zones. AWS EKS and Kubernetes collaborate in several different areas to ensure your company receives the best performance.

1. AWS Controller lets you manage and control your AWS service from your Kubernetes environment. Using AWS EKS, you can simplify building a Kubernetes application.
2. EKS can integrate with Kubernetes clusters. Developers can use it as a single interface to organize and resolve issues in any Kubernetes application implemented on AMS.
3. EKS add-ons are pieces of operational software. These add-ons will increase the functionality of Kubernetes operations. When you start an AMS cluster, you can select any applicable add-ons. Some of these add-ons include Kubernetes tools for networking and AWS service integrations.

Benefits of AWS EKS over standalone Kubernetes

There are several benefits of AWS EKS when compared to native Kubernetes.

• Implementing AWS EKS will remove time-consuming processes like creating the Kubernetes master cluster. With standalone Kubernetes, your employees would have to spend many company hours designing and building different infrastructures.
• AMS EKS eliminates a singular point of failure as the Kubernetes control plane is spread across various AWS availability zones.  
• EKS is embedded with a range of AWS monitoring services, which means it has scalability and can grow as your company expands. It makes features like AWS Identity Access Management and Elastic Load Balancing straightforward and convenient for your employees.

Amazon EKS use cases

Amazon EKS supports a variety of enterprise use cases, making it a versatile platform for running containerized applications. Below are some of the most common applications where Amazon EKS excels:

1. Deploying in hybrid environments

Amazon EKS enables consistent Kubernetes management across cloud, on-premises, and edge environments. This flexibility allows enterprises to run sensitive workloads on-premises while leveraging cloud scalability for other applications.

2. Supporting machine learning workflows

Amazon EKS simplifies the deployment of machine learning models by enabling scalable and efficient data processing. Frameworks like TensorFlow and PyTorch can run seamlessly on EKS, with access to AWS services like Amazon S3 for data storage and AWS SageMaker for model training and deployment.

3. Building web applications

Web applications benefit from Amazon EKS’s automatic scaling and high availability features. EKS supports microservices-based architectures, allowing developers to build and deploy resilient web applications using services such as Amazon RDS for databases and Amazon ElastiCache for caching.

4. Running CI/CD pipelines

Development teams can use Amazon EKS to build and manage CI/CD pipelines, automating software release processes. Integration with tools like Jenkins, GitLab, and CodePipeline ensures continuous integration and deployment for modern applications.

Amazon EKS best practices

To ensure smooth operation and maximum efficiency when managing Amazon EKS clusters, following best practices centered around automation, security, and performance optimization is essential. These practices help minimize downtime, improve scalability, and reduce operational overhead.

1. Automate Kubernetes operations

Automation reduces manual intervention and increases reliability. Infrastructure-as-code tools like Terraform or AWS CloudFormation can be used to define and deploy clusters. CI/CD pipelines can streamline code deployment and updates. Kubernetes-native tools like Helm can be used for package management, and ArgoCD can be used for GitOps-based continuous delivery.

2. Strengthen security

Securing your Kubernetes environment is crucial. Implement the following security best practices:

• Access control: Use AWS Identity and Access Management (IAM) roles and policies to manage access rights.
• Network security: Enable Amazon VPC for isolated network environments and restrict inbound/outbound traffic.
• Data encryption: Use AWS Key Management Service (KMS) for data encryption at rest and enforce TLS for data in transit.
• Cluster hardening: Regularly update Kubernetes versions and EKS node groups to apply the latest security patches.

3. Optimize cluster performance

Performance optimization ensures workloads run efficiently without overspending on resources. Consider the following strategies:

• Auto-scaling: Enable Kubernetes Cluster Autoscaler to adjust the number of worker nodes based on demand automatically.
• Right-sizing resources: Use AWS Compute Optimizer to recommend the best EC2 instance types and sizes.
• Monitoring and logging: Amazon CloudWatch and AWS X-Ray are used to monitor and trace application performance.

AWS EKS operation

AWS EKS has two main components — a control plane and worker nodes. The control plane has three Kubernetes master nodes that will be installed in three different availability zones. It runs on the cloud controlled by AMS. You cannot manage this control panel directly; it is managed through AMS.

The other component is worker nodes, which run on the organization’s private cloud and can be accessed through Secure Shell (SSH). The worker nodes control your organization’s containers, and the control panels organize and monitor the containers’ creation and place of origin.

As EKS operations are flexible, you can position an EKS cluster for every organization or use an EKS cluster from multiple applications. Without EKS, you would have to run and monitor the worker nodes and control panel, as it would not be automated. Implementing an EKS operation frees organizations from the burden of operating Kubernetes and all the infrastructure that comes with it. AWS does all the heavy lifting.

Here is how to get started with AWS EKS.

Amazon EKS pricing

Understanding Amazon EKS pricing is essential for effectively managing costs. Pricing is determined by various factors, including cluster management, EC2 instance types, vCPU usage, and additional AWS services used alongside Kubernetes.

Amazon EKS cluster pricing

All Amazon EKS clusters have a per-cluster, per-hour fee based on the Kubernetes version. Standard Kubernetes version support lasts for the first 14 months after release, followed by extended support for another 12 months at a higher rate.

Amazon EKS auto mode

EKS Auto Mode pricing is based on the duration and type of Amazon EC2 instances launched and managed by EKS Auto Mode. Charges are billed per second with a one-minute minimum and are independent of EC2 instance purchase options such as Reserved Instances or Spot Instances.

Amazon EKS hybrid nodes pricing

Amazon EKS Hybrid Nodes enable Kubernetes management across cloud, on-premises, and edge environments. Pricing is based on monthly vCPU-hour usage and varies by usage tier.

Other AWS services pricing

When using Amazon EKS, additional charges may apply based on the AWS services you use to run applications on Kubernetes worker nodes. For example:

• Amazon EC2: For instance capacity
• Amazon EBS: For volume storage
• Amazon VPC: For public IPv4 addresses

AWS Fargate pricing: Charges are based on vCPU and memory resources from container image download to pod termination, billed per second with a one-minute minimum.

To estimate your costs, use the AWS Pricing Calculator.

Maximize your Kubernetes investment with LogicMonitor 

AWS EKS is a system that can streamline and optimize your company. However, many need to be using it to its full potential. Monitoring will help you get the most out of your investment via key metrics and visualizations. 

LogicMonitor offers dedicated Kubernetes monitoring dashboards, including insights into Kubernetes API Server performance, container health, and pod resource usage. These tools provide real-time metrics to help you detect and resolve issues quickly, ensuring a reliable Kubernetes environment. These insights help drive operational efficiency, improve performance, and overcome common Kubernetes challenges.

Learn more here:

• LogicMonitor & AWS: Maximize your Kubernetes Investment with Monitoring
• LogicMonitor’s Kubernetes Monitoring Overview

If you need a cloud monitoring solution, LogicMonitor can help you maximize your investment and modernize your hybrid cloud ecosystem. Sign up for a free trial today!

Amazon Web Services (AWS) Kinesis is a cloud-based service that can fully manage large distributed data streams in real-time. This serverless data service captures, processes, and stores large amounts of data. It is a functional and secure global cloud platform with millions of customers from nearly every industry. Companies from Comcast to the Hearst Corporation are using AWS Kinesis.

What is AWS Kinesis? 

AWS Kinesis is a real-time data streaming platform that enables businesses to collect, process, and analyze vast amounts of data from multiple sources. As a fully managed, serverless service, Kinesis allows organizations to build scalable and secure data pipelines for a variety of use cases, from video streaming to advanced analytics.

The platform comprises four key components, each tailored to specific needs: Kinesis Data Streams, for real-time ingestion and custom processing; Kinesis Data Firehose, for automated data delivery and transformation; Kinesis Video Streams, for secure video data streaming; and Kinesis Data Analytics, for real-time data analysis and actionable insights. Together, these services empower users to handle complex data workflows with efficiency and precision.

To help you quickly understand the core functionality and applications of each component, the following table provides a side-by-side comparison of AWS Kinesis services:

How AWS Kinesis works

AWS Kinesis operates as a real-time data streaming platform designed to handle massive amounts of data from various sources. The process begins with data producers—applications, IoT devices, or servers—sending data to Kinesis. Depending on the chosen service, Kinesis captures, processes, and routes the data in real time.

For example, Kinesis Data Streams breaks data into smaller units called shards, which ensure scalability and low-latency ingestion. Kinesis Firehose, on the other hand, automatically processes and delivers data to destinations like Amazon S3 or Redshift, transforming and compressing it along the way.

Users can access Kinesis through the AWS Management Console, SDKs, or APIs, enabling them to configure pipelines, monitor performance, and integrate with other AWS services. Kinesis supports seamless integration with AWS Glue, Lambda, and CloudWatch, making it a powerful tool for building end-to-end data workflows. Its serverless architecture eliminates the need to manage infrastructure, allowing businesses to focus on extracting insights and building data-driven applications.

Security

Security is a top priority for AWS, and Kinesis strengthens this by providing encryption both at rest and in transit, along with role-based access control to ensure data privacy. Furthermore, users can enhance security by enabling VPC endpoints when accessing Kinesis from within their virtual private cloud.

Kinesis offers robust features, including automatic scaling, which dynamically adjusts resources based on data volume to minimize costs and ensure high availability. Furthermore, it supports enhanced fan-out for real-time streaming applications, providing low latency and high throughput.

Video Streams

What it is:

Amazon Video Streams offers users an easy method to stream video from various connected devices to AWS. Whether it’s machine learning, playback, or analytics, Video Streams will automatically scale the infrastructure from streaming data and then encrypt, store, and index the video data. This enables live, on-demand viewing. The process allows integrations with libraries such as OpenCV, TensorFlow, and Apache MxNet.

How it works:

The Amazon Video Streams starts with the use of the AWS Management Console. After installing Kinesis Video Streams on a device, users can stream media to AWS for analytics, playback, and storage. The Video Streams features a specific platform for streaming video from devices with cameras to Amazon Web Services. This includes internet video streaming or storing security footage. This platform also offers WebRTC support and connecting devices that use the Application Programming Interface. 

Data consumers: 

MxNet, HLS-based media playback, Amazon SageMaker, Amazon Rekognition

Benefits:

• There are no minimum fees or upfront commitments.
• Users only pay for what they use.
• Users can stream video from literally millions of different devices.
• Users can build video-enabled apps with real-time computer-assisted vision capabilities.
• Users can playback recorded and live video streams.
• Users can extract images for machine learning applications.
• Users can enjoy searchable and durable storage.
• There is no infrastructure to manage.

Use cases:

• Users can engage in peer-to-peer media streaming.
• Users can engage in video chat, video processing, and video-related AI/ML.
• Smart homes can use Video Streams to stream live audio and video from devices such as baby monitors, doorbells, and various home surveillance systems.
• Users can enjoy real-time interaction when talking with a person at the door.
• Users can control, from their mobile phones, a robot vacuum.
• Secure Video Streams provides access to streams using Access Management (IAM) and AWS Identity.
• City governments can use Video Streams to securely store and analyze large amounts of video data from cameras at traffic lights and other public venues.
• An Amber Alert system is a specific example of using Video Streams.
• Industrial uses include using Video Streams to collect time-coded data such as LIDAR and RADAR signals.
• Video Streams are also helpful for extracting and analyzing data from various industrial equipment and using it for predictive maintenance and even predicting the lifetime of a particular part.

Data firehose

What it is:

Data Firehose is a service that can extract, capture, transform, and deliver streaming data to analytic services and data lakes. Data Firehose can take raw streaming data and convert it into various formats, including Apache Parquet. Users can select a destination, create a delivery stream, and start streaming in real-time in only a few steps. 

How it works:

Data Firehose allows users to connect with potentially dozens of fully integrated AWS services and streaming destinations. The Firehose is basically a steady stream of all of a user’s available data and can deliver data constantly as updated data comes in. The amount of data coming through may increase substantially or just trickle through. All data continues to make its way through, crunching until it’s ready for visualizing, graphing, or publishing. Data Firehose loads data onto Amazon Web Services while transforming the data into Cloud services that are basically in use for analytical purposes.

Data consumers: 

Consumers include Splunk, MongoDB, Amazon Redshift, Amazon Elasticsearch, Amazon S3, and generic HTTP endpoints.

Benefits:

• Users can pay as they go and only pay for the data they transmit.
• Data Firehose offers easy launch and configurations.
• Users can convert data into specific formats for analysis without processing pipelines.
• The user can specify the size of a batch and control the speed for uploading data.
• After launching, the delivery streams provide elastic scaling.
• Firehose can support data formats like Apache ORC and Apache Parquet.
• Before storing, Firehose can convert data formats from JSON to ORC formats or Parquet. This saves on analytics and storage costs.
• Users can deliver their partitioned data to S3 using dynamically defined or static keys. Data Firehose will group data by different keys.
• Data Firehose automatically applies various functions to all input data records and loads transformed data to each destination.
• Data Firehose gives users the option to encrypt data automatically after uploading. Users can specifically appoint an AWS Key Management encryption key.
• Data Firehose features a variety of metrics that are found through the console and Amazon CloudWatch. Users can implement these metrics to monitor their delivery streams and modify destinations.

Use cases: 

• Users can build machine learning streaming applications. This can help users predict inference endpoints and analyze data.
• Data Firehose provides support for a variety of data destinations. A few it currently supports include Amazon Redshift, Amazon S3, MongoDB, Splunk, Amazon OpenSearch Service, and HTTP endpoints.
• Users can monitor network security with Event Management (SIEM) tools and supported Security Information.
• Firehose supports compression algorithms such as Zip, Snappy, GZip, and Hadoop-Compatible Snappy.
• Users can monitor in real-time IoT analytics.
• Users can create Clickstream sessions and create log analytics solutions.
• Firehose provides several security features.

Data streams

What it is:

Data Streams is a real-time streaming service that provides durability and scalability and can continuously capture gigabytes from hundreds of thousands of different sources. Users can collect log events from their servers and various mobile deployments. This particular platform puts a strong emphasis on security. Data streams allow users to encrypt sensitive data with AWS KMS master keys and a server-side encryption system. With the Kinesis Producer Library, users can easily create Data Streams.

How it works:

Users can create Kinesis Data Streams applications and other types of data processing applications with Data Streams. Users can also send their processed records to dashboards and then use them when generating alerts, changing advertising strategies, and changing pricing.

Data consumers:

Amazon EC2, Amazon EMR, AWS Lambda, and Kinesis Data Analytics

Benefits:

• Data Streams provide real-time data aggregation after loading the aggregate data into a map-reduce cluster or data warehouse.
• Kinesis Data Streams feature a delay time between when records are put in the stream and when users can retrieve them, which is approximately less than a second.
• Data Streams applications can consume data from the stream almost instantly after adding the data.
• Data Streams allow users to scale up or down, so users never lose any data before expiration.
• The Client Library supports fault-tolerant data consumption and offers support for scaling support Data Streams applications.

Use cases:

• Data Streams can work with IT infrastructure log data, market data feeds, web clickstream data, application logs, and social media.
• Data Streams provides application logs and a push system that features processing in only seconds. This also prevents losing log data even if the application or front-end server fails.
• Users don’t batch data on servers before submitting it for intake. This accelerates the data intake.
• Users don’t have to wait to receive batches of data but can work on metrics and application logs as the data is streaming in.
• Users can analyze site usability engagement while multiple Data Streams applications run parallel.
• Gaming companies can feed data into their gaming platform.

Data analytics

What it is:

Data Analytics provides open-source libraries such as AWS service integrations, AWS SDK, Apache Beam, Apache Zeppelin, and Apache Flink. It’s for transforming and analyzing streaming data in real time.

How it works:

Its primary function is to serve as a tracking and analytics platform. It can specifically set up goals, run fast analyses, add tracking codes to various sites, and track events. It’s important to distinguish Data Analytics from Data Studio. Data Studio can access a lot of the same data as Data Analytics but displays site traffic in different ways. Data Studio can help users share their data with others who are perhaps less technical and don’t understand analytics well.

Data consumers:

Results are sent to a Lambda function, Kinesis Data Firehose delivery stream, or another Kinesis stream.

Benefits:

• Users can deliver their streaming data in a matter of seconds. They can develop applications that deliver the data to a variety of services.
• Users can enjoy advanced integration capabilities that include over 10 Apache Flink connectors and even the ability to put together custom integrations.
• With just a few lines of code, users can modify integration abilities and provide advanced functionality.
• With Apache Flink primitives, users can build integrations that enable reading and writing from sockets, directories, files, or various other sources from the internet.

Use cases: 

• Data Analytics is compatible with the AWS Glue Schema Registry. It’s serverless and lets users control and validate streaming data while using Apache Avro schemes. This is at no additional charge.
• Data Analytics features APIs in Python, SQL, Scala, and Java. These offer specialization for various use cases, such as streaming ETL, stateful event processing, and real-time analytics.
• Users can deliver data to the following and implement Data Analytics libraries for Amazon Simple Storage Service, Amazon OpenSearch Service, Amazon DynamoDB, AWS Glue Schema Registry, Amazon CloudWatch, and Amazon Managed Streaming for Apache Kafka. 
• Users can enjoy “Exactly Once Processing.” This involves using Apache Flink to build applications in which processed records affect results. Even if there are disruptions, such as internal service maintenance, the data will still process without any duplicate data.
• Users can also integrate with the AWS Glue Data Catalog store. This allows users to search multiple AWS datasets
• Data Analytics provides the schema editor to find and edit input data structure. The system will recognize standard data formats like CSV and JSON automatically. The editor is easy to use, infers the data structure, and aids users in further refinement.
• Data Analytics can integrate with both Amazon Kinesis Data Firehose and Data Streams. Pointing data analytics at the input stream will cause it to automatically read, parse, and make the data available for processing. 
• Data Analytics allows for advanced processing functions that include top-K analysis and anomaly detection on the streaming data. 

AWS Kinesis vs. Apache Kafka

In data streaming solutions, AWS Kinesis and Apache Kafka are top contenders, valued for their strong real-time data processing capabilities. Choosing the right solution can be challenging, especially for newcomers. In this section, we will dive deep into the features and functionalities of both AWS Kinesis and Apache Kafka to help you make an informed decision.

Operation

AWS Kinesis, a fully managed service by Amazon Web Services, lets users collect, process, and analyze real-time streaming data at scale. It includes Kinesis Data Streams, Kinesis Data Firehose, and Kinesis Data Analytics. Conversely, Apache Kafka, an open-source distributed streaming platform, is built for real-time data pipelines and streaming applications, offering a highly available and scalable messaging infrastructure for efficiently handling large real-time data volumes.

Architecture

AWS Kinesis and Apache Kafka differ in architecture. Kinesis is a managed service with AWS handling the infrastructure, while Kafka requires users to set up and maintain their own clusters.

Kinesis Data Streams segments data into multiple streams via sharding, allowing each shard to process data independently. This supports horizontal scaling by adding shards to handle more data. Kinesis Data Firehose efficiently delivers streaming data to destinations like Amazon S3 or Redshift. Meanwhile, Kinesis Data Analytics offers real-time data analysis using SQL queries. 

Kafka functions on a publish-subscribe model, whereby producers send records to topics, and consumers retrieve them. It utilizes a partitioning strategy, similar to sharding in Kinesis, to distribute data across multiple brokers, thereby enhancing scalability and fault tolerance.

What are the main differences between data firehose and data streams?

One of the primary differences is in each building’s architecture. For example, data enters through Kinesis Data Streams, which is, at the most basic level, a group of shards. Each shard has its own sequence of data records. Firehose delivery stream assists in IT automation, by sending data to specific destinations such as S3, Redshift, or Splunk.

The primary objectives between the two are also different. Data Streams is basically a low latency service and ingesting at scale. Firehose is generally a data transfer and loading service. Data Firehose is constantly loading data to the destinations users choose, while Streams generally ingests and stores the data for processing. Firehose will store data for analytics while Streams builds customized, real-time applications. 

Detailed comparisons: Data Streams vs. Firehose

AWS Kinesis Data Streams and Kinesis Data Firehose are designed for different data streaming needs, with key architectural differences. Data Streams uses shards to ingest, store, and process data in real time, providing fine-grained control over scaling and latency. This makes it ideal for low-latency use cases, such as application log processing or real-time analytics. In contrast, Firehose automates data delivery to destinations like Amazon S3, Redshift, or Elasticsearch, handling data transformation and compression without requiring the user to manage shards or infrastructure.

While Data Streams is suited for scenarios that demand custom processing logic and real-time data applications, Firehose is best for bulk data delivery and analytics workflows. For example, Firehose is often used for IoT data ingestion or log file archiving, where data needs to be transformed and loaded into a storage or analytics service. Data Streams, on the other hand, supports applications that need immediate data access, such as monitoring dashboards or gaming platform analytics. Together, these services offer flexibility depending on your real-time streaming and processing needs.

Why choose LogicMonitor?

LogicMonitor provides advanced monitoring for AWS Kinesis, enabling IT teams to track critical metrics and optimize real-time data streams. By integrating seamlessly with AWS and CloudWatch APIs, LogicMonitor offers out-of-the-box LogicModules to monitor essential performance metrics, including throughput, shard utilization, error rates, and latency. These metrics are easily accessible through customizable dashboards, providing a unified view of infrastructure performance.

With LogicMonitor, IT teams can troubleshoot issues quickly by identifying anomalies in metrics like latency and error rates. Shard utilization insights allow for dynamic scaling, optimizing resource allocation and reducing costs. Additionally, proactive alerts ensure that potential issues are addressed before they impact operations, keeping data pipelines running smoothly.

By correlating Kinesis metrics with data from on-premises and other cloud performance services, LogicMonitor delivers holistic observability. This comprehensive view enables IT teams to maintain efficient, reliable, and scalable Kinesis deployments, ensuring seamless real-time data streaming and analytics.

Amazon Web Services (AWS) dominates the cloud computing industry with over 200 services, including AI and SaaS. In fact, according to Statista, AWS accounted for 32% of cloud spending in Q3 2022, surpassing the combined spending on Microsoft Azure, Google Cloud, and other providers.

A virtual private cloud (VPC) is one of AWS‘ most popular solutions. It offers a secure private virtual cloud that you can customize to meet your specific virtualization needs. This allows you to have complete control over your virtual networking environment.

Let’s dive deeper into AWS VPC, including its definition, components, features, benefits, and use cases.

What is a virtual private cloud?

A virtual private cloud refers to a private cloud computing environment within a public cloud. It provides exclusive cloud infrastructure for your business, eliminating the need to share resources with others. This arrangement enhances data transfer security and gives you full control over your infrastructure.

When you choose a virtual private cloud vendor like AWS, they handle all the necessary infrastructure for your private cloud. This means you don’t have to purchase equipment, install software, or hire additional team members. The vendor takes care of these responsibilities for you.

AWS VPC allows you to store data, launch applications, and manage workloads within an isolated virtualized environment. It’s like having your very own private section in the AWS Cloud that is completely separate from other virtual clouds.

AWS private cloud components

AWS VPC is made up of several essential components:

Subnetworks

Subnetworks, also known as subnets, are the individual IP addresses that comprise a virtual private cloud. AWS VPC offers both public subnets, which allow resources to access the internet, and private subnets, which do not require internet access.

Network access control lists

Network access control lists (Network ACLs) enhance the security of public and private subnets within AWS VPC. They contain rules that regulate inbound and outbound traffic at the subnet level. While AWS VPC has a default network NACL, you can also create a custom one and assign it to a subnet.

Security groups

Security groups further bolster the security of subnets in AWS VPC. They control the flow of traffic to and from various resources. For example, you can have a security group specifically for an AWS EC2 instance to manage its traffic.

Internet gateways

An internet gateway allows your virtual private cloud resources that have public IP addresses to access internet and cloud services. These gateways are redundant, horizontally scalable, and highly available.

Virtual private gateways

AWS defines a private gateway as “the VPN endpoint on the Amazon side of your Site-to-Site VPN connection that can be attached to a single VPC.” It facilitates the termination of a VPN connection from your on-premises environment.

Route tables

Route tables contain rules, known as “routes,” that dictate the flow of network traffic between gateways and subnets.

In addition to the above components, AWS VPC also includes peering connections, NAT gateways, egress-only internet gateways, and VPC endpoints. AWS provides comprehensive documentation on all these components to help you set up and maintain your AWS VPC environment.

AWS VPC features

AWS VPC offers a range of features to optimize your network connectivity and IP address management:

Network connectivity options

AWS VPC provides various options for connecting your environment to remote networks. For instance, you can integrate your internal networks into the AWS Cloud. Connectivity options include AWS Site-to-Site VPN, AWS Transit Gateway + AWS Site-to-Site VPN, AWS Direct Connect + AWS Transit Gateway, and AWS Transit Gateway + SD-WAN solutions.

Customize IP address ranges

You can specify the IP address ranges to assign private IPs to resources within AWS VPC. This allows you to easily identify devices within a subnet.

Network segmentation

AWS supports network segmentation, which involves dividing your network into isolated segments. You can create multiple segments within your network and allocate a dedicated routing domain to each segment.

Elastic IP addresses

Elastic IP addresses in AWS VPC help mitigate the impact of software failures or instance issues by automatically remapping the address to another instance within your account.

VPC peering

VPC peering connections establish network connections between two virtual private clouds, enabling routing through private IPs as if they were in the same network. You can create peering connections between your own virtual private clouds or with private clouds belonging to other AWS accounts.

AWS VPC benefits

There are several benefits to using AWS VPC:

Increased security

AWS VPC employs protocols like logical isolation to ensure the security of your virtual private cloud. The AWS cloud also offers additional security features, including infrastructure security, identity and access management, and compliance validation. AWS meets security requirements for most organizations and supports 98 compliance certifications and security standards, more than any other cloud computing provider.

Scalability

One of the major advantages of using AWS VPC is its scalability. With traditional on-premise infrastructure, businesses often have to invest in expensive hardware and equipment to meet their growing needs. This can be a time-consuming and costly process. However, with AWS VPC, businesses can easily scale their resources up or down as needed, without purchasing any additional hardware. This allows for more flexibility and cost-effectiveness in managing resources.

AWS also offers automatic scaling, which allows you to adjust resources dynamically based on demand, reducing costs and improving efficiency.

Flexibility

AWS VPC offers high flexibility, enabling you to customize your virtual private cloud according to your specific requirements. You can enhance visibility into traffic and network dependencies with flow logs, and ensure your network complies with security requirements using the Network Access Analyzer VPC monitoring feature. AWS VPC provides numerous capabilities to personalize your virtual private cloud experience.

Pay-as-you-go pricing

With AWS VPC, you only pay for the resources you use, including data transfers. You can request a cost estimate from AWS to determine the pricing for your business.

Comparison: AWS VPC vs. other cloud providers’ VPC solutions

When evaluating virtual private cloud solutions, understanding how AWS VPC compares to competitors like Azure Virtual Network and Google Cloud VPC is essential. Each platform offers unique features, but AWS VPC stands out in several critical areas, making it a preferred choice for many businesses.

AWS VPS

AWS VPC excels in service integration, seamlessly connecting with over 200 AWS services such as EC2, S3, Lambda, and RDS. This extensive ecosystem allows businesses to create and manage highly scalable, multi-tier applications with ease. AWS VPC leads the industry in compliance certifications, meeting 98 security standards and regulations, including HIPAA, GDPR, and FedRAMP. This makes it particularly suitable for organizations in regulated industries such as healthcare, finance, and government.

Azure Virtual Network

By comparison, Azure Virtual Network is tightly integrated with Microsoft’s ecosystem, including Azure Active Directory and Office 365. This makes it a strong contender for enterprises that already rely heavily on Microsoft tools. However, Azure’s service portfolio is smaller than AWS’s, and its networking options may not offer the same level of flexibility.

Google Cloud VPC

Google Cloud VPC is designed with a globally distributed network architecture, allowing users to connect resources across regions without additional configuration. This makes it an excellent choice for businesses requiring low-latency global connectivity. However, Google Cloud’s smaller service ecosystem and fewer compliance certifications may limit its appeal for organizations with stringent regulatory needs or diverse application requirements.

AWS VPC shines in scenarios where large-scale, multi-tier applications need to be deployed quickly and efficiently. It is also the better choice for businesses with strict compliance requirements, as its security measures and certifications are unmatched. Furthermore, its advanced networking features, including customizable IP ranges, elastic IPs, and detailed monitoring tools like flow logs, make AWS VPC ideal for organizations seeking a highly flexible and secure cloud environment.

AWS VPC use cases

Businesses utilize AWS VPC for various purposes. Here are some popular use cases:

Host multi-tier web apps

AWS VPC is an ideal choice for hosting web applications that consist of multiple tiers. You can harness the power of other AWS services to add functionality to your apps and deliver them to users.

Host websites and databases together

With AWS VPC, you can simultaneously host a public-facing website and a private database within the same virtual private cloud. This eliminates the need for separate VPCs.

Disaster recovery

AWS VPC enables network replication, ensuring access to your data in the event of a cyberattack or data breach. This enhances business continuity and minimizes downtime.

Beyond basic data replication, AWS VPC can enhance disaster recovery strategies by integrating with AWS Backup and AWS Storage Gateway. These services ensure faster recovery times and robust data integrity, allowing organizations to maintain operations with minimal impact during outages or breaches.

Hybrid cloud architectures

AWS VPC supports hybrid cloud setups, enabling businesses to seamlessly integrate their on-premises infrastructure with AWS. This allows organizations to extend their existing environments to the cloud, ensuring smooth operations during migrations or when scaling workloads dynamically. For example, you can use AWS Direct Connect to establish private, low-latency connections between your VPC and your data center.

DevOps and continuous integration/continuous deployment (CI/CD)

AWS VPC provides a secure and isolated environment for implementing DevOps workflows. By integrating VPC with tools like AWS CodePipeline, CodeBuild, and CodeDeploy, businesses can run CI/CD pipelines while ensuring the security and reliability of their applications. This setup is particularly valuable for teams managing frequent updates or deploying multiple application versions in parallel.

Secure data analytics and machine learning

AWS VPC can host secure environments for running data analytics and machine learning workflows. By leveraging services like Amazon SageMaker or AWS Glue within a VPC, businesses can process sensitive data without exposing it to public networks. This setup is ideal for organizations in sectors like finance and healthcare, where data privacy is critical.

AWS VPC deployment recommendations

Deploying an AWS VPC effectively requires following best practices to optimize performance, enhance security, and ensure scalability. Here are some updated recommendations:

1. Use security groups to restrict unauthorized access

• Configure security groups to allow only necessary inbound and outbound traffic to resources in your VPC.
• Apply the principle of least privilege by restricting access to specific IP addresses, protocols, and ports. For example, allow SSH (port 22) access only from a trusted IP range.

2. Implement multiple layers of security

• Use Network ACLs to provide an additional layer of protection at the subnet level.
• Combine these with security groups to create a layered security model, protecting resources from unauthorized access at both the instance and network level.

3. Leverage VPC peering for efficient communication

• Establish VPC peering connections to enable private communication between multiple VPCs within or across AWS accounts.
• Ensure route tables are correctly configured to enable seamless traffic flow between peered VPCs. Use this feature for scenarios like shared services or multi-region architectures.

4. Use VPN or AWS direct connect for hybrid cloud connectivity

• For hybrid cloud setups, establish Site-to-Site VPN connections or AWS Direct Connect to integrate your on-premises environment with your VPC.
• AWS Direct Connect offers lower latency and higher bandwidth, making it ideal for workloads requiring consistent performance.

5. Plan subnets for scalability and efficiency

• Allocate IPv4 CIDR blocks carefully to ensure sufficient IP addresses for future scaling. For example, reserve separate CIDR blocks for public, private, and database subnets.
• Divide subnets across multiple availability zones to increase availability and fault tolerance.

6. Enable VPC flow logs for monitoring

• Activate VPC Flow Logs to capture information about IP traffic going to and from network interfaces in your VPC.
• Use these logs to troubleshoot network connectivity issues, monitor traffic patterns, and enhance security by detecting unusual activity.

7. Optimize costs with NAT gateways

• Use NAT gateways to enable private subnet instances to access the internet without exposing them to inbound traffic.
• For cost-sensitive environments, consider replacing NAT Gateways with NAT Instances, although this requires more management effort.

8. Use elastic load balancing for high availability

• Deploy Elastic Load Balancers (ELBs) in public subnets to distribute traffic across multiple instances in private subnets.
• This improves scalability and ensures application availability during traffic spikes or failures.

9. Automate deployment with Infrastructure as Code (IaC)

• Use tools like AWS CloudFormation or Terraform to automate VPC setup and ensure consistency across environments.
• Version control your IaC templates to track changes and simplify updates.

10. Apply tagging for better resource management

• Assign meaningful tags to all VPC components, such as subnets, route tables, and security groups.
• Tags like Environment: Production or Project: WebApp make it easier to manage, monitor, and allocate costs.

By following these best practices, businesses can ensure that their AWS VPC deployments are secure, scalable, and optimized for performance. This approach also lays the groundwork for effectively managing more complex cloud architectures in the future.

Why choose AWS VPC?

AWS VPC offers a secure and customizable virtual private cloud solution for your business. Its features include VPC peering, network segmentation, flexibility, and enhanced security measures. Whether you wish to host multi-tier applications, improve disaster recovery capabilities, or achieve business continuity, investing in AWS VPC can bring significant benefits. Remember to follow the deployment recommendations provided above to maximize the value of this technology.

To maximize the value of your AWS VPC deployment, it’s essential to monitor and manage your cloud infrastructure effectively. LogicMonitor’s platform seamlessly integrates with AWS, offering advanced AWS monitoring capabilities that provide real-time visibility into your VPC and other AWS resources. 

With LogicMonitor, you can proactively identify and resolve performance issues, optimize your infrastructure, and ensure that your AWS environment aligns with your business goals.

AWS (Amazon Web Services) releases new products at an astounding rate, making it hard for users to keep up with best practices and use cases for those services. For IT teams, the risk is that they will miss out on the release of AWS services that can improve business operations, save them money, and optimize IT performance.

Let’s revisit a particularly underutilized service. Amazon’s T2 instance types are not new, but they can seem complicated to someone who is not intimately familiar. In the words of Amazon, “T2 instances are for workloads that don’t use the full CPU often or consistently, but occasionally need to burst to higher CPU performance.” This definition seems vague, though.  

What happens when the instance uses the CPU more than “often”? How is that manifested in actual performance? How do we reconcile wildly varying CloudWatch and OS statistics, such as those below?

Let’s dive in to explore these questions.

How CPU credits work on T2 instances

Amazon explains that “T2 instances’ baseline performance and ability to burst are governed by CPU credits. Each T2 instance receives CPU credits continuously, the rate of which depends on the instance size. T2 instances accumulate CPU credits when they are idle and use them when they are active. A CPU credit provides the performance of a full CPU core for one minute.” So the instance is constantly “fed” CPU credits and consumes them when the CPU is active. If the consumption rate exceeds the feeding rate, the CPUCreditBalance (a metric visible in CloudWatch) will increase. Otherwise, it will decrease (or stay the same). This dynamic defines T2 instances as part of AWS’s burstable instance family.

Let’s make this less abstract: Looking at a T2.medium, Amazon says it has a baseline allocation of 40% of one vCPU and earns credits at the rate of 24 per hour (each credit representing one vCPU running at 100% for one minute; so earning 24 credits per hour allows you to run the instance at the baseline of 40% of one vCPU). This allocation is spread across the two cores of the T2.medium instance. 

An important thing to note is that the CPU credits are used to maintain your base performance level—the base performance level is not given in addition to the credits you earn. So effectively, this means that you can maintain a CPU load of 20% on a dual-core T2.medium (as the two cores at 20% combine to the 40% baseline allocation). 

In real life, you’ll get slightly more than 20%, as sometimes you will be completely out of credits, but Amazon will still allow you to do the 40% baseline work. Other times, you will briefly have a credit balance, and you’ll be able to get more than the baseline for a short period.

For example, looking at a T2.medium instance running a high workload, so it has used all its credits, you can see from the LogicMonitor CloudWatch monitoring graphs that Amazon thinks this instance is constantly running at 21.7%:

This instance consumes 0.43 CPU credits per minute (with a constant balance of zero, so it consumes all the credits as fast as they are allocated). So, in fact, this instance gets 25.8 usage credits per hour (.43 * 60 minutes), not the theoretical 24.

AWS RDS instances also use CPU credits, but the calculation is a bit different and depends on instance size and class (general purpose vs memory optimized). The T2 burst model allows T2 instances to be priced lower than other instance types, but only if you manage them effectively.

Impact of CPU credit balance on performance

But how does this affect the instance’s performance? Amazon thinks the instance is running at CPU 21% utilization (as reported by CloudWatch). What does the operating system think?

Looking at operating system performance statistics for the same instance, we see a very different picture:



Despite what CloudWatch shows, utilization is not constant but jumps around with peaks and sustained loads. How can we reconcile the two? According to CloudWatch, the system uses 21% of the available node resources when it is running at 12% per the operating system and 21% when it is running at 80% per the operating system. Huh?

It helps to think of things a bit differently. Think of the 21% as “the total work that can be done within the current constraint imposed by the CPU credits.” Let’s call this 21 work units per second. The operating system is unaware of this constraint, so asking the OS to do the total work that can be done with 21 work units will get that done in a second and then be idle. It will think it could have done more work if it had more work—so it will report it was busy for 1 second, idle for the next 59 seconds—or 1.6% busy. 

However, that doesn’t mean the computer could have done 98% more work in the first second. Ask the computer to do 42 work units, and it will take 2 seconds to churn it out, so the latency to complete the task will double, even though it looks like the OS has lots of idle CPU power.

We can see this in simple benchmarks: On two identical T2.medium instances with the same workload, you can see very different times to complete the same work. One with plenty of CPU credits will complete a sysbench test much quicker:

sysbench --test=cpu --cpu-max-prime=2000 run

sysbench 0.4.12:
  multi-threaded system evaluation benchmark


Number of threads: 1


Maximum prime number checked in CPU test: 2000

Test execution summary:

    total time:                          1.3148s

    total number of events:              10000

While an identical instance, but with zero CPU credits, will take much longer to do the same work:

Test execution summary:

    total time:                          9.5517s

    total number of events:              10000

Both systems reported, from the OS level, 50% CPU load (single core of dual core system running at 100%). But even though they are identical ‘hardware’, they took vastly different amounts of time to do the same work.

This means a CPU can be “busy” but not work when it’s out of credits and finished its base allocation. It appears very similar to the “CPU Ready” counter in VMware environments, indicating that the guest OS has work to do but cannot schedule a CPU. After running out of CPU credits, the “idle” and “busy” CPU performance metrics indicate the ability to put more work on the processor queue, not the ability to do more work. And, of course, when you have more things in the queue, you have more latency.

Monitoring and managing CPU credit usage

So, clearly, you need to pay attention to the CPU credits. Easy enough to do if you are using LogicMonitor—the T2 Instance Credits DataSource does this automatically for you. (This may already be in your account, or it can be imported from the core repository.) This DataSource plots the CPU credit balance and the rate at which they are being consumed, so you can easily see your credit behavior in the context of your OS and CloudWatch statistics:

This DataSource also alerts you when you run out of CPU credits on your instance, so you’ll know if your sudden spike in apparent CPU usage is due to being throttled by Amazon or by an actual increase in workload.

What are burstable instances?

Burstable instances are a unique class of Amazon EC2 instances designed for workloads with variable CPU usage patterns. They come with a baseline level of performance and the ability to burst above it when your workload requires more CPU resources.

Each burstable AWS EC2 instance has a few baseline characteristics:

• Baseline performance: The base CPU performance level, which is a percentage of a full CPU core’s capacity
• CPU credits: Credits used to manage performance above the baseline level, given when the CPU usage is below the baseline
• Credit balance: The unused credits received due to performing below the baseline level

This capability makes burstable instances ideal for applications with a sometimes unpredictable traffic load. Some common use cases you see them used for include:

• Web servers with variable traffic patterns
• Small databases with occasional high-CPU operations from requests
• Development and test environments
• Microservices and containerized applications

T2s aren’t the only product that allows for burstable instances, either. They are also included in the following product families:

• T3
• T3a
• T4g

What are T3 instances?

T3 instances are Amazon’s next generation in the AWS T family of burstable instances. T3 offers improved performance and a better cost—making it a great choice for your business if you plan to start with AWS or upgrade your current instance.

T3 offers many benefits over T2:

• Better performance price: Get 30% better price-to-performance ratio compared to Amazon T2 instances
• Nitro system: Built on the Amazon Nitro systems to offer better networking and storage capabilities
• Unlimited mode: Run in “unlimited” mode by default to burst beyond baseline indefinitely for an added price
• Multiple processors: With T3 and T3a, get support for Intel and AMD processor lines

Overall, Amazon’s T3 lineup offers a substantial advantage over T2 in performance and cost. Look at your options to determine if it’s right for your organization.

Best practices for optimizing T2 instance performance

So, what do you do if you get an alert that you’ve run out of CPU credits? Does it matter? Well, like most things, it depends. If your instance is used for a latency-sensitive application, then this absolutely matters, as it means your CPU capacity is reduced, tasks will be queued, and having an idle CPU no longer means you have unused capacity. For some applications, this is OK. For some, it will ruin the end-user experience. So, having a monitoring system that can monitor all aspects of the system—the CloudWatch data, the OS-level data, and the application performance—is key.

Another note: T2 instances are the cheapest instance type per GB of memory. If you need memory but can handle the baseline CPU performance, running a T2 instance may be a reasonable choice, even though you consume all the CPU credits all the time.

Hopefully, that was a useful breakdown of the real-world effect of exhausting your CPU credits.

Managing observability across hybrid and multi-cloud environments is like flying a fleet of planes, each with different routes, altitudes, and destinations. You’re not just piloting a single aircraft; you’re coordinating across multiple clouds, on-premises systems, and services while ensuring performance, availability, and cost-efficiency. AWS customers, in particular, face challenges with workloads spanning multiple regions, data centers, and cloud providers. Having a unified observability platform that provides visibility across every layer is critical.

This is where LogicMonitor Envision excels. Its ability to seamlessly integrate observability across AWS, Azure, Google Cloud, and on-premises systems gives customers a comprehensive view of real-time performance metrics and logs, such as EC2 CPU utilization or Amazon RDS database logs. Additionally, LM Envision delivers visibility before, during, and after cloud migrations—whether you’re rehosting or replatforming workloads.

Let’s dive into how LogicMonitor makes managing these complex environments easier, focusing on features like Active Discovery, unified dashboards, and Cost Optimization.

The challenge of hybrid and multi-cloud: Coordinating your fleet across complex skies

Hybrid and multi-cloud environments are like managing multiple aircraft, each with its own systems and control panels. AWS workloads, on-prem servers, and Azure or Google Cloud applications have their own monitoring tools and APIs, creating silos that limit visibility. Without a unified observability platform, you’re flying blind, constantly reacting to issues rather than proactively managing your fleet.

Working at LogicMonitor, I’ve seen many customers struggle to manage hybrid environments. One customer managed 10,000 assets across multiple regions and cloud providers, using separate monitoring tools for AWS, on-prem, and their private cloud. They described it as “trying to control each plane separately without an overall view of the airspace.” (The analogy that inspired this blog!) This led to constant reactive management. By switching to LM Envision, they eliminated blind spots and gained complete visibility across their entire infrastructure, shifting to proactive management—the dream for ITOps teams everywhere.

Active Discovery: The radar system for automatically detecting new resources

Think of your infrastructure as an expanding airport. New terminals (services), planes (instances), and runways (connections) are constantly being added or modified. Manually tracking these changes is like trying to direct planes without radar. LM Envision simplifies this by automatically discovering AWS resources, on-prem data center infrastructure, and other cloud providers like Azure and Google Cloud. This visibility provides a comprehensive real-time view across services like Amazon EC2, AWS Lambda, and Amazon RDS.

Now, think of LM Envision’s Active Discovery as the radar system that continually updates as new planes enter your airspace. For example, when you’re spinning up new AWS EC2 instances for a major campaign, you don’t have to worry about manually adding those instances to your monitoring setup. LM Envision automatically detects them, gathers performance metrics, and sends real-time alerts. It’s like flying a plane—LM Envision is the instrument panel, providing instant feedback so you can make quick decisions. You’ll always have a clear view of performance, allowing you to react immediately and prevent potential outages, ensuring smooth operations from takeoff to landing.

Unified dashboards: The control tower for complete IT visibility

In any complex environment, especially hybrid or multi-cloud setups, visibility is key. LM Envision’s unified dashboards act like the control tower for your fleet, offering a single pane of glass across AWS, on-premises systems, Azure, and Google Cloud. These customizable dashboards allow you to track key performance metrics such as CPU utilization, database performance, and network latency across all your environments.

Think of these dashboards as your control tower. In a large airport, planes constantly land, take off, or taxi, and the control tower ensures everything runs smoothly. With LM Envision’s dashboards, you can monitor the health of your entire infrastructure in real time, from AWS EC2 instances to on-prem database health.

I’ve seen first-hand how these dashboards can transform operations. In one case, application latency spiked across multiple regions, but a customer’s traditional monitoring tools were siloed. They couldn’t easily tell if it was a network issue, a load balancer problem, or an AWS region failure. Once they implemented LM Envision, they built custom dashboards that provided insights into each layer of their stack, from the application down to the server and network level. When this issue happened again, within minutes, they isolated the root cause to an AWS load balancer misconfiguration in one region, drastically cutting troubleshooting time.

Cost optimization: The fuel gauge for efficient cloud spending

Managing costs in multi-cloud environments is like monitoring fuel consumption on long-haul flights—small inefficiencies can lead to massive overruns. AWS and Azure bills can quickly spiral out of control without proper visibility. LM Envision’s Cost Optimization tools, powered by Amazon QuickSight Embedded, provide a real-time view of your cloud spending. These dashboards enable you to identify idle EC2 instances, unattached EBS volumes, and other underutilized resources, ensuring you’re not wasting capacity.

LogicModules—with over 3,000 pre-configured integrations for technologies such as HPE, Cisco, NetApp, and AWS services—help monitor your infrastructure for the latest efficiencies. This allows you to right-size your cloud infrastructure based on real-time usage data.

In fact, a customer identified thousands of dollars in savings by using LM Envision’s cost forecasting tools, which provided actionable insights into resource usage. It’s like ensuring your planes fly with just the right amount of fuel and optimizing their routes to avoid costly detours.

Monitoring cloud migrations: Navigating turbulence with real-time insights

Cloud migrations can feel like flying through turbulence—downtime, cost overruns, and performance degradation are some common challenges. With LM Envision, you can monitor each step of the migration process, whether you’re rehosting or replatforming workloads to AWS.

I’ve seen multiple cloud migrations where resource usage spiked unpredictably. In one migration to AWS, a customer saw sudden increases in EC2 CPU usage due to unexpected workloads. LM Envision allowed them to monitor the migration in real-time and adjust instance types accordingly, avoiding major downtime. The system’s real-time alerts during migration help you navigate smoothly, much like flight instruments helping pilots adjust their routes during turbulence.

Wrapping up

Managing hybrid and multi-cloud environments is now the standard, and effective management requires an observability platform that scales with your infrastructure. LM Envision not only provides real-time visibility and cost optimization but also reduces complexity, making it easier for IT teams to manage distributed workloads proactively.

With LM Envision, you transition from being a reactive firefighter to a skilled pilot managing your fleet from the control tower. It ensures you keep your operations running smoothly, whether monitoring performance, scaling your infrastructure, or optimizing costs.

Amazon Redshift is a fast, scalable data warehouse in the cloud that is used to analyze terabytes of data in minutes. Redshift has flexible query options and a simple interface that makes it easy to use for all types of users. With Amazon Redshift, you can quickly scale your storage capacity to keep up with your growing data needs. 

It also allows you to run complex analytical queries against large datasets and delivers fast query performance by automatically distributing data and queries across multiple nodes. It allows you to easily load and transform data from multiple sources, such as Amazon DynamoDB, Amazon EMR, Amazon S3, and your transactional databases, into a single data warehouse for analytics. 

This data warehousing solution is easy to get started with. It offers a free trial and everything you need to get started, including a preconfigured Amazon Redshift cluster and access to a secure data endpoint. You can also use your existing data warehouses and BI tools with Amazon Redshift.Since Amazon Redshift is a fully managed service requiring no administrative overhead, you can focus on your data analytics workloads instead of managing infrastructure. It takes care of all the tedious tasks involved in setting up and managing a data warehouse, such as provisioning capacity, AWS monitoring and backing up your cluster, and applying patches and upgrades.

Contents

• What is Amazon Redshift?
• Key features of Amazon Redshift
• What is Amazon Redshift used for?
• What type of database is Amazon Redshift?
• What is a relational database management system?
• Is Redshift a SQL database?
• Which SQL does Redshift use?
• Is Redshift OLAP or OLTP
• What’s the difference between Redshift and a traditional database warehouse?

Amazon Redshift architecture

Amazon Redshift’s architecture is designed for high performance and scalability, leveraging massively parallel processing (MPP) and columnar storage. This architecture comprises the following components:

• Leader Node: The leader node receives queries from client applications and parses the SQL commands. It develops an optimal query execution plan, distributing the compiled code to the compute nodes for parallel processing. The leader node aggregates the results from the compute nodes and sends the final result back to the client application.
• Compute Nodes: Compute nodes execute the query segments received from the leader node in parallel. Each compute node has its own CPU, memory, and disk storage, which are divided into slices to handle a portion of the data and workload independently. Data is stored on the compute nodes in a columnar format, allowing for efficient compression and fast retrieval times.
• Node Slices: Compute nodes are partitioned into slices, each with a portion of the node’s memory and disk space. Slices work in parallel to execute the tasks assigned by the compute node, enhancing performance and scalability.
• Internal Network: Amazon Redshift uses a high-bandwidth network for communication between nodes, ensuring fast data transfer and query execution.

Key features of Amazon Redshift

• Columnar Storage: Data is stored in columns rather than rows, which reduces the amount of data read from disk, speeding up query execution. Columnar storage enables high compression rates, reducing storage costs and improving I/O efficiency.
• Massively Parallel Processing (MPP): Queries are executed across multiple compute nodes in parallel, distributing the workload and accelerating processing times. MPP allows Redshift to handle complex queries on large datasets efficiently.
• Data Compression: Redshift uses advanced compression techniques to reduce the size of stored data, minimizing disk I/O and enhancing performance. Automatic compression and encoding selection are based on data patterns, optimizing storage without user intervention.
• Automatic Distribution of Data and Queries: Redshift automatically distributes data and query load across all nodes in the cluster, balancing the workload and optimizing performance. Data distribution styles, such as key, even, and all, can be configured to align with specific use cases and data access patterns.
• Scalability: Redshift clusters can be easily scaled by adding or removing nodes, allowing organizations to adjust resources based on demand. Concurrency scaling enables automatic addition of transient capacity to handle peak workloads without performance degradation.
• Security: Redshift provides robust security features, including data encryption at rest and in transit, network isolation using Amazon VPC, and integration with AWS Identity and Access Management (IAM) for fine-grained access control. AWS Key Management Service (KMS) allows for the management and rotation of encryption keys.
• Integration with AWS Ecosystem: Redshift seamlessly integrates with other AWS services such as S3 for data storage, AWS Glue for data cataloging and ETL, and Amazon QuickSight for business intelligence and visualization. Integration with AWS CloudTrail and AWS CloudWatch provides logging, monitoring, and alerting capabilities.

What is Amazon Redshift used for?

Amazon Redshift is designed to handle large-scale data sets and provides a cost-effective way to store and analyze your data in the cloud. Amazon Redshift is used by businesses of all sizes to power their analytics workloads.

Redshift can be used for various workloads, such as OLAP, data warehousing, business intelligence, and log analysis. Redshift is a fully managed service, so you don’t need to worry about managing the underlying infrastructure. Simply launch an instance and start using it immediately.

Redshift offers many features that make it an attractive data warehousing and analytics option.

• First, it’s fast. Redshift uses columnar storage and parallel query processing to deliver high performance.
• Second, it’s scalable. You can easily scale up or down depending on your needs.
• Third, it’s easy to use. Redshift integrates with many popular data analysis tools, such as Tableau and Amazon QuickSight.
• Finally, it’s cost-effective. With pay-as-you-go pricing, you only pay for the resources you use.

What type of database is Amazon Redshift?

Amazon Redshift is one of the most popular solutions for cloud-based data warehousing solutions. Let’s take a close look at Amazon Redshift and explore what type of database it is.

First, let’s briefly review what a data warehouse is. A data warehouse is a repository for all of an organization’s historical data. This data can come from many sources, including OLTP databases, social media feeds, clickstream data, and more. The goal of a data warehouse is to provide a single place where this data can be stored and analyzed.

Two main databases are commonly used for data warehouses: relational database management systems (RDBMS) and columnar databases. Relational databases, such as MySQL, Oracle, and Microsoft SQL Server, are the most common. They store data in tables, each having a primary key uniquely identifying each row. Columnar databases, such as Amazon Redshift, store data in columns instead of tables. This can provide some performance advantages for certain types of queries.

So, what type of database is Amazon Redshift? It is a relational database management system. This means that it stores data in tables, each table has a primary key, and it is compatible with other RDBMSs. It is an open-source relational database optimized for high performance and analysis of massive datasets.

One of the advantages of Amazon Redshift is that it is fully managed by Amazon (AWS). You don’t have to worry about patching, upgrading, or managing the underlying infrastructure. It is also highly scalable, so you can easily add more capacity as your needs grow.

What is a relational database management system?

A relational database management system (RDBMS) is a program that lets you create, update, and administer a relational database. A relational database is a collection of data that is organized into tables. Tables are similar to folders in a file system, where each table stores a collection of information. You can access data in any order you like in a relational database by using the various SQL commands.

The most popular RDBMS programs are MySQL, Oracle, Microsoft SQL Server, and IBM DB2. These programs use different versions of the SQL programming language to manage data in a relational database.

Relational databases are used in many applications, such as online retail stores, financial institutions, and healthcare organizations. They are also used in research and development environments, where large amounts of data must be stored and accessed quickly.

Relational databases are easy to use and maintain. They are also scalable, which means they can handle a large amount of data without performance issues. However, relational databases are not well suited for certain applications, such as real-time applications or applications requiring complex queries.

NoSQL databases are an alternative to relational databases designed for these applications. NoSQL databases are often faster and more scalable than relational databases, but they are usually more challenging to use and maintain.

Is Redshift an SQL database?

Redshift is a SQL database that was designed by Amazon (AWS) specifically for use with their cloud-based services. It offers many advantages over traditional relational databases, including scalability, performance, and ease of administration.

One of the key features of Redshift is its relational database format, which allows for efficient compression of data and improved query performance. Redshift offers several other features that make it an attractive option for cloud-based applications, including automatic failover and recovery, support for multiple data types, and integration with other AWS.

Because Redshift is based on SQL, it supports all the standard SQL commands: SELECT, UPDATE, DELETE, etc. So you can use Redshift just like any other SQL database.

 Redshift also provides some features that are not available in a typical SQL database, such as:

• Automatic compression: This helps to reduce the size of your data and improve performance
• Massively parallel processing (MPP): This allows you to scale your database horizontally by adding more nodes
• User-defined functions (UDFs): These allow you to extend the functionality of Redshift with your own custom code
• Data encryption at rest: This helps to keep your data safe and secure

So, while Redshift is an SQL database, it is a very different database that is optimized for performance and scalability.

Which SQL does Redshift use?

Redshift uses PostgreSQL, specifically a fork known as Postgres 8.0.2. There are a few key reasons for this. First and foremost, Redshift is designed to be compatible with PostgreSQL so that users can easily migrate their data and applications from one database to the other. Additionally, PostgreSQL is a proven and reliable database platform that offers all of the features and performance that Redshift needs. And finally, the team at Amazon Web Services (AWS), who created Redshift, have significant experience working with PostgreSQL.

PostgreSQL is a powerful open-source relational database management system (RDBMS). It has many features make it a great choice for use with Redshift, such as its support for foreign keys, materialized views, and stored procedures. Additionally, the Postgres community is very active and supportive, which means there are always new improvements and enhancements being made to the software.

Redshift employs several techniques to further improve performance in terms of performance, such as distributing data across multiple nodes and using compression to reduce the size of data sets.

Is Redshift OLAP or OLTP

Most are familiar with OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing). Both are essential database technologies that enable organizations to manage their data effectively.

OLTP databases are designed for storing and managing transactional data. This data typically includes customer information, order details, product inventory, etc. An OLTP database focuses on speed and efficiency in processing transactions. To achieve this, OLTP databases typically use normalized data structures and have many indexes to support fast query performance. OLTP is designed for transactional tasks such as updates, inserts, and deletes.

OLAP databases, on the other hand, are designed for analytical processing. This data typically includes historical data such as sales figures, customer demographics, etc. An OLAP database focuses on providing quick and easy access to this data for analysis. To achieve this, OLAP databases typically use denormalized data structures and have a smaller number of indexes. OLAP is best suited for analytical tasks such as data mining and reporting.

Redshift is a powerful data warehouse service that uses OLAP capabilities. However, it is not just a simple OLAP data warehouse. Redshift can scale OLAP operations to very large data sets. In addition, Redshift can be used for both real-time analytics and batch processing.

What’s the difference between Redshift and a traditional database warehouse?

A traditional database warehouse is a centralized repository for all your organization’s data. It’s designed to provide easy access to that data for reporting and analysis. A key advantage of a traditional database warehouse is that it’s highly scalable, so it can easily support the needs of large organizations.

Redshift, on the other hand, is a cloud-based data warehouse service from Amazon. It offers many of the same features as a traditional database warehouse but is significantly cheaper and easier to use. Redshift is ideal for businesses looking for a cost-effective way to store and analyze their data.

So, what’s the difference between Redshift and a traditional database warehouse? Here are some of the key points:

Cost

Redshift is much cheaper than a traditional database warehouse. Its pay-as-you-go pricing means you only ever pay for the resources you use, so there’s no need to make a significant upfront investment.

Ease of use

Redshift is much easier to set up and use than a traditional database warehouse. It can be up and running in just a few minutes, and there’s no need for specialized skills or knowledge.

Flexibility

Redshift is much more flexible than a traditional database warehouse. It allows you to quickly scale up or down as your needs change, so you’re never paying for more than you need.

Performance

Redshift offers excellent performance thanks to its columnar data storage and massively parallel processing architecture. It’s able to handle even the most demanding workloads with ease.

Security

Redshift is just as secure as a traditional database warehouse. All data is encrypted at rest and in transit, so you can be sure that your information is safe and secure.

Amazon Redshift is a powerful tool for data analysis. It’s essential to understand what it is and how it can be used to take advantage of its features. Redshift is a type of Relational Database Management System or RDBMS. This makes it different from traditional databases such as MySQL.

While MySQL is great for online transaction processing (OLTP), Redshift is optimized for Online Analytical Processing (OLAP). This means that it’s better suited for analyzing large amounts of data.

What is Amazon Redshift good for?

The benefits of using Redshift include the following:

• Speed
• Ease of use
• Performance
• Scalability
• Security
• Pricing
• Widely adopted
• Ideal for data lakes
• Columnar storage
• Strong AWS ecosystem

What is Amazon Redshift not so good for?

Drawbacks include:

• It is not 100% managed
• Master Node
• Concurrent execution
• Isn’t a multi-cloud solution
• Choice of keys impacts price and performance

So, what is Amazon Redshift?

Amazon Redshift is a petabyte-scale data warehouse service in the cloud. It’s used for data warehousing, analytics, and reporting. Amazon Redshift is built on PostgreSQL 8.0, so it uses SQL dialect called PostgresSQL. You can also use standard SQL to run queries against all of your data without having to load it into separate tools or frameworks.

As it’s an OLAP database, it’s optimized for analytic queries rather than online transaction processing (OLTP) workloads. The benefits of using Amazon Redshift are that you can get started quickly and easily without having to worry about setting up and managing your own data warehouse infrastructure. The drawback is that it can be expensive if you’re not careful with your usage. 

It offers many benefits, such as speed, scalability, performance, and security. However, there are also some drawbacks to using Redshift. For example, it is not 100% managed and the choice of keys can impact price and performance. Nevertheless, Redshift is widely adopted and remains a popular choice for businesses looking for an affordable and scalable data warehouse solution.

To optimize your Amazon Redshift deployment and ensure maximum performance, consider leveraging LogicMonitor’s comprehensive monitoring solutions.

Book a demo with LogicMonitor today to gain enhanced visibility and control over your data warehousing environment, enabling you to make informed decisions and maintain peak operational efficiency.% managed and the choice of keys can impact price and performance. Nevertheless, Redshift is widely adopted and remains a popular choice for businesses looking for an affordable and scalable data warehouse solution.

Cloud computing is vast. It encompasses a huge variety of computing systems of different types and architectural designs. This complex computing network has transformed how we work and is a crucial part of our daily lives. For organizations, there are many ways to “cloud”, but let’s start with the basics of cloud computing; the internet cloud. This is generally categorized into three types:

1. Public cloud: Public cloud is a type of computing where resources are offered by a third-party provider via the internet and shared by organizations and individuals who want to use or purchase them.
2. Private cloud: A private cloud is a cloud computing environment dedicated to a single organization. In a private cloud, all resources are isolated and in the control of one organization.
3. Hybrid cloud: a combination of the two. This environment uses public and private clouds.

Cloud computing was created because the computing and data storage needs of organizations have become more business-critical and complex over time. Companies were beginning to install more physical storage and computing space, which became increasingly expensive and cumbersome. Cloud storage removes this burden.

Your confidential data is stored in a secure, remote location. It is “the cloud” to us, but it does live in a physical location. All this means is that it is housed by a third party, not on your premises. In most cases, you don’t know where this cloud is located. You can access programs, apps, and data over the internet as easily as if on your own personal computer.

The most common examples of cloud computing service models include Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). In most cases, organizations will leverage a combination of IaaS, PaaS, and SaaS services in their cloud strategy.

Contents

• What is a public cloud and its benefits and drawbacks?
• Who are the largest public cloud providers?
• What is a private cloud and its benefits and drawbacks?
• Who are the largest public cloud providers?
• What is the difference between a public and private cloud?
• What is a hybrid cloud and its benefits and drawbacks?
• Security concerns of a hybrid solution
• What is multi-cloud and its benefits and challenges?
• Making the right cloud choice

What is a public cloud?

Anything said to live in the cloud refers to documents, apps, data, and anything else that does not reside on a physical appliance, such as your computer, a server, a hard drive, etc. It lives in a huge data warehouse and is accessed only over the Internet. A public cloud does not mean that just anyone can log in, but it is more accessible than other types of clouds, which makes it the most popular.

A common use in business is document collaboration. You can upload and edit your documents, and give your collaborators an access link. Organizations of all sizes like this format because it provides:

• High scalability and elasticity: You do not need to worry about “running out of space” as you would with on-prem storage solutions
• Low-cost tier-based pricing: You can pay for only what you use, instead of needing to pre purchase capacity for future use

Public cloud services offered to consumers are often free or offered as a freemium or subscription-based service. Public cloud services offered to businesses usually carry a per-seat licensing cost. Their computing functionality can range from basic services such as email, apps, and storage to enterprise-level OS platforms or infrastructure environments your team can use for software development and testing (DevOps).

What are the benefits of a public cloud?

Public cloud offerings carry many benefits, enabling organizations to make progress on key business initiatives more quickly and efficiently. Benefits of public cloud adoption include:

• Saves time: When you use a public cloud, you don’t have to worry about managing hosting. Your cloud solution provider is entirely responsible for managing and maintaining the data center. This means there is no lengthy procurement process, and you don’t have to wait for operations to enable your operating system, there is no need to configure or assemble servers, and you never have to worry about establishing connectivity. Additionally, the technology allows developers to employ Agile workflows, significantly reducing lead times when testing, deploying, and releasing new products and updates.
• Saves you money: The amount of money organizations can save with the public cloud depends on the size of the operation. Large enterprises save millions a year, but with improper management, cost savings may not be realized at all. Take a look at your IT environment to see how the public cloud can save you money.
• No capital investments exist: You don’t need expensive equipment or extra physical space. Public cloud subscriptions are inexpensive to set up, and you only have to pay for the resources you use. Your infrastructure is transformed capital expenses into more affordable operating expenses.
• Most public cloud components are maintained and updated by the host. You are not responsible for any additional costs; everything is included in the subscription’s cost.
• Pay only for what you use: This eliminates paying for unused resources, and you always have the flexibility to scale up or down, giving you just the computing capacity you need.
• Lower energy costs: Without powering internal servers, you save money on energy costs.
• Free up IT time: IT talent can concentrate on more revenue-generating activities, instead of spending all of their time managing a data center.

Operating in the cloud is the best step forward for organizations. In addition to the benefits listed above, the cloud provides greater agility, higher efficiency, and more room to grow. When you are ahead of your competition in these areas, you can also be ahead in the market.

A “public” cloud is only accessible to people with your permission. Security is very tight. As recent history has shown, the majority of data leaks actually originate in-house. The public cloud offers:

• Strong cyber security: Attracting the most talented engineers in the world takes money. Engaging large security teams and the best security tools available is not a viable option for the average company. Cloud computing solves this problem. You benefit from having highly skilled IT professionals solely responsible for the protection of your public cloud infrastructure.
• Advanced technology creates more security innovations: More modern technology has led to advanced security services. Security innovations in the cloud are designed specifically for cloud-based solutions.
• Stringent penetration testing: Public clouds undergo stringent penetration tests and are held to stiffer standards than on-premise solutions or private cloud options. Private clouds are regularly slack in penetration testing because in-house breaches are assumed to be unexpected.
• Controlled access: The majority of data breaches result from human error. Critics claim that keeping data in-house allows better control, but the opposite is true. Data stored in the public cloud has fewer chances of falling into the wrong hands due to an employee’s mistake. As human control of your information decreases, so does your risk.

It should be noted that cloud security is a shared responsibility. Your cloud service provider is responsible for the security of the cloud, and you are responsible for your security in-house. Customers using cloud services need to understand they play a large role in securing their data and should ensure their IT team is properly trained.

Drawbacks of a public cloud

While public clouds offer numerous benefits, they do come with certain limitations:

• Security and privacy concerns: All types of clouds can be vulnerable to data breaches and cyber attacks, as data is stored on third-party servers, which might compromise sensitive information.
• Limited control: Users have limited control over the infrastructure and resources, making it difficult to customize the environment to meet specific requirements.
• Reliance on internet connectivity: A stable and reliable internet connection is essential to access public cloud services, and any disruption can affect performance and availability. This can be especially important for business operations in remote locations.
• Service downtime: Public cloud providers may experience service downtime due to hardware failures or maintenance activities, resulting in temporary loss of access to applications and data.
• Compliance and regulatory issues: Public cloud services may not meet certain compliance or regulatory requirements, which can create legal or contractual issues for businesses.
• Cost overruns: Billing is typically on a pay-per-use basis, leading to potential cost overruns if usage exceeds anticipated levels, particularly affecting mid-size to large enterprises.

Who are the largest public cloud providers?

The top cloud computing service providers are Amazon and Microsoft, closely followed by Google, Alibaba, and IBM. Let’s take a closer look at each:

• Amazon Web Services (AWS): AWS is an Amazon company that launched in 2002. It is currently the most popular cloud service provider in the world. It is the most comprehensive and widely adopted cloud platform that offers more than 165 full-featured services stored and provided by data centers worldwide. Millions of customers use this service globally.
• Microsoft Azure: Microsoft Azure launched many years later than AWS and Google Cloud but quickly rose to the top. It is one of the fastest-growing clouds of all. Azure offers hundreds of services within various categories, including AI and Machine Learning, Compute, Analytics, Databases, DevOps, Internet of Things, and Windows Virtual Desktop.
• Google Cloud Platform (GCP): Google’s cloud is similar to AWS and Azure. It offers many of the same services in various categories, including AI and Machine Learning, computing, virtualization, storage, security, and Life Sciences. GCP services are available in 20 regions, 61 zones, and over 200 countries.
• Alibaba Cloud: Alibaba was founded in 2009. It is registered and headquartered in Singapore. The company was originally built to serve Alibaba’s own e-commerce ecosystem but is now available to the public. It is the largest cloud server provider in China and offers various products and services in a wide range of categories. Alibaba is available in 19 regions and 56 zones around the world.
• IBM Cloud (IBM): IBM was founded in 1911 and is one of the oldest computer companies in the world. Its cloud platform, developed by IBM, is built on a set of cloud computing services designed for businesses. Similar to other cloud service providers, the IBM platform includes PaaS, SaaS, and IaaS as public, private, and hybrid models.

What is a private cloud?

The private cloud is a cloud solution that is dedicated to a single organization. You do not share the computing resources with anyone else. The data center resources can be located on your premises or off-site and controlled by a third-party vendor. The computing resources are isolated and delivered to your organization across a secure private network that is not shared with other customers.

The private cloud is completely customizable to meet the company’s unique business and security needs. Organizations are granted greater visibility and control into the infrastructure, allowing them to operate sensitive IT workloads that meet all regulations without compromising security or performance that could previously only be achieved with dedicated on-site data centers.

Private clouds are best suited for:

• Highly sensitive data
• Government agencies and other strictly regulated industries
• Businesses that need complete control and security over IT workloads and the underlying infrastructure
• Organizations that can afford to invest in high-performance technologies
• Large enterprises that need the power of advanced data center technologies to be able to operate efficiently and cost-effectively

What are the benefits of a private cloud?

The most common benefits of a private cloud include:

• Exclusive, dedicated environments: The underlying physical infrastructure for the private cloud is for your use only. Any other organizations cannot access it.
• Somewhat scalable: The environment can be scaled as needed without tradeoffs. It is highly efficient and able to meet unpredictable demands without compromising security or performance; however, not as scalable as public cloud.
• Customizable security: The private cloud complies with stringent regulations, keeping data safe and secure through protocol runs, configurations, and measures based on the company’s unique workload requirements.
• Highly efficient: The performance of a private cloud is reliable and efficient.
• Flexible: The private cloud can transform its infrastructure according to the organization’s growing and changing needs, enabled by virtualization

Drawbacks of a private cloud

As effective and efficient as the private cloud may be, some drawbacks exist. These include:

• Cost: A private cloud solution is quite expensive and has a relatively high total cost of ownership (TCO) compared to public cloud alternatives, especially in the short term. Private cloud infrastructure typically requires large capital expenditures in comparison to public cloud.
• Not very mobile-friendly: Many private cloud environments are built with strict security compliance requirements in mind, which may require users to initiate VPN connections in order to access the environment.
• Limited scalability: The infrastructure may not offer enough scalability solutions to meet all demands, especially when the cloud data center is restricted to on-site computing resources.

What is the difference between a public and private cloud?

A public cloud solution delivers IT services directly to the client over the Internet. This cloud-based service is either free, based on premiums, or by subscription according to the volume of computing resources the customer uses.

Public cloud vendors will manage, maintain, and develop the scope of computing resources shared between various customers. One central differentiating aspect of public cloud solutions is their high scalability and elasticity.

They are an affordable option with vast choices based on the organization’s requirements.

In comparison to legacy server technologies, a private cloud focuses on virtualization and thereby separates IT services and resources from the physical device. It is an ideal solution for companies that deal with strict data processing and security requirements. Private cloud environments allow for allocation of resources according to demand, making it a flexible option.

In almost all cases, a firewall is installed to protect the private cloud from any unauthorized access. Only users with security clearance are authorized to access the data on private cloud applications either by use of a secure Virtual Private Network (VPN) or over the client’s intranet, unless specific resources have been made available via the public internet.

What is a hybrid cloud?

A hybrid cloud is a computing environment that combines a physical data center, sometimes referred to as a private cloud, integrated with one or more public cloud environments. This allows the two environments to share access to data and applications as needed.

A hybrid cloud is defined as a mixed computing, storage, and services environment comprising a public cloud solution, private cloud services, and an on-premises infrastructure. This combination gives you great flexibility and control and lets you make the most of your infrastructure dollars.

What are the benefits of a hybrid cloud?

Although cloud services are able to save you a lot of money, their main value is in supporting an ever-changing digital business structure. Every technology management team has to focus on two main agendas: the IT side of the business and the business transformation needs. Typically, IT follows the goal of saving money. Whereas the digital business transformation side focuses on new and innovative ways of increasing revenues.

There are many differences between public, private, and hybrid clouds. The main benefit of a hybrid cloud is its agility. A business might want to combine on-premises resources with private and public clouds to retain the agility needed to stay ahead in today’s world. Having access to both private and public cloud environments means that organizations can run workloads in the environment that is most suitable to satisfy their performance, reliability, or security requirements.

Another strength of hybrid cloud environments is their ability to handle baseline workloads cost-efficiently, while still being able to provide burst capacity for periods of anomalous workload activity. When computing and processing demands increase beyond what an on-premises data center can handle, businesses can tap into the cloud to instantly scale up or down to manage the changing needs. It is also a cost-effective way of getting the resources you need without spending the time or money of purchasing, installing, and maintaining new servers that you may only need occasionally.

Drawbacks of a hybrid cloud

While hybrid cloud platforms offer enhanced security measures compared to on-premises infrastructures, they do come with certain challenges:

• Complexity: Setting up and managing a hybrid cloud can be complex, requiring integration between different cloud environments. This often demands specialized technical expertise and additional resources.
• Cost: Implementing and managing a hybrid cloud can be more expensive than using public or private clouds alone due to the need for extra hardware, software, and networking infrastructure. Additionally, organizations maintaining multiple types of cloud environments must also maintain multiple areas of expertise among technical staff, adding to related costs.
• Security Risks: All types of clouds can be vulnerable to security risks, such as data breaches or cyber-attacks, especially when there is a lack of standardization and consistency between the different cloud environments.
• Data Governance: Ensuring compliance with regulations such as GDPR or HIPAA can be challenging when managing data across multiple cloud environments.
• Network Performance: The reliance on communication between different cloud environments can lead to network latency and performance issues.
• Integration Challenges: Ensuring compatibility between applications and services across various cloud environments can be difficult.

Security concerns of a hybrid solution

Hybrid cloud platforms use many of the same security measures as on-premises infrastructures, including security information and event management (SIEM). In fact, organizations that use hybrid systems find that the scalability, redundancy, and agility provided by hybrid cloud environments lends to improved cybersecurity operations.

What is multi-cloud?

Having multiple vendors is a common practice these days. A multi-cloud architecture uses two or more cloud service providers. A multi-cloud environment can be several private clouds, several public clouds, or a combination of both.

The main purpose of a multi-cloud environment is to reduce the risks associated with relying on a single provider, and to capitalize on the strengths of different providers. With resources being distributed to different vendors, minimizing the chance of downtime, data loss, and service disruptions is possible. This redundancy ensures that the other services can still operate if one provider experiences an outage. Furthermore, different cloud service providers have different strengths, and having a multi-vendor cloud strategy allows organizations to use different vendors for different use-cases, as aligned with their strengths. Multi-clouds also increase available storage and computing power.

Benefits of multi-cloud environments

Adopting a multi-cloud strategy offers numerous benefits:

• Increased availability and resilience: If one provider’s services experience downtime, the workload can be shifted to another, minimizing the risk of complete downtime.
• Optimized performance: Cloud providers excel in their own areas. A multi-cloud approach allows you to optimize its performance by using the best service from each provider.
• Avoid vendor lock-in: By not being tied to a single provider, you can avoid lock-in and gain competitive pricing benefits. Cheaper services can be used for the less important tasks.
• Advanced regulatory compliance: A multi-cloud strategy allows you to scale workloads while allowing you to run workloads in the environment that best suits you from the perspective of regulatory compliance.
• Innovative capabilities: Different cloud providers will invest in different innovative products. A multi-cloud strategy allows you to leverage these innovations from each provider.

Challenges of multi-cloud environments

While multi-cloud environments provide significant advantages, they also present challenges such as:

• Complexity in management: It can be difficult to manage multiple cloud environments. You need expertise in handling integrations and monitoring.
• Interoperability issues: You must be able to achieve seamless interoperability. Applications and data need to move freely between cloud environments without facing compatibility issues.
• Cost management: Tracking and managing your costs across multiple cloud providers can be challenging. You need an effective strategy in place to avoid unexpected expenses. 

Making the right cloud choice

Understanding the differences between public, private, hybrid, and multi-cloud is crucial for selecting the best cloud strategy for your organization. Each strategy offers distinct advantages and challenges, from the scalability and cost-efficiency of public clouds to the security and customization of private clouds and the flexibility and control of hybrid clouds. By carefully evaluating your unique needs and objectives, you can make informed decisions that enhance your operations, bolster security, and drive innovation. As cloud technology advances, staying informed and adaptable will keep your organization competitive and efficient.

Ready to dive deeper into cloud computing?

Discover how hybrid observability can streamline your cloud migration strategies. Download “Agility and Innovation: How Hybrid Observability Facilitates Cloud Migration Strategies” and learn how to optimize your cloud journey confidently.

Enterprise generative artificial intelligence (GenAI) projects are gaining traction as organizations seek ways to stay competitive and deliver benefits for their customers. According to McKinsey, scaling these initiatives is challenging due to the required workflow changes. With AI adoption on the rise across industries, the need for robust monitoring and observability solutions has never been greater.

Why hybrid cloud observability matters

Hybrid cloud observability is a foundational partner as it provides comprehensive visibility over AI deployments across on-premises and cloud environments. LogicMonitor helps customers adopt and scale their GenAI investments with monitoring coverage of Amazon Bedrock. Visibility into Amazon Bedrock performance alongside other AWS services, on-prem infrastructure, and more lets users confidently experiment with their GenAI projects and quickly isolate the source of issues.

LogicMonitor’s hybrid cloud monitoring helps teams deliver AI with confidence

Hybrid cloud monitoring oversees IT infrastructure, networks, applications, and services across on-premises and cloud environments. With LogicMonitor’s hybrid cloud monitoring capabilities, customers gain a unified view of their entire IT landscape in one place. Visualizing resources in a single view helps customers quickly locate the root cause of problems and act on them to reduce project delays. For AI initiatives, this comprehensive hybrid cloud monitoring coverage gives teams:

• Unified visibility: A single pane of glass lets teams observe the performance, health, and usage of AI workloads regardless of their deployment location. This ensures that teams can easily monitor AI models, data pipelines, and compute resources across distributed environments.
• Proactive issue resolution: Real-time monitoring and alerting lets teams detect and address issues before they impact AI operations. Quickly identifying anomalies, resource constraints, or performance bottlenecks allows organizations to maintain the reliability and efficiency of their AI initiatives.
• Optimized resource utilization: Hybrid cloud monitoring helps organizations balance performance with resource utilization and costs. Dynamically offering insights and recommendations into resource consumption, usage, and workload performance is crucial for AI workloads, which usually increase in resource consumption as they scale. 

Unified view of AWS Bedrock services alongside other AWS services.  LogicMonitor’s Resource Explorer easily groups and filters resources to provide actionable insights.  Here we see active alerts for Bedrock and the top resource types and regions affected.

Accelerating AI with LogicMonitor and Amazon Bedrock 

Amazon Bedrock, a managed service from Amazon Web Services (AWS), allows teams to experiment with foundational models to build and deploy GenAI solutions easily. Amazon Bedrock lets teams accelerate their AI initiatives and drive innovation with pre-trained models, a wide range of compute options, and integration with hybrid cloud monitoring that enhances observability over AI models. 

LogicMonitor helps our customers unlock their GenAI adoption with monitoring coverage of Amazon Bedrock. The partnership between LogicMonitor and AWS ensures that customers can confidently deep dive into their GenAI projects, backed by the assurance of always-on monitoring across AWS resources to optimize functionality and quickly address issues that arise.

Benefits of combining LogicMonitor and Amazon Bedrock

For organizations adopting GenAI strategies, the combination of LogicMonitor Cloud Monitoring and Amazon Bedrock can modernize and scale AI projects with:

• Streamlined deployment and monitoring to ensure that AI models deliver consistent and reliable results
• Performance optimization over GenAI models so teams can improve resource utilization and fine-tune model parameters for better AI outcomes
• Proactive alerting so teams can detect anomalies or performance degradation in their GenAI models and maintain high performance and reliability

Out-of-the-box alerting for AWS Bedrock Services

LogicMonitor and AWS: Better together 

The alliance between LogicMonitor and AWS continues to thrive, with monitoring coverage for a wide array of commonly used and growing AWS services. Whether you are growing your AWS usage, maintaining business-critical on-premises infrastructure, or embracing cloud-native development, LogicMonitor is a strategic partner on your journey to help you visualize and optimize your growing AWS estate alongside your on-prem resources. LogicMonitor is available on AWS Marketplace.

Contact us to learn more on how LogicMonitor adds value to your AWS investments. 

Written by: Ismath Mohideen, Product Marketing Lead for Cloud Observability at LogicMonitor

Modern businesses are constantly looking for more efficiency and better performance in their daily operations. This is why embracing cloud computing has become necessary for many businesses. However, while there are numerous benefits to utilizing cloud technology, obstacles can get in the way.

Managing a cloud environment can quickly overwhelm organizations with new complexities. Internal teams need to invest substantial time and effort in regularly checking and monitoring cloud services, identifying and resolving issues, and ensuring optimal system performance.

This is where the power of serverless computing becomes evident. By using platforms like Amazon Web Services (AWS) Lambda, businesses can free themselves from worrying about the technical aspects of their cloud applications. This allows them to prioritize the excellence of their products and ensure a seamless experience for their customers without any unnecessary distractions.

What is Serverless Computing, and Why is it Important?

Serverless computing is an innovative cloud computing execution model that relieves developers from the burden of server management. This doesn’t mean that there are no servers involved. Rather, the server and infrastructure responsibilities are shifted from the developer to the cloud provider. Developers can focus solely on writing code while the cloud provider automatically scales the application, allocates resources, and manages server infrastructure.

The Importance of Serverless Computing

So why is serverless computing gaining such traction? Here are a few reasons:

• Focus on Core Product: Serverless computing allows developers to concentrate on their main product instead of managing and operating servers or runtimes in the cloud or on-premises. This can lead to more efficient coding, faster time to market, and better use of resources.
• Cost-Effective: With serverless computing, you only pay for the computing time you consume. There is no charge when your code is not running. This can result in significant cost savings compared to the traditional model of reserving a fixed amount of bandwidth or number of servers.
• Scalability: Serverless computing is designed to scale automatically. The system accommodates larger loads by simply running the function on multiple instances. This means businesses can grow and adapt quickly to changes without worrying about capacity planning.
• Reduced Latency: Serverless computing can reduce latency by allowing you to run code closer to the end user. You don’t have to send requests to a home server; instead, you can deploy functions in multiple geographic locations.

What is AWS Lambda?

Lambda is a serverless computing service that allows developers to run their code without having to provision or manage servers.

The service operates based on event-driven programming, executing functions in response to specific events. These events can range from changes in data within AWS services, updates from DynamoDB tables, and custom events from applications to HTTP requests from APIs.

AWS Lambda’s key features include:

• Autoscaling: AWS Lambda automatically scales your functions in response to the workload.
• Versioning and Aliasing: You can deploy different versions of your functions and use aliases for production, staging, and testing.
• Security: AWS Lambda ensures your code is always secure and encrypted.

How Does AWS Lambda Work?

AWS Lambda operates on an event-driven model. Essentially, developers write code for a Lambda function, which is a self-contained piece of logic, and then set up specific events to trigger the execution of that function.

The events that can trigger a Lambda function are incredibly diverse. They can be anything from a user clicking on a website, a change in data within an AWS S3 bucket, or updates from a DynamoDB table to an HTTP request from a mobile app using Amazon API Gateway. AWS Lambda can also poll resources in other services that do not inherently generate events.

When one of these triggering events occurs, AWS Lambda executes the function. Each function includes your runtime specifications (like Node.js or Python), the function code, and any associated dependencies. The code runs in a stateless compute container that AWS Lambda itself completely manages. This means that AWS Lambda takes care of all the capacity, scaling, patching, and administration of the infrastructure, allowing developers to focus solely on their code.

Lambda functions are stateless, with no affinity to the underlying infrastructure. This enables AWS Lambda to rapidly launch as many copies of the function as needed to scale to the rate of incoming events.

After the execution of the function, AWS Lambda automatically monitors metrics through Amazon CloudWatch. It provides real-time metrics such as total requests, error rates, and function-level concurrency usage, enabling you to track the health of your Lambda functions.

AWS Lambda’s Role in Serverless Architecture

AWS Lambda plays a pivotal role in serverless architecture. This architecture model has transformed how developers build and run applications, largely due to services like AWS Lambda.

Serverless architecture refers to applications that significantly depend on third-party services (known as Backend as a Service or “BaaS”) or on custom code that’s run in ephemeral containers (Function as a Service or “FaaS”). AWS Lambda falls into the latter category.

AWS Lambda eliminates the need for developers to manage servers in a serverless architecture. Instead, developers can focus on writing code while AWS handles all the underlying infrastructure.

One of the key benefits of AWS Lambda in serverless architecture is automatic scaling. AWS Lambda can handle a few requests per day to thousands per second. It automatically scales the application in response to the incoming request traffic, relieving the developer from the task of capacity planning.

Another benefit is cost efficiency. With AWS Lambda, you are only billed for your computing time. There is no charge when your code isn’t running. This contrasts with traditional cloud models, where you pay for provisioned capacity, whether or not you utilize it.

What is AWS CloudWatch

CloudWatch is a monitoring and observability service available through AWS. It is designed to provide comprehensive visibility into your applications, systems, and services that run on AWS and on-premises servers.

CloudWatch consolidates logs, metrics, and events to provide a comprehensive overview of your AWS resources, applications, and services. With this unified view, you can seamlessly monitor and respond to environmental changes, ultimately enhancing system-wide performance and optimizing resources.

A key feature of CloudWatch is its ability to set high-resolution alarms, query log data, and take automated actions, all within the same console. This means you can gain system-wide visibility into resource utilization, application performance, and operational health, enabling you to react promptly to keep your applications running smoothly.

How Lambda and CloudWatch Work Together

AWS Lambda and CloudWatch work closely to provide visibility into your functions’ performance.

CloudWatch offers valuable insights into the performance of your functions, including execution frequency, request latency, error rates, memory usage, throttling occurrences, and other essential metrics. It allows you to create dynamic dashboards that display these metrics over time and trigger alarms when specific thresholds are exceeded.

AWS Lambda also writes log information into CloudWatch Logs, providing visibility into the execution of your functions. These logs are stored and monitored independently from the underlying infrastructure, so you can access them even if a function fails or is terminated. This simplifies debugging.

By combining the power of CloudWatch with AWS Lambda, developers can gain comprehensive visibility into their serverless application’s performance and quickly identify and respond to any issues that may arise.

A Better Way to Monitor Lambda

While CloudWatch is a useful tool for monitoring Lambda functions, it can sometimes lack in-depth insights and contextual information, which can hinder troubleshooting efficiency.

LogicMonitor is an advanced monitoring platform that integrates with your AWS services. It provides a detailed analysis of the performance of your Lambda functions. With its ability to monitor and manage various IT infrastructures, LogicMonitor ensures a seamless user experience, overseeing servers, storage, networks, and applications without requiring your direct involvement.

So whether you’re using Lambda functions to power a serverless application or as part of your overall IT infrastructure, LogicMonitor can provide comprehensive monitoring for all your cloud services and give you the extra detail you need to maximize performance and optimize your cost savings.

Keeping up with the speed of business requires the right tools and tech. You expect efficiency gains when moving to and from the cloud, but risks and visibility gaps happen when resources are monitored by separate tools and teams. And since on-premises infrastructure is likely managed by dedicated IT teams and monitoring tools, you can’t clearly see if migrated resources perform correctly. The results involve disconnected visibility, tool sprawl, and increased MTTR. 

Holistic visibility is imperative for team agility, identifying anomalies, and resolving issues before your customers react. LogicMonitor provides this depth of visibility, wherever your business and customers demand, unifying monitoring across your hybrid multi cloud ecosystem.

Our expanded alliance with AWS

LogicMonitor lets IT and CloudOps teams confidently migrate with reduced risk, and oversee their post-migration estate on a unified platform. This enables customers to monitor efficiently across teams, quickly discover anomalies, and close visibility gaps. We announced additional monitoring coverage across a breadth of AWS services, as well as our involvement in the AWS Independent Software Vendor (ISV) Accelerate Program, a co-sell program for AWS partners with solutions that run on or integrate with AWS. Our participation in this program makes LogicMonitor easier to acquire, and aligns customer outcomes with mutual commitment from both AWS and LogicMonitor.
LogicMonitor’s SaaS-based, agentless platform helps you accelerate your AWS migration and reduce risk with full cloud visibility that scales alongside your on-premises investments. This relationship deepens Amazon CloudWatch visibility, giving you the power to control cloud costs, maintain uptime, and connect teams to data throughout business changes.

LogicMonitor’s Innovation for AWS

In addition to our partnership upgrade, our AWS monitoring capabilities have been significantly upgraded as well. Here are some of the highlights we announced at the AWS New York Summit.

Fast and easy to get started

Our alliance is thriving with comprehensive monitoring for an extensible array of commonly used and growing AWS services. LogicMonitor meets you at any stage of your hybrid cloud journey, whether you’re starting to migrate workloads and require storage, or even if your dev teams operate multiple Kubernetes clusters. You can quickly and automatically see performance and surface critical insights without requiring technical expertise with out-of-the-box dashboard coverage for nearly every AWS service that LogicMonitor supports. 

For visibility and operational efficiency post migration, deploy LogicMonitor’s monitoring for AWS Relational Database Service (RDS), Elastic Compute Cloud (EC2), networking services such as Elastic Load Balancer (ELB), Elastic Block Storage (EBS), Simple Storage Service (S3), and more. Monitoring includes pre-configured alert thresholds for immediately meaningful alerts, and inline workflows to view logs and metric data side by side to pinpoint root causes of errors and quickly troubleshoot. 

With LogicMonitor, you can extend beyond CloudWatch data and gain deeper insights into OS and app level metrics including disk usage, memory usage, and metrics for standard applications like Tomcat or MySQL. You know when you’re approaching limits and can quickly take action when taking advantage of these out-of-the-box benefits.

Best of all, we’ve made it even faster and easier to get started! You can significantly reduce onboarding time by bulk uploading multiple accounts into LogicMonitor via AWS Organizations and govern with Control Tower. 

Conveniently access coverage via the AWS Marketplace or directly through LogicMonitor. 

Control cloud costs

The cost of maintaining AWS resources is easier to predict and control as you scale. LogicMonitor helps you control cloud costs and prevent unexpected overages by presenting cloud spend alongside resources and utilization, with billing dashboards available out-of-the-box. Visualize total cloud spend, and for granular control, see costs aligned to operation, region, service, or tag. View over or underutilized resources to make informed decisions about changing resources according to business requirements.

Migrate confidently 

You can pinpoint what happened, where it happened, why it happened, and when it happened. 

New monitoring capabilities help you scale by clearly illustrating your AWS deployments. AWS topology mapping shows your connected AWS resources, helping you better understand your multi-pronged environment and isolate the location of errors for faster troubleshooting. Additionally, AWS logs integration allows for faster problem solving by presenting logs associated with alerts and anomalies, correlated alongside metrics. 

To improve customer experiences and website availability, we have enhanced AWS Route 53 coverage to include added support for hosted zones, including health checks and resolver component to quickly correct website traffic issues and maintain uptime. 

Scaling and adapting to your AWS deployment

You have flexibility and choice in deciding where to deploy Kubernetes clusters and continuously monitor throughout changes. Empower your DevOps teams with support for EKS monitoring for Kubernetes deployments and new support and coverage for EKS Anywhere to monitor on-premises Kubernetes deployments. 
Additionally, enhanced Kubernetes helm and scheduler monitoring provides greater coverage to monitor more elements in the cluster, providing deeper visibility to help you collaborate, troubleshoot faster, and prevent downtime.

We have also simplified the installation of Kubernetes monitoring for EKS, so that your ephemeral resources are monitored automatically throughout changes. This helps you continue migrating and expanding your AWS containerized deployments without worrying about reconfiguring clusters to effectively monitor them.  

Whether you are growing your AWS usage, maintaining business critical on-premises infrastructure, or embracing cloud native development across multiple clouds, LogicMonitor helps you clearly visualize across your growing AWS estate alongside your on-prem resources. 
Learn more about LM Cloud, watch a quick demo below, and contact us to get started.