Learn about AWS deployment strategies, serverless computing, containerized deployment, and infrastructure as code with AWS services like EC2, Lambda, Fargate, and CloudFormation.
When it comes to aws deployment, most developers would agree that understanding the wide range of AWS services available can be overwhelming.
The good news is, by learning the strengths of each AWS deployment option, you can choose the right strategies to meet your application needs.
In this post, you'll get a complete breakdown of EC2, Lambda, containers, and automation services to simplify AWS deployments. You'll learn guidelines to select the best services for your goals, optimizing performance, costs, and developer productivity.
AWS offers a wide range of deployment options to meet diverse application needs. Choosing the right services depends on factors like app architecture, scalability needs, budget and more. This section provides an overview to help guide your deployment decisions.
The AWS deployment process typically involves:
AWS provides pre-configured services, infrastructure as code tools like CloudFormation and third-party solutions to automate this process.
Compute
Storage & Database
Networking
Management & Automation
Key advantages of using AWS for deployment include:
Choosing the right AWS deployment architecture and tools is key to harnessing these benefits.
A deployment group in AWS is the entity used by AWS CodeDeploy to manage deployments of applications to EC2 instances or AWS Lambda functions. It allows you to group resources that should receive the same deployment packages for easier management.
Some key things to know about AWS deployment groups:
For example, you may have a deployment group for "production" and another for "staging" environments. When you deploy through CodeDeploy, you target the appropriate group.
Using deployment groups instead of manually targeting instances gives you more flexibility and control over your deployments. You can set up automatic shifting of traffic and rollback if failures occur.
Overall, AWS deployment groups are a powerful concept for managing deployments. They help group resources in a logical way to simplify targeting your CodeDeploy applications.
Deploying services on AWS can seem daunting, but following these steps will help guide you through the process:
Before deploying, make sure you have:
Be sure to monitor deployments and check logs for any issues. Consider setting up automatic scaling and continuous delivery pipelines.
With the proper groundwork laid out, deploying services on AWS can be straightforward. Let these steps guide you through the process.
Deploying data in AWS can be done in a few key ways depending on your use case. Here are some best practices:
Before deploying data, consider the following:
For most general data storage needs, Amazon Simple Storage Service (S3) is a great option. It offers high durability and availability for data at any scale.
For key-value and document NoSQL databases, DynamoDB provides fast performance at any scale with high availability. Its serverless nature helps reduce administrative overhead.
If you need a relational database like MySQL, PostgreSQL, Oracle, or SQL Server, Amazon RDS offers easy setup and management. It handles tasks like backups, software patching, automatic failure detection, and recovery.
For big data analytics, building a data lake with Amazon S3 and analyzing the data with services like Amazon EMR and Amazon Athena provides flexibility and scalability.
The key is choosing the right AWS data storage and processing services based on your specific data and workload requirements. With the wide range of options available, you can build data solutions optimized for performance, scale, security, and cost efficiency.
Deploying applications in AWS can be done in various ways depending on your specific needs. Here are some of the most common AWS deployment strategies:
This involves deploying a new version of the application directly on top of the existing version. The advantages are simplicity and speed, but the drawbacks are potential downtime and inability to quickly rollback.
This involves having two production environments, Blue and Green. You deploy the new version on the environment not currently serving production traffic. After testing, you switch the router to point to the new version. This allows rapid rollback if issues emerge.
You deploy the new version to a small subset of servers first while the existing version remains on other servers. You shift more traffic to the canary version once you've validated its performance. This gradual rollout allows monitoring of impacts.
You slowly roll out the new version to servers in the production environment one after another or in small batches rather than all at once. This may suit large server fleets and allows gradual capacity testing.
The new version is rapidly deployed to all servers simultaneously. This minimizes complexity but increases downtime risk if issues occur. Automated testing and rollback procedures are essential.
Choosing the most appropriate AWS deployment strategy depends on your application architecture, traffic levels, team skills, and rollout objectives. Testing deployment automation early is key to smooth updates.
Amazon EC2 provides highly customizable and flexible virtual machine instances for deploying applications on AWS infrastructure. As one of the core AWS compute services, EC2 offers multiple options to meet diverse application deployment needs. We'll explore some of the primary methods for application deployment using EC2.
Deploying applications on EC2 generally involves these key steps:
The basic method is manual deployment by:
Automating deployments improves efficiency and consistency by using:
Automation enables replicable and scalable application deployment on EC2.
Optimizing AWS deployment on EC2 instances can improve performance, availability and reduce costs. Some key optimization strategies include:
Continually evaluating instance sizing, automated scaling policies and load balancing helps optimize application deployment on EC2.
Amazon EC2 Auto Scaling allows defining automated rules to scale EC2 instances based on metrics like CPU, memory, traffic etc.
Key benefits include:
Auto Scaling enables deploying the right amount of instances needed to maintain application performance and reduce resource over-provisioning.
Amazon EC2 Placement Groups allow deploying instances within logical grouping for higher bandwidth, lower latency and improved network throughput between instances.
This provides benefits like:
For deploying high-performance applications on EC2, placement groups can help optimize the network experience.
Exploring serverless computing options provided by AWS for deploying applications without managing servers.
AWS Lambda allows you to run code without provisioning or managing servers. You pay only for the compute time you consume - there is no charge when your code is not running.
To get started with AWS Lambda:
Some key benefits of AWS Lambda:
Overall, AWS Lambda is a simple and efficient way to deploy serverless applications.
Amazon API Gateway provides a scalable, low latency API service to connect your Lambda functions and other AWS services to mobile and web applications.
To integrate API Gateway with Lambda:
Now your client apps can access the API, which then triggers the Lambda function. Some key benefits:
API Gateway removes complexity of deploying APIs and complements Lambda nicely.
Some major benefits of using AWS serverless services like Lambda and API Gateway:
Serverless helps teams focus on product innovation rather than infrastructure.
The AWS Serverless Application Repository is a service that hosts serverless applications packaged as AWS CloudFormation templates.
It enables you to quickly deploy code samples, components and complete apps for common use cases:
Using the repository accelerates your serverless development by letting you stand up complete apps with backend and infrastructure quickly.
Deploying containerized applications can provide increased portability, scalability, and infrastructure efficiency. AWS offers several managed services to simplify container deployments without managing servers.
AWS Fargate allows developers to deploy containers without provisioning or managing servers. Fargate will automatically scale infrastructure to meet application demands.
Key benefits of Fargate:
To deploy containers with Fargate:
Fargate removes infrastructure management burdens, allowing developers to focus on their applications.
Amazon Elastic Container Service (ECS) and Amazon Elastic Kubernetes Service (EKS) help run, scale, and manage containers.
ECS natively integrates with other AWS services. It allows defining task definitions to deploy containers as services or batch jobs. ECS handles cluster management, capacity provisioning, load balancing, scaling, and monitoring.
EKS provides managed Kubernetes control plane for running Kubernetes worker nodes across AWS compute services. This allows leveraging Kubernetes for container orchestration while EKS handles management of the Kubernetes infrastructure.
Both ECS and EKS integrate with AWS Fargate, EC2, and other services. Choose ECS for simpler AWS-native orchestration or EKS for portable Kubernetes-centric workflows.
AWS App Runner is a fully managed service that makes deploying containerized web applications and APIs easier without managing servers.
Key features:
App Runner allows focusing on code while AWS handles infrastructure, networking, CI/CD pipelines, and scaling. It's ideal for quickly deploying containers without ops overhead.
Apply CI/CD best practices to container workflows:
Following CI/CD methodology allows safely automating container deployments while minimizing risks.
In summary, AWS provides multiple services for running containers without managing servers like Fargate, ECS, EKS, and App Runner. Combine with CI/CD practices for automated container deployment pipelines.
AWS offers a suite of Code services designed to automate various aspects of the deployment pipeline to streamline delivery of applications. These services facilitate continuous integration, delivery, and deployment while also handling critical components like building, testing, and version control.
AWS CodePipeline provides a visual workflow to model end-to-end release processes. It automates the build, test, and deployment phases whenever code changes are pushed to the repository. Key benefits include:
By leveraging CodePipeline, teams can focus less on manual processes and more on developing code.
AWS CodeDeploy automates application deployments to EC2, Lambda, ECS, on-premises servers, etc. It facilitates rapid releases while minimizing downtime through blue/green deployment strategies.
CodeDeploy takes care of the complexities around deploying applications, letting developers concentrate on building features.
AWS CodeBuild compiles source code, runs tests, and produces software packages ready for deployment. It completely manages the build environment, providing:
By offloading build and test processes to CodeBuild, teams avoid the hassle of configuring their own build servers.
AWS CodeCommit offers a fully-managed source control service to host Git repositories in the cloud. Key features include:
Using CodeCommit enables version control without operating own servers.
AWS CodeStar provides a unified view to manage software development projects on AWS. It centralizes:
With CodeStar, teams can visualize connections between all components powering their applications. This makes it easier to manage deployments holistically.
By leveraging these Code services collectively, organizations can achieve continuous integration, delivery, and deployment of applications on AWS.
Understanding how Infrastructure as Code (IaC) can be implemented on AWS using CloudFormation and Deployment Manager.
AWS CloudFormation allows you to model, provision, and manage AWS resources using templates. With CloudFormation, you can develop repeatable and predictable environments by describing the AWS resources, and any associated dependencies or runtime parameters, required to run your applications.
Some benefits of using AWS CloudFormation for aws deployment include:
Infrastructure as code - The template is your single source of truth for the infrastructure. Changes to the infrastructure are made by editing the template.
Cost control - You can estimate costs based on the resources defined in the template. Updates or deletions can be handled by the template as well.
Productivity - With a template, you can set up your resources consistently and quickly. You don't need to figure out all the individual steps each time.
Compliance - Templates can be used to standardize deployments across your organization and make sure they comply with policy.
Overall, CloudFormation gives you a simple way to model your solutions and handle infrastructure deployments in a structured, secure, and repeatable manner.
The AWS Deployment Manager is a service that helps you organize, deploy, and manage applications on AWS at scale using CloudFormation templates. Some key capabilities include:
A major benefit of Deployment Manager is it allows you to work with your infrastructure programmatically. For example, you can create REST APIs to manage your stacks, integrate with CI/CD pipelines for automated deployments, and import existing configs using the AWS CLI.
By leveraging Deployment Manager on top of CloudFormation, you gain an extra layer of flexibility and control when provisioning infrastructure as code on AWS. Common use cases include multi-tier application architectures, microservices deployments, and managing infrastructure across multiple AWS accounts or regions.
AWS offers a wide variety of deployment options to meet diverse needs, from fully managed services to flexible infrastructure. Evaluating options based on management overhead, scalability, skills, and costs helps determine the right service mix.
Choosing AWS deployment strategies depends on:
Align strategies to capabilities and requirements for smooth AWS adoption.
