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Tag: AWS Application Patterns

  • How Organizations Benefit from AWS App Patterns

    How Organizations Benefit from AWS App Patterns

    As a startup, an organization thrives by breaking rules, challenging the status quo and standing for all that is against the norm. The same rules don’t apply once the same organization grows, matures, becomes popular, gains a long list of clients and expands to a global footprint. With scale and growth comes the need for reliability, consistency, being able to deliver the same level of service to all customers and keeping promises made to customers. At this stage, application patterns become indispensable to an organization’s day-to-day functioning.

    Support of the Application Now and Long-Term

    Mid- to large-size organizations typically deal with numerous internal-only applications and external vendor applications. These applications are changed and updated regularly. To support the applications’ needs, and effectively manage them, organizations need a change management system in place.

    A service such as AWS Config helps to audit AWS services that power applications so there’s increased visibility. It tracks changes made by users and applications, and presents a clear picture of how these changes impact AWS resources.

    Enabling Shift-Left Without Additional Overhead

    DevOps bring together Dev, QA and Ops into smaller, cross-functional teams. The goal is to break down silos between teams, facilitate collaboration and drive better results faster. Accordingly, shift-left is a tactic used to encourage QA and Ops teams to play a more pivotal role in the initial Dev stages of the pipeline.

    The benefits are many. QA brings their experience of testing the application to its limits and informs about what’s feasible to implement. Ops chimes in with concerns and things to watch for once deployed. While shift-left is great from a team standpoint, it can meet with hurdles and can fail if communication patterns are random, and ownership of each step is not clearly defined.

    Application patterns encourage systematizing this collaboration between teams. Patterns help to define the type of infrastructure to be used, their configuration and the kind of tools that fit in with the team’s requirements. Application patterns guide decisions and help to make the right choices, and sometimes compromises, at every step.

    Supporting the Advancements in Open-Source Without Fear of Tool Attrition

    There is an explosion of open source tools today. If DevOps teams are attracted to the next shiny new open source tool, they’ll soon find themselves overwhelmed with tools they don’t use and that slow them down.

    Open source tools evolve quickly, and their popularity rises and falls with the times. One look at the open source monitoring space will show you that older tools such as Nagios and Zabbix are now becoming less relevant, and modern container-aware monitoring tools such as Prometheus are taking their place.

    AWS supports the use of the top open source tools; for example, Elasticsearch. The AWS Elasticsearch service integrates seamlessly with Kibana for visualization and delivers the ELK stack without the maintenance.

    Application patterns enable you to define a strategy for tool adoption. You may find the best open source tool in the world, but what’s the point if you can’t use it in your toolchain, or if it slows down the pace of operations? The popular DevOps mantra of people, processes and then tools holds true here.

    Value Stream Management

    Value stream mapping is about visualizing the flow of value from an organization to its customers. In simple words, it’s about taking stock of what you have to offer to customers and monitoring how your offerings change over time.

    Value flows outward to customers, as well as internally between employees and systems. For example, in the context of DevOps, it helps to map the value of Ops to Dev and QA. As these teams are each other’s internal customers, knowing what the value stream is can help remove bottlenecks and drive greater efficiency.

    Building a Team and Culture First

    AWS is a great place to build team culture as it is expansive and includes services for pretty much anything you’ll ever need. You can find tools such as CodePipeline to quicken development pipelines, to make sure everyone on the team is informed of important changes using CloudWatch and SNS queues, and to automate common tasks with Lambda.

    Rather than jumping feet first into AWS and then figuring out how it all works, it pays to first define application patterns and team culture that will govern the way you use AWS. Patterns and culture are closely related. If culture is about the people, application patterns are about the infrastructure and technology stack being used by people.

    Conclusion

    Whether you’re yet to move to AWS or are already on board but not getting the most out of it, application patterns help evolve the way you manage applications in AWS. Application patterns help scale applications over the long term, they enable shift-left for DevOps teams without the friction of overhead. Application patterns make it possible to adopt the right open source tools and find success with them. By using application patterns you can visualize the entire process and drive great efficiency, and finally, they support great team culture within DevOps teams.

    DevOps teams that run their operations predominantly on AWS need not fly blind. Application patterns bring greater visibility, control, collaboration and ultimately value.

    This sponsored article was written on behalf of Eplexity.

    — Twain Taylor

  • What Are AWS Application Patterns?

    What Are AWS Application Patterns?

    The cloud-native approach is not just about compute power and cloud services. A “set it and forget it” attitude won’t work with cloud native; there are far too many moving pieces and there is just way too much complexity. Instead, this approach requires constant testing, optimization and improvement. This is epitomized in the two key phrases of the DevOps methodology: continuous integration and continuous delivery.

    To make this a reality, however, it takes a strategic approach to defining application patterns that will govern the way in which cloud-native applications are built and shipped, rather than a one-off effort. Since AWS is the leading cloud provider, we should discuss which application patterns are relevant to the AWS platform. There are many aspects to consider when it comes to AWS application patterns, and we will cover them here.

    Operational Excellence

    The best starting point for application patterns is a commitment to operational excellence. Typically, the term “Ops” is used to identify the IT team, but in this case, operational excellence rests on the shoulders of Dev, QA and Ops teams. In cross-functional DevOps teams, every member should strive for operational excellence at every step–even in the smallest of tasks.

    In their white paper on operational excellence, AWS encourages its users to manage operations as code. This helps avoid human error, enables automation of complex processes and allows for quick and easy rollback when errors occur. Failure, according to AWS, is an opportunity for learning. They even advise performing “pre-mortems” that try to anticipate potential points of failure before a deployment.

    AWS Trusted Advisor is a useful tool that enables this type of accountability and shared responsibility within a team. It checks and reviews your new and existing AWS workloads looking for opportunities to save costs, improve performance, tighten security or follow best practices for scaling operations.

    Security

    Security is a pattern that is non-negotiable. The term “DevOps” has inspired another variation called DevSecOps that signifies this fact. Security operations take center stage when talking about DevOps.

    The interesting thing about DevSecOps is it actually results in more freedom, flexibility and trust across teams when done right. This is unlike traditional approaches to security which created silos, stifled creativity and slowed down innovation. Security is now a key piece of the puzzle, not an afterthought. AWS has many machine learning-based security solutions such as Macie which automatically identifies and secures personally identifiable information (PII).

    Along with this, configuring role-based access controls using AWS IAM is essential to have greater control over who or which applications access data and for how long. Access Control Lists govern access to data stored in AWS S3 buckets.

    The news is full of instances of data breaches due to weak security policies and access controls. Capital One is a recent example of a data breach that affected 100 million of its users. While security in the cloud is more complex than before, AWS has the necessary tools and systems in place. It’s up to organizations to implement a security strategy that prevents misuse of their data.

    Reliability

    Today, there is a low tolerance for downtime because of the potential loss of revenue. With the stakes so high, reliability is a pattern any application custodian would have high on their list of priorities. AWS has multiple reporting and health monitoring tools to keep you constantly on the pulse of your applications.

    Architectural patterns like microservices enable better resilience by decomposing monolithic applications into more manageable chunks called services. This “divide and conquer” approach leads to higher availability, which is critical for SaaS applications.

    Patterns like chaos engineering seek to intentionally kill random instances and services and force the DevOps team to figure out workarounds and backups for every scenario.

    Application Performance

    With the explosion of data, applications are always dealing with lots of data to transfer, store and process. An application’s performance depends on three factors: the backend data storage layer, the middleware components and the frontend.

    The backend data layer is the most critical component when it comes to performance. Choosing the right storage formats, database types, storage devices and organization patterns all contribute to the speed of data access. AWS has an amazing array of storage services, including S3, RedShift, Aurora and Glacier. AWS gives its users every type of storage solution, all of which should be considered to improve data management.

    AWS is devising new ways to query large datasets without bearing the load on your existing systems. RedShift Spectrum is one example of this. It is a great way to “minimize the need to scale Redshift with a new node, which can be expensive.” If you’d still rather not have to manage any RedShift clusters on your own AWS Athena is a great serverless alternative

    That gives you all the power you need to query large datasets in S3 without having to maintain any clusters of your own.

    This pay-per-use model is making its ways across the entire AWS stack with services such as ECS and Fargate offering completely serverless experiences when operating containers at scale. What started out with AWS Lambda is catching on fast, and many AWS services like the ones mentioned above enable serverless experiences with a focus on application performance.

    Cost Optimization

    When moving to the cloud, pricing is completely different. Rather than paying for perpetual licenses, you now pay a small fee for every small resource that you use. Though small on its own, you’ll soon realize that cloud costs can shoot up easily if you’re not paying close attention to your spending.

    Cost optimization is necessary as you scale operations in the cloud. Cost optimization includes looking for more efficient services, whether that’s within the same vendor platform or a different platform. At a minimum it may require a lift-and-shift approach to transition from expensive old infrastructure to inexpensive new alternatives. For example, shifting workloads from VMs to containers to serverless within the same platform can show great cost savings, since containers and serverless are generally more cost-effective than VMs.

    It pays to leverage the expertise of experts in this area. They should ideally be people who have spent years using AWS, understand Well-Architected best practices and have found ways to cut costs and derive more value from the same budget.

    Application Patterns Enable Extensibility

    Microservice applications adopt a pluggable architecture. This enables them to be easily integrated with external applications. Typically, API-based communication patterns are most common. AWS API Gateway is a service that manages API design for AWS applications. In some cases, an SDK is the better choice–for example, when you need more control over the developer experience and integrations. It’s important to know when to choose either option.

    Application Patterns Enable Portability

    Portability is the ability to move workloads across instances without interrupting their functioning. As instances change, the workloads which they support shouldn’t be affected. This is only possible if there is a clear separation layer between the workloads and the infrastructure that runs them.

    Application Patterns Enable Shift-Left

    The DevOps methodology is known to encourage a shift-left approach to software delivery. This means that not only Dev, but also QA and Ops contribute to the design and development stage of an application. This fosters deeper collaboration across teams and breaks down silos.

    A shift-right approach holds true as well. Shift-right means developers are responsible for the code they write. They are equal owners with operations teams for the resilience of the apps they build and also the bugs they introduce into the system. This practice of putting ownership and responsibility where it belongs is very healthy for a DevOps team.

    AWS facilitates pipeline management using tools such as CodePipeline, CodeCommit and CodeDeploy. They each perform similar but complementary roles and help build a CI/CD pipeline. They make it easy to get started with CI/CD within the AWS platform.

    For organizations further down the line in their cloud journey, AWS’ pipeline management tools may seem too simplistic for something like GitOps. Instead, they’d want to look outside AWS for tools such as Jenkins X or Weave Flux which are designed with GitOps automation in mind. Whether it’s the simple or advanced route, a pipeline brings great benefits over a manual development process.

    Conclusion

    Though tools enable the implementation of helpful application patterns, on their own, they are ineffective. Application patterns need a deliberate strategy and an approach for a start. Only after a strategy is in place, can you move to deciding which tools to use. With a strong strategy in place, any of the tools mentioned here can be swapped out for other better tools and the results will be the same. It’s important to not be distracted by the next shiny tool but keep a keen focus on the strategy for implementing AWS application patterns.

    As we’ve seen here, application patterns are diverse, but they all build upon each other. Without even one of these pillars, organizations would not be able to realize the goal of cloud-native applications. On the other hand, if they give careful thought to each of these application patterns, organizations would deliver cutting-edge applications that can only get better with the passing of time.

    This sponsored article was written on behalf of Eplexity.

    — Twain Taylor