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Benefits and Best Practices for Infrastructure as Code
Infrastructure as code (IaC) has changed how the infrastructure is managed and automated by treating configurations and infrastructure setups as code. It allows for the provisioning and managing of infrastructure through automated scripts to increase efficiency, consistency and scalability.

IaC is incredibly relevant in modern DevOps practices, enabling rapid deployment, version control and collaboration, which are essential for supporting continuous integration and continuous deployment (CI/CD) workflows.

In this post, we will look at some of the biggest benefits of implementing IaC and the best practices for doing so.

The Benefits of IaC

Adopting IaC for your projects has many benefits — from enhanced operational efficiency to consistency across environments.
- Speed and Efficiency
IaC dramatically reduces deployment times and operational overhead. Instead of manually configuring hardware or virtual machines, IaC allows you to define infrastructure requirements in code.

This automation enables quick, consistent deployments across environments, eliminating manual errors and the need for repetitive setup tasks. As a result, you can focus on development and innovation, which speeds up the delivery of new features and services while reducing costs associated with manual operations.
- Consistency and Standardization
By defining infrastructure through code, IaC directly minimizes human error, replacing manual configurations with automated processes and ensuring consistent configurations across different environments. Executing the same code eliminates the discrepancies that manual setups often introduce and enhances the reliability of deployments.
- Scalability and Flexibility
IaC supports scalable infrastructure management by enabling rapid provisioning and de-provisioning of resources through automation. This adaptability allows systems to scale up or down efficiently in response to changing requirements. Infrastructure can dynamically adjust to varying load demands without manual intervention, optimizing resource utilization and responsiveness.
- Auditability and Compliance
IaC also brings significant benefits in maintaining compliance and simplifying the audit process. Through version control, IaC ensures every change to the infrastructure is tracked and documented. It creates a transparent, auditable history, providing a clear, immutable record of all infrastructure modifications. This helps adhere to compliance standards and streamlines audit activities.

Best Practices for Implementing IaC

As we’ve seen above, adopting infrastructure as code can significantly improve how you manage and provision IT resources. However, you should follow the best practices to reap its full benefits. In this part, we will outline essential strategies such as version control, code reuse, security measures and automation, which are crucial for effective IaC implementation.
- Modularize and Reuse Code
Creating modular IaC components involves breaking down infrastructure into smaller, reusable pieces that can be shared across projects. The strategies here include:
- Defining clear interfaces
- Using parameterization to customize components for different environments
- Packaging common patterns as templates or modules.
This approach promotes leveraging pre-built components to achieve reusability, reduce duplication and accelerate the deployment process.
- Keep Security in Focus
Integrating security practices into the IaC lifecycle is crucial for maintaining robust security postures. It includes conducting regular security scans to detect vulnerabilities early and applying the principle of least privilege to limit access rights for users and systems to the minimum necessary. These practices help prevent security breaches and ensure compliance with security standards, safeguarding the infrastructure throughout its lifecycle.
- Automate Testing and Deployment
Automated testing and deployment processes in the development process can be streamlined by CI and CD. CI ensures that code changes are automatically built and tested, facilitating early detection of errors. CD automates the deployment of code changes to various environments, accelerating the delivery of new features and fixes while ensuring stability and reliability.
- Version Control Everything
Using version control systems for IaC scripts is essential for tracking changes, improving collaboration and enabling rollback capabilities. It ensures that every modification is documented, providing an audit trail that enhances transparency and accountability. The VC practice supports effective team collaboration on IaC scripts and helps maintain the integrity and reliability of infrastructure deployments.
- Document and Review Code Regularly
Related to version control, documenting IaC configurations and conducting regular code reviews are very important for ensuring clarity, maintainability and quality.

Documentation provides a clear understanding of the infrastructure setup and intent, allowing easier maintenance and onboarding. Meanwhile, code reviews enhance code quality by identifying potential issues and making improvements.

Overcoming Common Challenges in IaC

In the process of adopting IaC, you may use tools like Pulumi and Terraform and may often encounter obstacles such as configuration drift, where the actual state of the infrastructure diverges from what was defined in the code. This can lead to inconsistencies and operational challenges. You should also put significant concerns into ensuring security and compliance within rapidly deployed infrastructures and managing the complexity of large-scale, multi-service environments.

The best practices mentioned above should help mitigate these challenges. For example, rigorous version control and state management policies can help track and reconcile differences, preventing drift. Integrating automated security scans and compliance checks into the CI/CD pipeline ensures that security standards are embedded from the outset. For managing complexity, adopting modular design principles and reusable components can simplify the management of large infrastructures.

Additionally, providing comprehensive training and leveraging community insights can further empower your team to utilize IaC methodologies effectively.

Wrapping Up

IaC revolutionizes IT infrastructure management by automating and codifying the deployment processes, ensuring consistency, efficiency and security. By adopting IaC best practices, such as version control and automated testing, organizations can significantly enhance their operational agility and responsiveness to changes.
July 16, 2024
How GitHub Approaches Internal Developer Experience

Developer experience (DX) has been the talk of the town, and for many reasons. Quality DX is intrinsically tied to increased productivity, velocity and developer happiness. Organizations that invest in DX tend to see efficiency improvements and less employee churn, contributing to reduced costs.

As such, much effort is being placed on ways to improve the experience developers have with their internal workflows and tools. But what are some good models of developer experience in practice? For that, let’s turn to GitHub.

GitHub, the ubiquitous code repository and version control platform, is naturally a developer-favorite tool. And internally, GitHub is constantly seeking to improve the experience its own developers have in building, deploying and maintaining software. Below, we’ll explore how GitHub approaches DX and instills these traits into its software development culture.

Allocating Time For Deep Work

The most crucial practice GitHub takes is allowing developers to enter the flow state and stay there. Dr. Eirini Kalliamvakou, staff researcher at GitHub, defines “deep work” as time blocked off intended for focus. “During that time, you experience as little interruption as possible,” she said. According to Kalliamvakou, employees at GitHub actively block off time for deep work — some teams even have “no-meeting Thursdays.”

There is research to support the benefits of allocating time for deep work. As I previously covered, one internal study of Microsoft employees found that developers who formally set aside focus time for coding experienced a significant boost in overall satisfaction and perceived productivity.

More recently, GitHub partnered with research group DX for a study, DevEx In Action, that assesses the observable outcomes of improving developer experience. Impressively, the results found that developers who dedicate significant time to deep work enjoy a 50% productivity boost.

Using AI Pair Programmers

It will come as no surprise that developers at GitHub dogfood GitHub Copilot, the AI-powered code completion tool built on OpenAI’s Codex. According to Jon Peck, senior manager, developer relations & enterprise advocacy at GitHub, Copilot is truly integrated into most of his daily tasks. “Copilot lives with me in any development environment I’m using,” he said.

Nowadays, the technology can enable complete, viable functions that are contextually and stylistically aware, he said. Furthermore, Copilot Chat now integrates into Jetbrains and VSCode, bringing it closer to the programmer’s accustomed development environment. Peck specifically highlights developer experience gains from using it for boring activities, such as building unit tests for new code or auto-summarizing the functionality of components.

Running Automation in the Background

Having more automation baked in is another way GitHub enhances its developer productivity and experience. “Developer-facing automation allows devs to stay in the flow longer, interrupting them only when something needs their attention,” said Peck.

For example, teams utilize GitHub Actions for all kinds of triggers, such as responses to new code commits or changing the state of issues, said Peck. Running asynchronous tests in the background and notifying the developer only if something breaks enables them to stay head-down in the code for more extended periods. Additionally, asynchronous communication around pull requests and code reviews can ensure teammates collaborate effectively on their own time.

These traits are often adopted in cloud-native software development cultures like GitHub. The more you can make tests and build processes run in the background, the better, explained Kalliamvakou. Plus, reducing notification fatigue is another way to ensure developers aren’t interrupted unless something really needs their attention.

Regularly Collecting Feedback

To optimize developer experience, it’s a good idea to collect developer feedback. For instance, at GitHub, their developer experience team regularly conducts internal surveys to surface areas for improvement — this helps gauge satisfaction with the amount of deep work developers are engaged in. Similarly, LinkedIn regularly collects qualitative input using an internal framework to gauge developers’ satisfaction with their tools.

GitHub also runs a newsletter to spread awareness about what new internal features they’re introducing and to regularly review the state of things. “When you introduce a developer experience initiative, you need to create awareness around it and track how people are responding over time,” said Kalliamvakou. Keeping an active tab on the success of these initiatives is a good health check to avoid burnout.

Shifting Security Left A Bit

Another area where GitHub focuses on improving developer experience is around security. Traditionally, systems perform security scanning after all code is committed. However, this can be a big pain to go back and fix, especially when you need to track down multiple dependencies and don’t have great visibility.

“The closer you get security scanning to the point where the vulnerability was created, the easier it will be to remediate it,” said Peck. Shifting left to detect potential vulnerabilities in the moment and offering “one-click to remediate” options can significantly enhance the developer experience. He also described how GitHub engineers dogfood GitHub’s advanced security features to perform things like ongoing secrets scanning, input sanitization and pattern-based analysis.

Making Processes Understandable and Intuitive

Interestingly, the DevEx in Action study found that developers who report a high degree of understanding of their code feel 42% more productive than those with low or no understanding. So then, how can we enhance our understanding of code?

Often, a developer might forget the context behind something they wrote last year, or something a teammate programmed might seem obfuscated. This is another area where AI could be utilized to generate documentation automatically. However, the future of code understandability goes beyond just static documentation.

“The trend is toward learnability right where they are, to keep them in the flow,” said Kalliamvakou. She indicates that AI-driven insights embedded into the IDE that explain what a program or function means make the developer workflow highly intuitive.

Offering Cloud-Based IDEs

Developers often run into the “works on my machine” problem, which can be a big developer experience hurdle. In essence, applications depend on far more than just the code — they are at the whim of the environment, local dependencies, its configurations, and the actual device running the program.

By using cloud-based IDE like Codespaces, said Peck, GitHub developers are able to spin up a container in the cloud and retain the same configurations. The benefit of this is that you can keep your local editing experience as well, he said.

Retaining An Engaged Developer Experience

According to Kalliamvakou, the benefits of developer experience are no longer just anecdotal — we now have real-world evidence to back up these claims. For instance, developers who find their work engaging feel 30% more productive, according to the aforementioned report.

It seems that having an AI coding assistant by your side trained on a large language model (LLM) can encourage developer satisfaction and productivity gains. Introducing AI-based tooling can help automate away some of the unfulfilling work, leaving more time for more engaging activities, said Kalliamvakou.

But although AI can solve many things, there will always be some degree of toil. Kalliamvakou recommends distributing grunt work across multiple employees and avoiding throwing it all on one person! At GitHub, the DevEx team also analyzes the efficiency of meetings to remove non-high-quality meetings, which can also benefit engagement.

February 8, 2024
CloudBolt Commits to Applying AI to FinOps

CloudBolt Software today revealed it plans to infuse artificial intelligence (AI) into the financial operations (FinOps) capabilities it provides via its IT management platform over the coming year.

Kyle Campos, chief product and technology officer for CloudBolt, said given the inherent complexity of modern IT environments, there is a need to apply machine learning algorithms and other forms of AI to enable IT organizations to apply policies in real-time to reduce costs. That capability is especially critical in cloud computing environments where services are often invoked by developers with little to no oversight, he noted.

FinOps is a discipline that promotes a shared responsibility for an organization’s cloud computing infrastructure and costs. Rather than having disparate procurement teams working in silos to identify and approve costs, business, financial and IT leaders establish policies and best practices for usage that are programmatically enforced. The FinOps Foundation, an arm of the Linux Foundation, has been defining a set of FinOps best practices, but many organizations still lack the discipline required to implement them. One of the major reasons organizations lack this discipline is that many of them have been allowing application developers to provision cloud resources themselves. It’s not until the bill comes due at the end of the month that finance teams realize there is an issue when there is a sudden spike in costs.

Rather than deploying a separate platform to manage FinOps, CloudBolt is making a case for integrating a framework for managing FinOps within the same platform IT teams use to manage those IT environments. That approach makes it possible to apply FinOps policies in a way that ensures best practices for controlling costs are implemented without creating yet another silo within an IT organization, said Campos.

Running application workloads in the cloud is fundamentally different than running them in an on-premises IT environment where costs are largely fixed. Not many developers have much of an appreciation for the additional storage and cybersecurity expenses that are generated every time a new workload is added. As a result, many organizations lack visibility into the total cost of cloud computing until the monthly bill arrives. By then, it’s too late for IT teams to have any impact on the outcome.

In the face of ongoing economic uncertainty, more organizations are clearly moving to reduce cloud computing costs more aggressively. In an ideal world, IT teams would be able to instantly see what impact any change to an IT environment will have on costs as part of any scenario planning exercise, said Campos.

While most organizations would like to reduce their cloud costs, it’s just as important to ensure that the bills being incurred are predictable. Finance teams don’t appreciate surprises, no matter how important an application may be. IT leaders are now being held more accountable than ever to make sure cloud computing costs meet expectations from one month to the next. The challenge now is finding a way to inject controls in IT environments long after applications have already been deployed.

January 30, 2024
5 Serverless Challenges of DevOps Teams and How to Overcome Them

Serverless is a cloud computing execution model where the cloud provider dynamically manages the allocation of machine resources. The term ‘serverless’ might be somewhat misleading as it doesn’t mean servers are not involved. Instead, it means that developers no longer have to think about servers, even though they’re running on them.

The idea is to abstract the infrastructure away so that developers can focus solely on the code. Functions, the units of code, are triggered by a variety of events, including HTTP requests, database events, queuing services, monitoring alerts, file uploads, scheduled events (cron jobs), and user sign-ups or in-app activities.

The Importance of Serverless for DevOps Teams

Serverless computing brings a paradigm shift in how applications are built, deployed and managed, directly impacting DevOps teams. The core advantage of serverless for these teams is the significant reduction in operational overhead. With serverless, the cloud provider takes responsibility for running the server, ensuring its reliability, and scaling the infrastructure. This frees DevOps teams from the routine tasks of server provisioning, maintenance, and patching, allowing them to focus more on optimizing the application’s code and business logic.

However, this shift also demands a change in mindset and approach. DevOps teams must adapt to a more granular, event-driven architecture that serverless promotes. They need to design systems thinking about individual functions and how they interact, instead of traditional monolithic applications or even microservices. This requires a deeper understanding of the cloud environment and its services, as serverless functions are often tightly integrated with other cloud services like databases, message queues, and API gateways.

Moreover, serverless computing introduces new challenges in areas like security, monitoring, and cost management. For instance, the ephemeral nature of serverless functions can make traditional monitoring approaches less effective, and the dynamic scaling can lead to unpredictable costs. DevOps teams need to develop new strategies and adopt specialized tools to address these challenges. Despite these issues, the benefits of serverless – in terms of agility, scalability, and potential cost savings – make it an increasingly essential tool in the DevOps arsenal, particularly for organizations looking to innovate and deploy rapidly in a cloud-centric world.

5 Serverless Challenges of DevOps Teams and How to Overcome Them

1. Complexity of Monitoring in Serverless

A main challenge in the serverless world is the complexity of monitoring. Unlike traditional architectures where you can monitor physical or virtual servers, in serverless, you need to monitor individual functions. This can be tricky as you may have hundreds or even thousands of functions running simultaneously.

To effectively monitor serverless architectures, you need to go beyond traditional methods. You need tools that can provide insights at the function level, such as AWS CloudWatch or Azure Monitor. These tools allow you to monitor metrics like execution time, error rates, and invocation counts for each of your functions.

But monitoring is not just about gathering metrics. It’s also about understanding the relationships between your functions and other components of your system. For this, you need distributed tracing tools like AWS X-Ray or OpenTelemetry, which can provide a detailed view of how requests flow through your system.

2. Managing Lambda Cold Starts

One of the most common serverless challenges that DevOps teams face is cold starts. A “cold start” refers to the delay that occurs when a function is invoked after being idle for a while. This delay can significantly impact the performance of your applications, especially those that rely on real-time processing.

The first step to managing Lambda cold starts is understanding what triggers them. Typically, a cold start happens when a new instance of your function is created, which can be due to several reasons, such as an increase in traffic, the deployment of a new version of your function or after a function has been idle for too long.

To overcome this challenge, there are several strategies you can employ. First, you can use provisioned concurrency, a feature offered by AWS Lambda that allows you to keep a specified number of function instances warm and ready to respond to invocations. Another strategy is to design your applications to be resilient to cold starts by implementing retry logic or using asynchronous invocation when possible.

3. Security Concerns in Serverless Architectures

Security is a top concern for any DevOps team. In serverless architectures, you have less control over the underlying infrastructure, making it more difficult to ensure the security of your applications.

The first step to securing your serverless applications is understanding the shared responsibility model. In a serverless environment, the cloud provider is responsible for securing the infrastructure, while you are responsible for securing your code and configurations. This means you need to pay extra attention to things like input validation, error handling and secure configuration practices.

Another key aspect of serverless security is managing permissions. Each of your functions should have the least privileges necessary to perform its task. This can be achieved by using identity and access management (IAM) services provided by your cloud provider.

4. Managing Third-Party Dependencies

Managing third-party dependencies is another challenge that DevOps teams often face in serverless architectures. These dependencies can introduce vulnerabilities into your applications, affect the performance of your functions, and even increase the cost of your serverless deployments.

The key to managing third-party dependencies is to minimize their use. Only use dependencies that are necessary for your application and regularly review them to ensure they are still needed. You should also keep your dependencies up-to-date to benefit from the latest security patches and improvements.

When using third-party dependencies, it’s also important to understand their impact on your functions. You can use tools like AWS X-Ray or Google Cloud Trace to monitor how these dependencies affect the performance of your functions.

5. Cost Management and Optimization

While serverless can be cost-effective, it’s easy to rack up high bills if you’re not careful. This is because in serverless, you pay for every invocation of your functions, even if they result in errors.

To manage costs in serverless, you need to monitor your usage closely. Tools like AWS Cost Explorer or Google Cloud Billing can provide insights into your spending and help you identify areas for optimization.

You can also optimize costs by designing efficient functions. This means writing code that executes quickly and efficiently, minimizing the use of third-party dependencies, and managing the concurrency of your functions.

In conclusion, while serverless technologies offer many benefits, they also present unique challenges for DevOps teams. By understanding these challenges and employing the right strategies, you can effectively manage and overcome them, ensuring the successful delivery of your serverless applications.

January 26, 2024
Swimm Adds Generative AI Chat Tool for Documentation

Swimm today announced it has added a chat tool that enables developers to use natural language to surface insights into code.

Company CEO Oren Toledano said the /ask Swimm tool makes it simpler to launch queries that enable developers to better understand how code was constructed.

The /ask Swimm tool aggregates documentation along with other related data to surface factors that are not evident in the code itself, such as documentation of business decisions, product design considerations and decisions concerning why specific architectural choices were made. It automatically captures and updates code-related knowledge to provide a continuous feedback loop as code, documentation, files and repositories are created and updated to provide deeper insights into code that goes beyond what might have been actually documented.

The overall goal is to provide a mechanism that enables developers to understand how and why code was constructed within the context of an integrated development environment (IDE), said Toledano.

Swimm has previously made available a platform that uses generative AI platform to create a static analysis of documentation. That capability should make it simpler for organizations to track documentation at a time when generative AI platforms such as ChatGPT are exponentially increasing the amount of code being written. In theory, those platforms should be able to also create documentation for that code, but there will still be a need for tools to track and analyze it.

In the longer term, Swimm plans to continue to extend its usage of generative AI to provide deeper levels of insights across entire applications and software ecosystems to address that challenge, noted Toledano.

In the short term, however, it’s clear that AI is making developers more productive than ever. In fact, the volume of code being generated might soon overwhelm existing DevOps pipelines and workflows. Most DevOps teams will need to revamp those workflows as the pace at which more applications than ever are being developed and deployed faster than ever.

It’s still early days as far as generative AI adoption is concerned, but it’s clear many developers are already using it to write code. How much of that code makes it into a production environment is difficult to determine, but a lot of that code is going to be of varying quality. A general-purpose AI platform such as ChatGPT was trained using code collected from all across the web. The code generated by these platforms is only as good as the examples used to train it, all of which were created by human developers who may have made mistakes, such as including code that has known vulnerabilities.

Regardless of whether code was generated by a machine or a human, the probability that documentation will need to be analyzed as part of a code review process is always high. The challenge and the opportunity is to determine the best way to apply AI to a longstanding challenge that is now being exacerbated by the existence of AI tools that, with each passing day, are only becoming more accessible to developers.

January 17, 2024
Running MongoDB on AWS: A Practical Guide
MongoDB is a source-available document-oriented database program. MongoDB is a a NoSQL database solution that uses JSON-like documents with optional schemas. Many organizations are opting to run MongoDB in the Amazon Web Services (AWS) cloud to gain improved scalability and reliability for their MongoDB deployment.

AWS offers a range of services that can be integrated with MongoDB, including computing power and multiple storage options. Running MongoDB on AWS can be done in a few different ways:
- You can manually install and manage MongoDB on an AWS instance.
- You can use MongoDB Atlas, a fully-managed database-as-a-service from the makers of MongoDB, that automatically takes care of your database operations.
In this article, we will cover the benefits of running MongoDB on AWS and show how to get started with MongoDB Atlas on AWS.

Benefits of Running MongoDB on AWS

Infrastructure Flexibility

With MongoDB on AWS, you can choose the instance that best fits your workload. For example, if you need more computing power, you can select a compute-optimized instance. If your workload requires more memory, you can opt for a memory-optimized instance. This flexibility extends to storage as well, with AWS offering both SSD and HDD storage options for your MongoDB database.

Scalability

Whether you’re dealing with a small application or a large enterprise-level operation, AWS allows you to quickly scale up or down to handle changes in requirements or spikes in popularity. AWS’s auto-scaling feature, combined with MongoDB’s sharding capabilities, provides you with a database solution that can grow with your business.

Integrated Security

Running MongoDB on AWS also offers robust security measures. AWS provides various security features, such as AWS Identity and Access Management (IAM), which allows you to set user permissions and policies. This means that you can have granular control over who can access your MongoDB databases and what actions they can perform.

Backup and Disaster Recovery

Finally, the combination of MongoDB and AWS provides robust options for backup and disaster recovery. AWS offers Amazon S3 for object storage, which is known for its durability and is well-suited to NoSQL workloads. You can also use AWS Snapshots to create backups of your MongoDB databases. In the event of a disaster, you can quickly launch more resources in AWS to ensure business continuity.

What is MongoDB Atlas?

MongoDB Atlas is a cloud-based, fully managed database service offered by MongoDB Inc. It provides an integrated suite of advanced features such as automated backups, monitoring, scaling, and security to run MongoDB in the cloud.

MongoDB Atlas handles the operational aspects of running MongoDB, eliminating the need for manual interventions. Some primary characteristics of MongoDB Atlas include:
- Managed Service: MongoDB Atlas offloads the tasks of installation, configuration, maintenance, and scaling from the user, making it a go-to option for those who want to focus more on database operations and less on administration.
- Platform-Agnostic: While MongoDB Atlas operates seamlessly on AWS, it is also available on other cloud platforms, including Google Cloud Platform (GCP) and Microsoft Azure. Users can choose their preferred cloud provider and region.
- Security: MongoDB Atlas comes with built-in security measures, including end-to-end encryption, VPC peering, and enterprise-grade authentication mechanisms.
- Automated Backups: Atlas provides continuous backups with point-in-time recovery. This means you can restore your data to any second in the past without significant downtime.
- Performance Insights: MongoDB Atlas offers real-time monitoring and alerts. Users can gain insights into queries, resource utilization, and other metrics directly from the Atlas dashboard.
Migrating MongoDB to Atlas on AWS

Here are the high-level steps involved in migrating a self-hosted MongoDB database to MongoDB Atlas on AWS Cloud. For more details, see Amazon’s prescriptive guidance.

Step One: Discovery and Assessment

The first phase involves understanding your current MongoDB setup and preparing for the migration. Begin by evaluating your existing MongoDB environment, including the database size, configuration, and performance metrics. This assessment will help you determine the right size and configuration for your MongoDB Atlas cluster on AWS. It’s also crucial to analyze your application’s dependencies on the database to understand how the migration will impact your application.

During this phase, you should also identify any potential challenges or limitations that might arise during migration. These could include compatibility issues, data integrity concerns, or performance implications. By addressing these issues upfront, you can mitigate risks and plan for a smoother migration.

Step Two: Configure Security and Compliance

Security and compliance are paramount in any migration process. Start by configuring the network settings in MongoDB Atlas to ensure secure connectivity. This typically involves setting up VPC peering or AWS Direct Connect between your AWS environment and MongoDB Atlas.

Next, focus on configuring the security features in MongoDB Atlas. These include setting up encryption at rest and in transit, identity and access management, and auditing and monitoring features. Ensure that these settings align with your organization’s security policies and compliance requirements.

It’s also vital to understand the shared responsibility model in AWS and MongoDB Atlas. While MongoDB Atlas manages the security of the cloud, you are responsible for security in the cloud, which includes securing your data and controlling access.

Step Three: Migrate Data

With the preparations complete, you can start migrating your data to MongoDB Atlas. MongoDB Atlas offers a live migration service, which allows you to migrate your data with minimal downtime. To use this service, you will need to provide details about your source MongoDB environment and initiate the migration through the MongoDB Atlas interface.

Monitor the migration process closely to ensure that the data is transferred accurately and efficiently. It’s advisable to perform validation checks during and after the migration to ensure data integrity. Also, be prepared to address any issues that arise during this phase promptly.

Step Four: Configure Operational Integration

Once your data is migrated, the next step is to integrate MongoDB Atlas into your operational processes. This involves updating your application configuration to point to the new MongoDB Atlas cluster. You will need to update your application’s database connection strings and test the application thoroughly to ensure it is interacting correctly with MongoDB Atlas.

Additionally, configure monitoring and alerting in MongoDB Atlas to keep track of your database’s performance and health. MongoDB Atlas provides various tools and metrics that can help you monitor your database effectively.

Finally, consider implementing any necessary changes to your backup and recovery procedures. MongoDB Atlas offers automated backup solutions, but you should align these with your data protection policies and test your backup and recovery process to ensure it meets your requirements.

Conclusion

In conclusion, running MongoDB on AWS offers a combination of flexibility, scalability, security and robust backup and disaster recovery options. By leveraging AWS’s diverse infrastructure, you can tailor your MongoDB environment to meet specific workload demands, be it compute, memory, or storage-centric requirements.

The migration process to MongoDB Atlas on AWS involves discovery and assessment, configuring security and compliance, data migration and operational integration. By following this process, organizations can effectively migrate and optimize their MongoDB deployments in the AWS Cloud, reaping the benefits of a fully managed, scalable and secure database environment.
January 12, 2024
Best of 2023: 11 Steps to a Successful DevOps Career
As we close out 2023, we at devopsy.kinsta.cloud wanted to highlight the most popular articles of the year. Following is the latest in our series of the Best of 2023.

The world of technology is evolving at a rapid pace, and the past few years have introduced a new shift in digital transformation and software development. The traditional business model is no longer viable, and to remain relevant in the extremely competitive business landscape, organizations need DevOps.

As more and more companies explore what it takes to implement DevOps practices, the need for highly qualified DevOps engineers has soared. In fact, DevOps engineers are among the most sought-after professions in the IT industry.

So, if you have set your mind on becoming a DevOps engineer, your chances of getting a job are high. Yet, as demand for DevOps increases, so is your competition. Enterprises will absolutely not hire just everyone. Therefore, talent and expertise are a must.

The process of becoming a DevOps engineer requires time and dedication. So, it’s crucial to start off right. We’ve put together a guide to the steps needed to get you on the right track, along with a few resources you can take advantage of.

How to Become a Successful DevOps Engineer

1. Master at Least One Programming Language

To successfully manage DevOps projects, you need to be familiar with programming languages. While you are unlikely to be writing source code, you will need to automate processes, integrate databases, and occasionally debug code. Therefore, working knowledge of programming languages will help you understand code and review it.

So, mastering one or several programming languages is a great place to start your DevOps roadmap. While you can get away with one, we do recommend that you pick at least two or three to be more competitive. Here are some programming languages that you can choose from: Java, JavaScript, Python, Ruby, C, C++, Go and Node.js.

Here are some free resources you can use:
And some paid resources:
2. Learn Different Operating Systems

Operating systems will power the technology the teams will be using to work and communicate, as well as run the servers you’ll deploy applications on. So, you’ll need to know how to configure your servers using the command line terminal.

Operating systems you can get started with are Linux, Windows, MacOs, Ubuntu and Unix.

Some free resources you can use are:
And some paid resources:
3. Know How to Operate in the Terminal

Once you are familiar with operating systems, you should know how to perform systems tasks without the help of GUI or graphical user interface. So, get started on learning how to do bash scripting, text manipulation, process monitoring, system performance, networking, as well as compiling apps from source. PowerShell, Vim, Nano and Emacs are names you should get familiar with.

Here are some free resources for you:
And some paid ones:
4. Understand Networking, Security and Protocols

Cybersecurity is becoming more important by the day, especially if you walk in DevOps shoes. So, you’ll need to understand how protocols support various request types. This will prevent the risks of bad players (and there are plenty of them) intercepting your data during transfers and intercepting your applications.

What should you learn? HTTP, HTTPS, SSL and SSH.

A free resource to get your hands on:
- OSI Model Explained
And some paid ones:
5. Learn Server Administration

As a DevOps engineer, you will have to monitor servers. They will be fulfilling requests when users request the information they need. Common web servers and related services to get familiar with are Apache, IIS, Tomcat, Linkerd, NGINX, Envoy, Istio and Caddy.

A few free resources for you:
And some paid ones:
6. Study Containers and Container Orchestration

Containers gather and organize all application code, libraries and dependencies together in a portable format so code can be written once and run anywhere. Container management is the use of a platform to organize software containers. It is essential to optimize efficiency and streamline container delivery for faster application development and deployment. They combine all libraries in a deployable unit without depleting the host operating system’s CPU and memory resources.

Here are some free resources:
As well as some paid ones:
7. Specialize in Microservices and Learn about Miniservices

To remain competitive in the DevOps space, you need to use the latest architecture patterns. While many still rely on miniservices, microservices architecture is the way of the future. They allow to improve scalability, system resilience and maintainability, offer greater technology stack flexibility, and enable faster deployments.

Keep in mind that miniservices are still relevant, and you should be familiar with both.

Here are some valuable resources to get your hands on:
8. Learn Infrastructure-as-Code (IaC)

Once you master containers and container management, infrastructure-as-code (IaC) is where you should head next. It’s one of the main areas of focus for a skilled DevOps engineer and will be vital for the success of the projects that you work with.

Some tools you’ll need to familiarize yourself with are Puppet, Terraform, Docker Swarm, Ansible, Istio, Chef and more.

Some free resources to get the basics:
As well as paid ones:
9. Understand Continuous Integration and Continuous Deployment (CI/CD) Tools

One of the core DevOps principles is continuous integration and continuous delivery/deployment (CI/CD). So, it’s a no-brainer that you should get familiar with the subject. CI/CD tools support various phases of the pipeline and help automate processes and handoffs that free up your teams’ hands.

The CI/CD tools for you to learn are GitHub, GitLab, Jenkins and Bamboo.

Free tools to get started with:
And paid ones:
10. Learn and Practice How to Monitor Software and Infrastructure

As a DevOps engineer, you will be responsible for monitoring applications and infrastructure to work on improving efficiency and resolving errors. This will require you to observe data from applications, servers, network devices and more and track metrics like hardware capacity, network throughput and uptime.

Some tools to get familiar with are Grafana, Datadog, Zabbix, Prometheus and Nagios.

A few free tools to start with are:
And a few paid resources:
11. Study Cloud Providers and Cloud Design Patterns

Modern applications are hosted in the cloud, with cloud services providing numerous benefits such as scalability and cost reduction. As a result, it’s vital to understand the various cloud services available as well as their design patterns and best practices.

Tools you should get familiar with are AWS, Azure, Google Cloud and Heroku.

Free resources to start with:
And paid ones:
Bonus: Learn DevOps Tools and Stay Up to Date with DevOps Trends

And last but not least, remember that this DevOps roadmap is just the beginning of your DevOps career. In an industry that is continuously evolving, you’ll need to continuously evolve and learn so that you know how to efficiently manage your clients’ projects, regardless of their scope. Be sure to regularly take new courses and follow DevOps development blogs for industry news and insights.

As technology is continuously advancing and evolving, the need for highly skilled and experienced DevOps engineers is on the rise. If you would like to become a DevOps engineer, follow this DevOps roadmap for 2023 and arm yourself with lots of patience (and coffee, lots of coffee).
December 21, 2023
How Generative AI Can Streamline Code Documentation

Let’s be honest—it’s not always that software documentation matches production environments, let alone that documentation exists at all. Too often, developers are pushed to deliver code faster and faster, and documentation often falls under the wayside as a second priority. As such, obscure functions and code libraries often become undocumented and difficult for new developers to comprehend.

Generative AI is having an impact on many areas within the software development life cycle. And documentation is another surefire area that artificial intelligence is set to revolutionize. By running large language models (LLMs) over preexisting codebases, AI could document legacy technologies and potentially even generate documentation in lockstep with new projects.

Many obstacles inhibit documentation practices across modern engineering environments. Below, we’ll check in with the experts to see how AI could advance the status quo. We’ll consider some techniques for using generative AI for documentation, outline the potential benefits and discuss some of the caveats to watch out for.

The Blockers to Current Documentation Practices

Various obstacles within the software development pipeline can hinder a documentation-driven culture. A significant roadblock, until recently, has been upkeeping documentation manually.

“It is almost impossible for organizations to keep the current state of their IT application portfolio documented manually,” said Miten Marfatia, founder and CEO, EvolveWare. Newer agile documentation generation tools can fix this by creating and updating metadata for IT applications automatically, yet they still require some manual intervention, he explained.

In addition to manual documentation standards, other inhibitors can present themselves, such as balancing support for pre-existing documentation for old projects with documentation for new projects. “There is an eternal tradeoff between creating documentation for new features and revising or optimizing documentation for existing features,” explained Sanjay Sarathy, vice president, marketing, Cloudinary. “As companies add more products or existing products become richer, the tradeoff calculus becomes more complex and harder to get right.”

According to Igor Jablokov, CEO and founder of Pryon, organizations suffer from a lack of knowledge visibility, which can hinder development and even become a compounded risk within a highly regulated industry. “Friction abounds—it takes so much effort to document things, and tribal knowledge of all types is seldom properly recorded,” he said. “So, at times, organizations have to restart projects because they don’t know how an existing component works.”

Streamlining Documentation With Generative AI

As one can see, there are many drawbacks to the current state of documentation habits. However, generative AI is set to greatly advance the current manual and error-prone documentation standards. It could help address documentation policies for projects that have been sitting in the queue for too long while also encouraging new developer experiences to emerge.

“With generative AI modeling techniques, the documentation process would become progressively seamless and automated whether an application is being documented for the first time or its documentation is being updated,” said Marfatia. “Generative AI will best apply to documentation and transformation of legacy code.”

“Generative AI can certainly be used to streamline documentation processes,” said Sarathy. “It’s particularly promising and valuable in that it can help accelerate the process of keeping legacy content fresh and accurate.”

While it might not replace manual reviews entirely, generative AI could be used to flag old content, update code samples and help address gaps in the full scope of documentation support, said Sarathy. He likens this to the 80/20 rule: AI can help teams optimize high-trafficked pages while keeping up with the other 80% lower-priority applications.

Last but not least, generative AI presents a new opportunity to create novel experiences with old documentation, bringing some new life back to how projects are shared and understood. “By transforming previous static documents into interactive experiences, an organization can discover contradictions, gaps, and overlapping content,” said Jablokov. “They can unify multimodal assets from audio, images, text and videos that may contain critical regulatory information or lessons learned.”

Techniques for Using AI For Documentation

First and foremost, generative AI is set to benefit the documentation and transformation of legacy code the most since large volumes of data and code are already available for the model to ingest to infer understanding of similar code. So, it might behoove adopters to first trial AI-generated documentation in this scenario.

But what would the actual process look like? Well, speaking generically, it would first involve training an LLM on a large data corpus, which will likely include an enterprise’s software development artifacts like code, documentation, functional specifications, technical specifications, test cases, user stories and a variety of other supporting materials, explained Keith Cox, managing director of application modernization & migration at Deloitte. “Generative AI can be used to utilize foundational knowledge for generating future state system artifacts,” he said. “We can use these foundational legacy elements to create high-value LLMs for the enterprise.”

With this in hand, developers could prototype a desired functionality. But taking this a step further, if generative AI was used to generate a data model, it could also help spur design-first approaches for greenfield development. “Ultimately, generative AI could revolutionize the entire development life cycle,” said Cox, “First by accelerating the generation of design specifications and then potentially for some application scenarios, replacing the end-to-end cycle of application development.”

Yet, for generative documentation to be effective, it will rely on a valuable data corpus and bespoke capabilities that extend beyond off-the-shelf models. “The highest value generative AI models will be those that are specialized with large amounts of samples, targeted to specific industries, market segments or applications of technology,” said Cox.

Benefits of Using AI For Documentation

Generative AI is set to improve documentation and, in effect, speed up future development. LLMs that generate the documentation for legacy applications could be useful for generating functionality requirements for modern applications, too, said Marfatia. Ultimately, generative AI could revolutionize the entire development lifecycle.

In addition to development efficiency gains, the end benefits to the business could easily be improved user experiences that result in higher revenue and profitability. “The content produced by the brand can be updated more regularly to ensure greater accuracy and thoroughness,” said Sarathy. “Better, more accurate content leads to fewer support tickets and faster resolutions.”

Another interesting outcome of adopting AI for documentation generation is helping teams contend with multiple cloud providers and an increasingly diverse array of technology stacks.

“Instead of bouncing between myriad provider sites to support their internal development teams, they could unify all support documentation within a singular experience,” said Jablokov. “This allows them to reduce the risk of errant signals when they interact with the public web, as well as see underperforming vendors in a coordinated way. As a bonus, they could blend in their own internal documentation, so they have a cohesive view of what technologies are used for what workflows.”

That said, AI-driven documentation generation (and all generative AI endeavors, for that matter) should be undertaken cautiously, especially when it comes to the security concerns of the organization and not ingesting intellectual property without permission, said Marfatia.

Final Thoughts

It’s good to document your code—not only for learnability but for security reasons, too. By encouraging a more regular, automated regiment for documentation, technical leaders could enhance service discoverability, reduce zombie endpoints, increase the reusability of internal services, enable partner-sharing capabilities and reap other benefits.

Generative AI presents an interesting opportunity to localize and document preexisting shadow IT, making it more visible to engineers. And it could guide the documentation of greenfield development too. Yet, of course, these models rely on a large data corpus and take effort to implement. As such, generative AI shouldn’t be trumpeted as the end-all cure for every scenario or software development woe.

December 5, 2023
Dynatrace Survey Surfaces DevOps Automation Gains and Challenges

A global survey of 450 DevOps and security practitioners published this week by Dynatrace found investments in automation have improved software quality (61%), reduced deployment failures (57%) and decreased IT costs (55%).

However, only 38% of respondents said their organization had a clearly defined DevOps automation strategy. On average, organizations have succeeded in automating just over half (56%) of their end-to-end DevOps life cycle, the survey found.

Saif Gunja, director of product marketing for Dynatrace, said the survey makes it clear that automation is still being applied to isolated workflows rather than centrally managed. That is changing, however, as more organizations embrace platform engineering to manage DevOps workflows at scale, he added.

Overall, organizations are investing in DevOps automation over the next 12 months to improve security and compliance management (55%), infrastructure provisioning and management (52%) and performance optimization (51%).

The biggest barriers preventing organizations from automating new DevOps use cases are security concerns (54%), difficulty operationalizing data (54%) and toolchain complexity (53%). The average organization relies on more than seven different tools for DevOps automation, the survey finds.

As a result, the automation that has been put in place is often too brittle to effectively scale beyond a small DevOps team, noted Gunja.

A full 71% of respondents also used observability data and insights to drive automation decisions and improvements in DevOps workflows. However, 85% also admitted they faced challenges using observability and security data to drive DevOps automation. The top three challenges facing organizations included inaccessible data (51%), siloed data (43%) and the need for data to flow through many systems (41%).

Well over half (54%) said their organization is investing in platforms to enable easier integration of tools and collaboration between teams involved in automation projects.

Finally, 59% also anticipated large language models (LLMs) such as ChatGPT and Bard would have a significant impact on DevOps, with the top three benefits expected including increased productivity and reduced manual effort (57%), improved development, security and operations collaboration (56%) and enabling teams to generate code automatically (48%).

Many organizations that have embraced DevOps are now looking to strike a balance between the concept of developers owning every application or service they developed and the need to reduce the amount of toil developers now encounter, noted Gunja. Developers typically only spend about a quarter of their time writing code so there is a clear need to reduce the current level of cognitive load developers encounter as they maintain application development environments, he added.

The challenge is finding a way to reduce that load in a way that doesn’t overly restrict the ability of developers to embrace new tools and platforms that will enable them to build applications in a more innovative fashion. One of the primary reasons many development teams embraced DevOps in the first place was to not have the way they build applications dictated to them by a centralized IT team.

One way or another, DevOps workflows will continue to be increasingly automated. Less clear at the moment, however, is exactly who within an organization will be driving those initiatives.

September 28, 2023
The Differences Between DevOps, DevSecOps and SRE

DevOps, DevSecOps and SRE are all quickly gaining traction within the tech world due to their effectiveness and promise of reliability for companies. Each framework provides various levels of safety and scalability—DevOps is agile, DevSecOps prioritizes security above all else and SRE focuses on performance optimization. Together, these are changing how software gets deployed from conception to production. We’ll break down each one in detail.

What is DevOps?

DevOps is a set of practices that combines software development and IT operations, which aims to shorten the software development life cycle (SDLC).

DevOps bridges the gap between those two areas by bringing together different people–developers, operators, testers and system engineers–who are responsible for different parts of the software delivery process, from planning to deployment into production environments.

A core tenet of DevOps is to automate manual processes within each stage of the SDLC, eliminating much of the redundant work that used to take weeks or even months. This automation significantly drives down costs, as well as reduces problems posed by human error when running manual tasks.

The following are some of the core tenets of DevOps:

1. Collaboration
2. Quality and reliability
3. Automation
4. Scalability
5. Security

What is DevSecOps?

DevSecOps (or DevOps security) is an emerging field within the DevOps practice that focuses on strengthening software security within the development and operations process.

These three distinct groups often have different priorities when it comes to building software applications. Developers are focused on speed-to-market objectives from their employers or clients, operations focus on reliability and scalability, while security teams prioritize safety from malicious actors or data breaches. At the same time, with DevSecOps, these goals become united under one umbrella: Build fast and reliable applications with built-in protection against attacks.

DevSecOps focuses on implementing security as code—or automating many routine tasks involved with identity management, like user authentication and authorization. To ensure that security is properly integrated into your DevOps pipelines, below are some key DevSecOps practices:

1. Automate security processes whenever possible
2. Encourage continuous testing
3. Prioritize remediation over detection
4. Create a culture around secure coding principles

What is SRE?

SRE stands for site reliability engineering. The basic idea behind SRE practices is that software engineers should be responsible for both the development as well as the reliability of their product as a whole. This means having an understanding of not only how users will interact with the application or service but also how it interacts with other services in its operating environment, such as databases, firewalls, cloud environments, etc.

To maintain uptime while meeting these requirements, SRE applies various techniques like automation and continuous deployment processes to ensure high availability.

A key goal of site reliability engineering is service-level objectives (SLOs), which measure how consistently a service meets its goals over a given period of time—such as how often it’s available, how quickly it responds to requests made by users or customers—and establishing goals around those SLOs. Achieving objectives like these helps organizations build customer trust in their products and services since it ensures that they have stable and reliable day-to-day operations.

Here are some of the most important SRE principles:
1. Automation
2. Proactive monitoring
3. Fault tolerance
4. Measurement and analysis
5. Service-level objectives (SLOs)

DevOps Vs. DevSecOps Vs. SRE

The differences between DevOps, DevSecOps and SRE can be summed up in three main areas: Scope, roles and objectives.

DevOps focuses on improving the speed of releasing software by automating processes and optimizing collaboration between development and operations teams. This helps organizations create a continuous delivery pipeline to quickly deploy code into production with minimal risk. As such, it is typically focused on development or operations activities and has become synonymous with agile methodologies such as continuous integration/continuous delivery (CI/CD).

DevSecOps expands upon these concepts to include security considerations from the very beginning of a project’s life cycle instead of tacking them on after the fact. By shifting security left in the process–meaning bringing security earlier into the design phase–teams are able to reduce risk early on while achieving faster results overall through continuous feedback loops.

As part of this shift left process, DevSecOps practitioners focus heavily on automating security compliance activities as part of their job function rather than waiting until after deployment has occurred.

Meanwhile, SRE focuses more broadly on keeping the user experience consistent across various products an organization offers by responding to customer needs quickly while creating automation processes that allow common tasks to be repeated without manual intervention from engineers or developers.

Then there is also platform engineering, which is a closely related yet distinct discipline with its own unique responsibilities. While both DevOps and platform engineering involve automation and working with the infrastructure that supports software development, there are some key differences between them.

Platform engineering can be defined as a practice that involves developing a standardized platform on which all applications will run. Platform engineers build the underlying infrastructure to support applications, including servers, databases, virtualization layers and networking components using standardized toolsets. An important task for this field is designing an application’s architecture in such a way that allows it to scale easily with increased demand or usage levels. Additionally, they create self-service tools that enable developers on their team to quickly spin up new environments without requiring outside assistance.

Wrapping Up

In conclusion, while all three approaches have similar goals–ensuring reliability, speeding up the software delivery process and securing user data–they do so using different strategies:

● DevOps favors automation.
● DevSecOps puts focus on proactive security measures.
● SRE prioritizes optimization, reliability and scalability.

Each one can contribute significantly when appropriately implemented into any organization’s workflow, depending upon its own specific needs.

August 24, 2023