For decades, DevOps has been a buzzword in the software development industry. It’s an integral part of any successful organization’s infrastructure. But how do you get started with data-driven DevOps? What does it mean for your company? And how can you achieve these benefits for your business? In this post, we’ll explore what data-driven DevOps is, why it matters to your business and how to implement it successfully by following these six best practices.

1. Containerization

Data-driven DevOps is a way of building and enabling the continuous delivery pipeline.

Containerization is a way to package and deploy applications using a container registry, like the container registry by JFrog. It provides standardization, makes it easier to scale applications, and helps with automation.

Containers can be used with DevOps processes because they provide consistency across your builds, deployments, and testing environments. This is valuable for data-driven DevOps as it allows developers to create consistent APIs and services that other teams can use.

2. Actionable Analytics

If there’s one thing that data analytics cores can provide, it’s actionable analytics. When you’re working with data in DevOps, you need to be able to get information on demand. This means you need a system that allows you to query your data anytime and get answers quickly.

Actionable analytics provide a clear picture of where you are and how you can improve. There are several things to keep in mind when designing your data-driven DevOps strategy:

  • Analytics should be available across all teams. They help everyone learn from each other, so they should be accessible to everyone who needs them.
  • Use analytics to guide decisions and improve performance, not just as an afterthought or way to make yourself look good in the annual review.
  • Analytics should be accessible to everyone in your organization. The more people who can use the data, the better. If you only make it available to specific teams, they’ll work in silos.
  • Analytics should be actionable, not just descriptive. They need to provide insights that can help you improve your processes and increase efficiency.

3. Integration

The next step in a data-driven DevOps approach is integration, which is not just about data. Integration is a crucial requirement for DevOps because it allows you to automate transforming data into actionable insights and improve your products.

The best tools for integrating your applications are intuitive, easy to use, flexible enough to handle any project, and secure and scalable so they can expand with your needs—and your growing team.

4. Consistent Workflow

Consistency is critical to a successful data-driven DevOps process because it helps you deliver value and consistently make better decisions. You can achieve consistency through the following best practices:

  • Define and enforce your standard operating procedures (SOPs) for data-driven DevOps. In particular, establish standards for handling errors during software development and deployment—for example, always documenting error codes so that they’re easy to search for when troubleshooting problems.
  • Set up a system of checks and balances by implementing automated tests and manual code reviews before it’s deployed into production systems. For example, you can use unit tests or integration tests before code goes into staging environments; then have end-to-end functional testing performed on staging systems before they’re put into production mode; finally, add user acceptance testing after those two stages are complete so that users can verify that what was developed meets their needs in terms of functionality and usability.
  • Acknowledge that “good enough” isn’t good enough when it comes time for release management—you need processes that ensure every piece is ready for deployment at once rather than waiting until everyone has finished working on their tasks independently (which could take hours).

5. Simplification

Simplification is a key principle of DevOps, and it’s something that you can implement on your own. Simplifying your tools, processes, and infrastructure will help you achieve faster results with fewer resources.

You may have heard the saying “less is more,” but that doesn’t mean you should stop there when simplifying your data infrastructure and processes. Here are some things to consider:

  • Use a single tool or platform for each task. If there aren’t any good alternatives available in industry-standard products, consider creating your solution using open-source software like Ansible or Docker Swarm instead of multiple proprietary ones that don’t integrate well.
  • Focus on improving only one thing at a time instead of trying everything at once; this will allow you to make progress without getting overwhelmed by all the work required upfront before seeing results later down the line (and vice versa).

6. Automation

Automation is the key to a successful DevOps practice. While it may seem like an afterthought or low priority in a project’s early stages, automation can scale your organization and reduce costs. Automation also increases productivity, efficiency, and quality assurance within an organization.


DevOps is a fast-moving industry with new technologies and practices popping up every day. While some people may be tempted to dive into the next big thing without thinking about whether it’s a good fit for their organization, we believe that adopting a data-driven DevOps approach is the best way to ensure success.

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