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The Power of Harnessing DevOps for the Database

DevOpsForDatabaseEbook
Why do some organizations excel in streamlining their database operations and applications development while others find it immensely challenging? Why can some database teams embrace agility while others take months of cycles to deploy even a single line of code? What secret sauce can allow some database teams to work smarter (not harder), streamline database development lifecycles better, get to deployment faster, and create an overall stronger alignment across departments? In the free eBook DevOps for the Database, author Baron Schwartz digs into the root causes. He offers practical guidance for more widespread and effective use of DevOps practices to optimize database performance. It is written for DBAs, developers, SREs, tech operations staff, managers – anyone who is responsible for data management for data-intensive applications and wants to modernize for speed, stability, and efficiency by improving the performance of their organization’s databases. Below are a few key takeaways from the eBook, which help highlight the benefits and best practices you need to know when implementing DevOps from a database perspective.

What is Database DevOps?

To define database DevOps, it’s important to understand why the DevOps community and its practitioners have differing opinions on how to define the DevOps culture and mindset. Software industry leaders have been trying to define what DevOps is forever. And while there are many varied definitions of DevOps and different ideas about the right way to do it – all are context specific. In simplest terms, DevOps is rooted in ensuring software developers and IT operations teams work in a unified manner with a shared purpose. It means breaking free of old-school, manual systems administration and taking ownership of the provisioning infrastructure, deployment of their applications, and monitoring how they perform. What’s often described as the “first age of DevOps” began as engineers started automating their work with DevOps tools such as Chef, Puppet, and Ansible. Now, many organizations are leveraging DevOps practices to have a positive and meaningful impact on operations and the application development pipeline. While most top firms say they’re committed to a holistic DevOps philosophy and are trying to put it to work across their organization by integrating roles and functions throughout the development process, many of them are overlooking the one area where problems are most common and consequential: the database.

Benefits of Database DevOps

Leveraging DevOps methodologies to automate tasks like database provisioning, backups, workflows, and continuous integration/continuous delivery are some of the use cases designed to help provide immediate and long-term benefits to database teams. Also, fostering a database culture vested in DevOps can allow all involved teams to take more ownership of the databases, which can help reduce database code bottlenecks and encourage DBAs to innovate faster, break things less, and outperform their peers.

How to Use DevOps for the Database

In the eBook, Baron provides first-hand observations and insights into the benefits of using DevOps for the database as sourced from the database community. He outlines six areas organizations should focus on when beginning to implement DevOps best practices from a database perspective:
  1. Apply baseline DevOps infrastructure operations practices to the database: These baselines include automated provisioning, automated configuration, and monitoring.
  2. Automate database-specific operational tasks: These automation tasks should include backups, recovery, upgrades, and refreshing the test dataset on pre-production databases.
  3. Implement version control: Manage database schema, code, and data models – table structures, stored procedure code, etc. – similar to source code with version control and “normal” change control processes in CI/CD pipelines.
  4. Automate deployments of database changes: Use database automation and migration scripts instead of manual, time-consuming database deployments for schema changes (migrations) and data transformations (data migrations).
  5. Involve developers: Performance and availability are not just DBA jobs. Make sure developers are involved and ideally responsible and on-call for their application’s production database performance.
  6. Stop repetitive tasks: Database administrators should not be doing repetitive tasks. Instead, they should serve as subject matter experts who consult with or are embedded within engineering teams to maximize productivity.
While ordered by ease of implementation, Baron emphasizes these six areas do not represent a maturity model. DevOps is a journey, not a destination, and progress is not linear, so you’re never “done.” Database DevOps is about doing what is feasible and valuable for your situation.

The Intersection of Database DevOps with Observability

It’s worth keeping in mind strategies for harnessing DevOps processes for database management, application changes, application development, and maintenance aren’t occurring in a vacuum. The adoption of observability as an operational imperative can and should support and influence what benefits DevOps can bring to not only database administration teams but entire organizations. With deeper levels of visibility, observability can help provide many of the insights DBAs need to do their jobs, such as monitoring the effect of schema changes to data flow/lineage, making updates to data tables and data distribution, checking application code into source control, and other optimizations alongside what other teams may be focused on in the same environment. Implementing DevOps for the database methodology + observability can be a game-changer in helping database, and other teams detect and proactively avoid costly downtime and other harmful business impacts by providing a shared view they can all speak to and use to inform their work. Learn more about how the observability solutions on the SolarWinds® Platform are built to help teams become more collaborative and efficient by centralizing visualizations and metrics across on-premises and cloud instances.
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