DevOps has transformed the way developers, Database Administrators (DBAs), and IT professionals work. Thanks to the various automated tools and solutions being implemented today, processes like debugging and testing have become fast and efficient. This is now helping all stakeholders focus more on quality.
Besides improving productivity and collaboration, DevOps implementation can reduce IT expenditure by over 30% and also lower redundancy by 35%.
However, this is not necessarily the case with Database DevOps, the biggest elephant in the room in the DevOps world. This is largely due to the different nature of the database, where every schema change can potentially lead to data loss, code clashes, configuration drifts, and other errors.
There are a wide range of issues hampering smooth database operations today. Reliant on manual processes, DBAs are failing to keep up with the pace of application development. They are constantly facing a barrage of review requests from the developers, who are getting increasingly frustrated as entire sprints pass by while waiting on the DBA teams. While Devs are accelerating in a race car, DBA teams are schlepping along on foot.
Naturally, there are also a wide range of errors and technical issues that are plaguing database delivery pipelines when those are managed manually.
Today’s dynamic DevOps flows put a lot of stress on the DBAs and the databases in the same pipeline. When schema changes are not made as systematic repeatable processes, inconsistencies arise. These lead to failures even in tested release packages once they are in the “drifted environment”.
For example, a tested package can enter production, only to find itself in direct conflict with a hotfix. This can break the fix or the entire release.
Also, there are two scenarios that occur quite frequently:
- There are many urgent package updates that can be business critical. These are often built on a smaller package framework, which often get updated or patched automatically. This creates a drift.
- Scaling up requires new peak time configurations (e.g, – eCommerce websites during the holiday season). However, if these changes have not been documented, they can cause drifts after reverting back.
Manual processes introduce more bugs into the ecosystem, which often snowball into serious issues that become hard to detect, let alone fix. With everything now required to happen faster for better performance and time to market (TTM), these errors and issues can be very damaging to the organization.
To make matters worse, manual documentation and the lack of a proper version control solution also make roll-backs extremely painful. With QA testers and developers working overtime to detect and resolve the issues, productivity, collaboration, quality, and innovation become afterthoughts.
Slow and buggy database releases also create a toxic “blame culture”, where DBAs, devs, IT workers, and QA teams are constantly blaming each other.
As with anything else in life, the more humans that are involved in a database release, the more likely it is to fail. DBAs simply can’t perform the same task exactly the same way every time, especially with developers knocking on the door and opening more and more tickets for database reviews, testing, and releases.
This means that DBAs and DB teams have to work extra hours, which increases the chances for errors and mistakes. Also, organizations often have to expand their teams due to the increasing amount of manual work going into database releases. These releases are slow, costly, cumbersome, and come with a hefty price tag.
Database release automation essentially promotes a Shift Left methodology, where key database deployment procedures are automated systematically to create a seamless Database DevOps pipeline. These processes include the build, integration, testing, and eventually the release of the latest version.
Every organization should strive to implement tight source control, where all teams know what database schema version matches with the application version. However, achieving this goal manually is virtually impossible due to the number of stakeholders involved and the dynamic nature of DevOps pipelines.
Database DevOps can only be achieved with database release automation, where old manual tasks (e.g, – script review, validation and execution) are automated for reduced overhead and elimination of manual errors. A special emphasis should be placed on testing. The sooner you detect issues, the fewer bottlenecks you’ll have.
- Fewer Deployment Errors – Happier DBAs
- Fewer Remediation Tasks – Happier IT professionals
- Enhanced Productivity – Happier developers and team leaders
- Faster Application And Feature Releases – Happier business executives
- Painless Audits – Happier CISOs and CIOs
You should keep process standardization in mind while setting up your database release automation solution. This means that your solution should be able to mimic the human actions that are taken while transitioning from the Dev to Test environments, all the way to Production. Create and pre-define milestones all along the pipeline.
Here is another tip for when your Database DevOps is up and running.
Identify the database rules and policies that are most commonly violated. Use DRA to eliminate those rules violations. Think index restrictions that have been defined to four per table. Exceeding this limit, undetectable with manual database management, can be easily caught with the help of DRA.
Automated database version control can be defined as the enforcing of code, structure, and content change policies without manual intervention. When all your development teams are aware of the latest validated database schema change, errors are minimized and so are cumbersome rework processes.
Besides picking a proper DRA solution, you will need to make sure that you:
- Address all database code parameters – code, structure, content, grants
- Announce your version control rep as the “only source of truth”
- Make sure your deployment script knows the exact environment status
- Try to create deployment scripts that merge conflicts beforehand
- Ensure that the deployment script recognizes all database dependencies
Automated version control basically involves a database enforced change management solution that allows the validates changes while debugging unauthorized access or changes that are being made without properly logging in. A detailed history of all changes is also made automatically.
It’s not advised to settle for basic compare and sync solutions, where you have still have to manually review scripts and handle conflicts.
The Result: No Configuration Drifts, Enforced Change Policy, More Testing.
Besides the performance and technical implications of manually managing your database, configuration drifts and other issues can cause a wide range of security and compliance issues that can prove to be very costly. Not passing audits, or being compromised by hackers, can prove to be very costly.
Here are just a few of the leading compliance laws in effect today:
- The General Data Protection Regulation (GDPR)
- The Health Insurance Portability and Accountability Act (HIPAA)
- Sarbanes-Oxley (SOX)
Managing database access roles and permissions manually required dedicated professionals or even teams. Enter plug-and-play automation. You can now define, create, and manage the exact stakeholders who are required to access the database, while also defining and revoking their permissions as needed.
Besides being able to monitor all database activity in real-time and even getting notified about suspicious access, you are also getting an automatically generated history of all database related activity. This activity and change trail means that flunking and failing compliance audits is a thing of the past.
The Result: No Unauthorized DB Access, Automated Change History for Audits.
The modern DevOps ecosystem is complex and hard to manage, with development teams now working from different locations and new workers constantly entering the picture with “less than adequate” onboarding and training. This complexity directly affects productivity and code quality.
By managing your database operations via one centralized dashboard, the DBA automatically becomes more productive. Furthermore, he has more time to analyze the performance metrics to improve planning and prepare for scaling up when needed. These metrics can also be shared with other teams.
DevOps is all about iterative development and strong feedback loops from production all the way to development. A DRA solution with built-in centralized monitoring will help establish just that. It will also eliminate the need for implementing unproven and incomplete third-party monitoring tools.
The Result: Enhanced Transparency, More Insights for Business Decisions.
As the saying goes – time is money. By implementing Database DevOps, your organization will be able to save a lot of time across all departments for a better bottom line. This will also lead to improved design and planning, along with a faster time to market.
A good Database Release Automation solution can help you achieve your organization, performance, and business goals with minimal investment. With today’s markets all about optimizing quality and innovating new products, you simply can’t afford to be bogged down by manual database management.
The time to prioritize and embrace an advanced database DevOps solution is now. Automate your database releases now for sustainable revenue growth.