Intro to Database Deployment Automation
If you build and deliver software for your company, you know there’s a constant push to go faster while maintaining (or increasing) quality. Things get tough when your team must deliver faster and at higher quality without any additional resources. Add the challenges a database deployment offers, and your database team can quickly become a bottleneck to application releases.
Across industries, software teams have turned to DevOps and Agile processes to get faster application releases and higher quality code. The name of the game is automation and process re-engineering.
As DevOps continues to spread, organizations are aggressively investing in continuous integration (CI) and continuous delivery (CD) tools such as:
- Microsoft Team Foundation Server
- Travis CI
They might also be relying on CI (or release automation) tools like:
- IBM UrbanCode Deploy
- XebiaLabs XL Deploy
While these tools can bring CI/CD to application code, they do not appropriately address the deployment of database changes.
What is Database Deployment Automation?
Database deployment automation is the practice of including databases in the DevOps process and automating it for the whole pipeline to run more smoothly. With organizations needing to do better and deliver faster than ever, deployment automation has become key to having a competitive advantage.
Database Releases Without Database Release Automation
Before you even think about deployment, you must guarantee effortless database change cadences. Database changes – often in the form of SQL scripts – should be committed to source control. These scripts can be checked out of source control and packaged into an artifact such as a ZIP file with a CI tool like Jenkins. The resulting artifact can be pushed to an artifact repository. Then, a release automation tool, like XebiaLabs XL Deploy, can deploy to environments along the release pipeline all the way to production.
While it may seem like database CI/CD can be accomplished with tools like Jenkins alone, organizations are setting themselves up for failure.
Since databases have state, database changes must be carefully managed because you do not want to corrupt their state. While it’s possible to replace an older version of an application by overwriting it with an updated version, the same isn’t true for the database. Also, a bad database release can result in data loss for the organization or a major outage for an application.
Relying solely on build, configuration, and release automation tools for database deployments puts data at risk. Given the consequences of a bad change, database changes are often handled in a separate, manual process. Organizations are left living with a slower application release velocity and lower code quality inherent with a manual database change process.
In the end, if your team relies on build and release automation tools alone, application and database changes will never flow through the release pipeline at the same pace.
Working with Datical can help
Datical enables you to automate your processes safely from development to production. Whether it’s developers reworking database changes or DBAs needing to perform a database audit, Datical database deployment tools can help eliminate the manual processes that slow releases. Our solutions augment build and release automation tools with a number of essential DevOps capabilities. Together, these tools and automation unify DB changes into the same pipeline that application code flows through.
Database Code Development
It’s not uncommon for a developer to rework a database change prior to a database deployment. Given that databases retain state, reworking a change requires more effort than any other type of code. DBAs often need to manually revert a database environment to allow a developer to rework a bad database change. Without manually undoing the change that needs reworking, a different roll-forward is done in lower level environments such as DEV. Also, a completely different change is applied to higher-level environments that were never exposed to the original change. This breaks the fundamental DevOps concept of “build once, deploy often,” as the deployment to higher environments isn’t consistent with the deployment to lower environments.
With deployment automation, Liquibase enables database CI and simplifies the process of reworking database changes. With Liquibase, developers can treat database code just like application code and check an updated version of the database change into source code control. Liquibase intelligently sanitizes lower-level environments where an older version of the change was made and applies the updated version. It also properly applies only the updated change to higher level environments that were never exposed to the old version of the change. Regular build and release tools are not able to intelligently manage this type of change. Effectively, Datical gets rid of the separate, manual effort, and allows for a consistent artifact that can be deployed through the pipeline.
Database Code Validation
Once a developer checks in code, it’s customary for the code to go through a series of automated tests during the build process. Database code is not so lucky—the validation of database code is a completely manual process.
This is where database CI and CD capabilities from Datical saves the day. With Datical’s Dynamic Rules Engine, you can codify standards and best practices while automating the validation of database code. Datical employs an object-based rules engine as opposed to a simple regular-expression engine so that functional rules such as limiting the total number of indices per table can be easily achieved.
CI/CD and configuration automation tools (such as XebiaLabs XL Deploy, Jenkins, or Ansible) cannot perform this level of validation. Datical’s Dynamic Rules Engine gets rid of tedious manual effort otherwise required by database professionals. It also allows developers to get instant feedback on database changes submitted to source code control, just as they currently get with application code.
Deploying the Database
Bad database deployments can be costly to recover from and possibly fatal to the organization. To get rid of errors or issues in database deployments, the Database Forecast can:
- Build an in-memory model of the target database
- Apply proposed changes to the model
- Verify that the final state of the model meets expectations.
Standard CI and CD tools can’t simulate the impact of DB changes before they are deployed. Instead, they blindly apply SQL scripts to environments, which guarantees a SEV1 outage if done all the way to your production server, which can ruin the state of database deployment. Liquibase provides safeguards in the DB release process so teams can automate database deployments without any risk. In fact, Liquibase automated forecast capabilities can reduce risk and helps achieve much higher success rates on first-time deployments.
Reporting and Auditing
After database deployments, you will need to be accountable and monitor problems that may arise. Whether operating in a heavily regulated industry or not, auditing and reporting on database changes is a best practice. Liquibase Deployment Management Console includes a real-time dashboard that lets stakeholders track and report on the status of every database deployment across the enterprise. Powerful filtering tools built into the interface make it possible to get immediate insight into any specific database change that has been made to a Liquibase managed database.
Datical provides a pipeline view and tracks database deployment velocity at each step in each pipeline. This helps identify and fix bottlenecks quickly. The Deployment Management Console also makes historical reports to help simplify audit and compliance tasks.
Need Help Automating Your Database Deployments?
Bring DevOps to the database and realize more value from your investments. Automated deployments can help teams of all sizes save time and release faster. Contact us or get a demo to learn more about what database automation can do for your software delivery process.
5 Hidden Costs of Manual Database Deployments
Many organizations deal with database schema and logic changes using a lengthy and often costly manual process. It’s important to remember that a task that is expensive doesn’t just cost organizations resources and time. There are unintended costs that are hidden and often ignored.
Here are the 5 hidden costs associated with today’s approach to deploying database changes.
1. Time-to-Market Delays
When production application releases are delayed due to manual database deployments, it impacts time-to-market. Customers aren’t receiving new features, enhancements, and bug fixes quickly. This delay results in decreased revenue and even job security. In today’s market, competition is only a swipe away on a mobile device. Because of this, companies can’t afford to be stagnant in their approach to the database pipeline. The delay in the delivery of new application features to the market will mean that you’ll lose to your competition.
With manual database deployment processes, there are always mistakes. To err is human, after all. These mistakes lead to the need for further repairs. When the database deployment process is completed with no errors, you save time spent diagnosing failed deployments and resolving them. Instead of delivering new features, your teams are fixing unforced errors. This is an unnecessary cost that wastes precious resources and time.
3. Finger-Pointing and Shifting Blame
Covering for one’s mistakes is time-consuming and takes away from far more valuable activities. However, being forced to defend the correctness of database deployments when something goes wrong is even worse.
Finger-pointing often happens with database deployments during a failed environment push. Teams will point to the DB change as the most likely source of the failure. When this happens, it’s left to the data team to prove they did the database deployment correctly, which costs time and money.
4. Interruptions and Task-Switching
Application developers and DBAs are often walled off from each other in their own silos. Because of this, feedback on DB changes is not provided to the dev team until the DBAs review the change. Reviews happen late in the release cycle. Devs are in a hurry up and wait situation. Thus, the feedback loop is too long. By the time the DBAs provide feedback and request for change, the dev team has moved onto the next sprint. This requires an interruption to the current dev cycle and delays to new features. Not to mention a lot of frustration.
5. Quality of life
Poor performing products, out-of-band changes, finger-pointing, and interruptions impact the entire team’s quality of life. In turn, that can lead to staff turnover, in-fighting, and inwardly focused technical teams. These problems distract from creating and delivering great products that benefit the company.
All these elements show that database deployments’ hidden costs are large and impactful. To improve your processes, you must get rid of friction in database deployments and align database code changes with application code changes to guarantee best practices for database deployment. This gets rid of massive costs and helps the company win against the competition.
To learn more about the benefits of database deployment automation and removing these hidden costs read our white paper, The ROI of Automating Database Deployments.
7 Best Practices for Database Deployment
The database is a bottleneck to application deployments. Survey after survey shows that Database Managers and Application Development Managers agree. In each survey, over 90% of professionals involved in software releases are feeling the pain of database deployments.
In many organizations, database deployments are managed by teams outside of the core application development team. Since the database and application must be released as a unit, this two-path approach to deployments is difficult to manage and can create serious visibility problems. While many developers don’t want to admit it, application deployments are very much everybody’s problem.
1. Create visibility in all processes to anticipate problems
Limited visibility can increase the cost of database deployment and gives less time to react to problems. Without visibility, we don’t understand the impact database changes will have on the application, which causes us to move slower.
The same is true for database deployments. With more visibility into the impact of database change, you can see and fix problems before they become issues. This also has the added benefit of allowing faster deployments.
2. Measure every bit of code change
Teams should track every bit of code as it moves through the Software Development Lifecycle (SDLC). With the proper tracking, a team can improve application development thanks to data from:
- User story creation
Furthermore, if your team sees a large number of build failures that are delaying application artifact delivery, they can determine the issue and fix it.
The same cannot be said with database deployments. Typically, when a database deployment is needed, someone will create a helpdesk ticket for the DBAs. Then, the team may attach a SQL script or describe the change in the body of the ticket. And then… there is a long wait.
Without insight into how long it takes to accept the ticket, review it, approve it, and schedule for deployment, the entire database deployment process is a black box. No one knows if the process needs to improve acceptance speed or approval speed. Where does management need to apply more resources to improve our process? The answer is a shrug, which should terrify those with P&L responsibility for the business unit building the application.
3. Automate database deployment to alert team members for problem
Developers are impatient. As Development moves forward to the next sprint, moving back to a previous sprint to fix a database update problem is painful. This not only slows the progress of the current sprint but also forces dev teams to mentally switch to resolve the database deployment issue found by a Production DBA.
This difficulty can be avoided with database deployment automation. The automation process can alert necessary team members that the DB change was not sufficient. Until then, we are going to have to deal with “out of band” changes and interruptions.
4. Encourage your DBAs to tell you “No”
By having insight into database deployments, you can see why changes are being rejected and integrate those learnings into your development process.
Waiting until a Production maintenance window to find out if your proposed database changes are acceptable is inefficient. Understanding what is acceptable to DBAs allows Development to avoid wait states and rework.
If your database team has standards, you need to know what those standards are so you can adhere to them. When expectations are clearly defined, those expectations will be met.
However, there are instances of “DBA shopping” in companies with loosely defined standards and uneven enforcement. Development teams will seek out DBAs that are lax or inexperienced for complex deployments. Thinking that the lack of oversight will improve the likelihood of a successful deployment, the dev teams will actually cause more work. Without a knowledgeable DBA, database changes get pushed to production, causing more work down the line.
5. Encourage hands-on application work until it’s in the hands of the customer
If your application is not in the hands of the customers, they are seeing no benefit from your development efforts. Thus, there really was no point in your making those code changes in the first place. Moreover, app development requires feedback from the customer to improve. That feedback is impossible to receive until the customer is using the application. By relying on a slow, manual, error-prone process to update the database, you slow down your actual database deployments. You must automate the entire application process, not just the application itself.
6. Create the best team possible
As software becomes more and more linked to business performance, it also becomes more and more complex. No one has complete knowledge of all software systems that a business relies on. This is seen in the software development lifecycle. The further left you are in the SDLC, the more knowledge you have of the application. The further right you are, the more knowledge you have of the environment. By having insight into the later stage environments, especially the database layer, developers can help Production DBAs. With this relief, developers can deliver changes that deploy to all environments in the SDLC.
There will be times that database standards should not be applied to a specific change. For example, if a new column needs to be added with default values, most DBAs will reject the change. In the past, new columns with default values would cause a DML lock on a table while the default value is updated row by row. Furthermore, this sort of change can be viewed by the DBA as code smell. Instead, the DBA may insist that the data insert should be made only by the application code.
7. Bring DevOps to the database. Treat database code like app code.
One of the most integral aspects of database deployment is the culture of database DevOps. Companies that adopt DevOps have a higher market capitalization growth rate (total stock outstanding X stock price) than their competitors. DevOps adopters also outperform the S&P 500.
With DevOps, we’ve seen high performing IT organizations quickly attacking new markets and expanding rapidly in existing markets. If your company does not adopt DevOps, you will be looking for a new job because your company will eventually fail.
Development teams may look at database deployments as somebody else’s problem or a mere annoyance. But the truth is that the state of the database deployments of an organization can affect a developer’s paycheck. Thanks to database DevOps and CI/CD for databases, jobs are now at risk due to competitive threats from other companies that are more agile from an IT perspective.
There is a solution, though. Automated database deployments or Application Release Automation can help. To complete the task, you must have your DB changes follow the same path as your application changes. That means:
- Using the same source code repository for both database and application changes
- Providing lights out and single click deployments for all environments
- Demanding immediate feedback to proposed database changes like you have for your code
If you have already applied these patterns to your application code, it’s time to bring the database deployment into the future the same way.
To learn more about the role of developers in database DevOps read: Making DB Changes Easy for Application Developers.
Not sure where to start with database automation? Speak with one of our database specialists to discuss your unique requirements and how to build the best database CI/CD foundation for your software releases.