Database Change Management


Table of contents

Database change management (DCM) has been a fundamental aspect of database administration since the inception of databases, traditionally rooted in manual processes. Yet, as DevOps methodologies permeate deeper into application and data pipelines, a more efficient approach to database change management emerges, transforming the workflow with automation, control, and agility. 

Within pipelines embracing database DevOps, automating change management empowers application, data, and database teams to innovate rapidly while safeguarding the integrity and reliability of their data stores. This modern approach enables teams to collaboratively and efficiently implement necessary changes, ensuring seamless integration without disrupting the database, application, or overall pipeline.

Depending on an organization's DevOps maturity, there may be some structure around database updates, but it tends to remain one of the last manual holdouts – often overrun with humans intervening –  in an otherwise automated pipeline. Automating database change management closes the last mile of CI/CD, integrating the otherwise slow, manual database change process into a streamlined workflow that minimizes errors by shifting left into earlier environments. 

To understand and capture the value of database change management automation, you’ll need to understand what it is, how it works, and what it means for your application, data, and database teams. You’ll also need the right tools and integrations that leverage change management automation, governance, and observability to accelerate and protect high-integrity pipelines. 

What is database change management (DCM)?

Database change management is the practice of developing, reviewing, tracking, and deploying updates to databases – including their schema (structure), relationships, and data – in a structured manner that strives for efficiency, accuracy, security, and integrity. By treating database change as code, it can be managed with DevOps principles and integrated into modern CI/CD pipelines. 

For application development teams, database change management is critical to ensuring that database schemas and structures align seamlessly with the application's evolving requirements. It provides a structured approach to managing schema changes, migrations, and version control, which are essential for maintaining the integrity and performance of the database as the application grows and changes. 

Because these changes have the potential for significant impacts on database performance and availability, they must be carefully strategized by the application development, DevOps, and data teams who request them. And they must be even more carefully reviewed and validated by database administrators (DBAs) before deployment. In traditional application development settings, database code is written as SQL code, with structural modifications orchestrated through Data Definition Language (DDL) and data alterations managed via Data Manipulation Language (DML).

While database change management can be a process as complex as the data stores it updates, introducing automation (typically as part of complete database deployment automation) can simplify, streamline, improve, and accelerate the process and, subsequently, the complete CI/CD pipeline. Database CI/CD, including change management, brings database change into the immediate feedback workflow that detects, prevents, and remediates problems before they reach production. The managed change process then aligns with the application’s short, incremental release cycles.  

The role of database change management for data teams 

Data scientists, analysts, and business intelligence (BI) professionals rely on the database to provide accurate, consistent, and timely data for analysis, reporting, and decision-making processes. Change management ensures that changes to the database — whether they are schema modifications, data updates, or new data integrations — do not disrupt the quality, availability, or integrity of data used for analytical purposes. 

Database change management enables data governance by setting rules and policies, as well as maintaining a clear record of changes, which is crucial for audit trails, compliance, and data security. It also facilitates the integration of new data sources and the migration of data across platforms, ensuring data integrity and consistency. This is particularly important for BI and analytics projects, where the reliability of data directly impacts the insights derived and the decisions made. 

By ensuring that database changes do not negatively affect data quality or access, database change management empowers data teams to deliver more accurate and reliable analytics and reporting services.

The manual database change management process

 In the case of most databases used within application development and data pipelines, the process for instigating and executing change generally follows a path like this:

Identify the need for the change

Database change starts by recognizing the need to modify the database, typically stemming from factors like new features, performance optimization, bug fixes, data integrations, or compliance updates. It's about understanding what needs to change and why, setting the stage for detailed planning and execution. Changes might deal with:

  • Designing and expanding database structure: This involves optimizing the database schema for efficiency and adjusting hardware allocations to meet new performance demands. The goal is to ensure that the database structure supports the application’s requirements while optimizing resource use.
  • Optimizing table design: These efforts focus on defining table partitions to improve manageability and query performance, and on planning indices to enhance data retrieval speeds. These changes are carefully aligned with usage patterns to avoid adding unnecessary complexity.
  • Maintaining and improving compliance: These changes may aim to update metadata layers for clarity, change access controls for security, and bolster security systems against vulnerabilities. These measures ensure the database's integrity and compliance with data protection standards.

Document and submit the change request

Once a change is identified, an official request documents the specifics: what will be changed, how the change will be implemented, the expected impact, and the steps needed to achieve it. This documentation serves as a blueprint for the change process and ensures clarity and alignment across the team, especially between application developers and DBAs. 

Implement and test the change request

Ideally pushed through a CI/CD pipeline including testing and staging environments, this often involves a lot of manual review work instead. This step is hands-on, involving the actual application of changes to database structures or data, using SQL scripts, migrations tools, or direct modifications through database management tools. This step should highlight any issues or errors with the change before they reach production, making sure changes don’t yield instability, drift, or bugs.  

Review and approve the change

A thorough review process typically involves DBAs, developers, and sometimes business analysts. This step ensures that the change meets all requirements, follows best practices, and doesn't introduce any issues or risks. Approval is needed to move forward with deployment to production environments.

Deploy the change

Deployment of the requested change to the production environment must be carefully managed to minimize downtime and avoid impacting users or applications. It often involves automated deployment tools and processes to ensure a smooth transition.

Finalize and monitor changes

Finally, the team gets confirmation the change is fully integrated and functioning as intended in the production environment. Continuous monitoring follows to quickly identify and address any issues that arise post-deployment, ensuring the change achieves its intended benefits without adverse effects. With database observability capabilities, teams can also analyze the change process itself. 

The exact process varies across teams and may also change as new applications and data sources are introduced. But in general, this process overview covers the journey from identifying a need to implementing a solution. Unfortunately, database change management can often be a slow, cumbersome, and stubborn process due to the complexities of multiple stakeholders and handoffs, a mostly manual workflow, and plenty of room for error.

Challenges of manual database change management workflows

In environments lacking modern database change management processes, the manual workflow presents numerous challenges, each affecting crucial aspects of operations like cost, velocity, agility, and risk.

Lack of organization and strategy

Changes are made without a cohesive plan, leading to disorganized deployments. This absence of strategy not only slows down the execution process but also increases the likelihood of errors, directly impacting project costs and reducing development velocity.

Time-consuming deployments

Manual deployment practices significantly extend the timeline for implementing database changes. Each change requires planning, coding, and execution by database administrators and developers, a process ripe with delays. This sluggish pace hampers an organization's ability to rapidly innovate or adapt to market shifts and customer demands, directly impacting its competitive edge and operational efficiency.

Error-prone processes

The reliance on manual execution for database changes introduces a high risk of human error. Even minor mistakes during the deployment phase can lead to significant issues, including data corruption, downtime, and service disruptions. These errors not only require additional time and resources to rectify but also increase the risk of compromising data integrity and system reliability, further exacerbating the challenges faced by organizations in maintaining high standards of service and compliance.

Insufficient testing 

Due to the cumbersome nature of manual deployments, changes are often not adequately tested. This oversight elevates the risk of deploying faulty changes that can disrupt operations, leading to potential revenue loss and damage to customer trust.

Lack of version control and rollback capabilities

Without version control, tracking changes becomes a challenge, complicating rollback processes. The inability to easily revert changes amplifies the risk of prolonged downtime in the event of an error, hindering operational resilience.

Inefficient and informal communication

The absence of structured communication channels leads to delayed integrations and exacerbates downstream problems. Inefficient communication not only slows down the development process but also introduces gaps in understanding that can affect the quality of deployments.

Security vulnerabilities and lack of observability

The manual approach leaves systems exposed to security risks due to inadequate oversight. Moreover, the lack of observability tools in such environments means issues are often identified reactively rather than proactively, increasing the potential for security breaches and operational disruptions.

Complicated auditability

Without a systematic approach, creating a comprehensive audit trail for compliance and troubleshooting is challenging. This complexity exposes organizations to regulatory and security risks, making it difficult to diagnose and address issues promptly. The reactive nature of problem-solving in such settings leads to delayed resolutions that are often unclear, further escalating costs and impacting the organization's ability to operate efficiently.

These challenges and risks culminate into a pressing need for database teams and the application, DevOps, and data teams they work with to take a managed approach to database change. By addressing these challenges with a structured and automated approach to database change management, organizations can enhance their operational efficiency, reduce risks, and improve compliance, ultimately leading to better cost management, increased development velocity, and greater business agility.

Best practices for database change management

How can your teams improve and optimize their database change management workflow? By following these best practices, you can maximize efficiency, collaboration, and integrity. 

Training and documentation

The point of managing database change is to create a repeatable, consistent process that can be continuously optimized. Critical to that happening? Making note of the actual process!

Teams need to provide training and up-to-date documentation for the team on new tools, processes, or changes to the database structure and workflow. This helps in maintaining a high level of competence and understanding across the teams involved in database change management. It also allows the team to set baseline procedures, iterate upon them, and refine the methodology. 

Cross-team communication

Database change management should simplify communications between teams. Establishing a clear communication plan for informing relevant stakeholders about upcoming changes, potential impacts, and any required actions on their part ensures alignment and preparedness across teams.

These practices help minimize downtime and prevent data loss during migrations or updates, directly contributing to the application's stability and reliability. By fostering close collaboration between developers, DBAs, and DevOps engineers, database change management ensures that database changes are made in a controlled, transparent manner, aligning with the overall objectives of application development and deployment.

Tracking dependencies

If one thing breaks… will the rest of the dominoes fall?

Change management plans need to include dependencies and plans to maintain or avoid them. Understanding and documenting dependencies within the database and between the database and applications can prevent issues arising from changes that impact connected systems or data.

Strategic scheduling

Database changes should happen when their risk of impact on the environments and applications is minimal. Without a cohesive, managed approach to database change, various and sometimes even competing changes can intersect and cause immediate or downstream issues. It can also cause broken experiences for users. Implementing a scheduling system for changes can help avoid conflicts and ensure that changes are deployed during times that minimize impact on users and system performance.

Version control 

Just like code, database changes should be version-controlled to keep track of what’s been changed. This allows for better collaboration and management, maintaining a detailed history of modifications covering the “who, what, when, and why” for each. Version control drives accountability, but, more importantly, improves auditability and accelerates remediation efforts.

Collaboration and productivity also get a boost from version control because this component allows multiple people to work on database changes at the same time, without conflicting deployments. The database updates can then be aligned with application code dependencies as well as the rest of the broader CI/CD pipeline. 

All of this reduces errors associated with changes. Version control also allows for analysis of the database’s state and change over time, identifying database drift or other concerning evolutions. 

Code quality and rule checks

Ensuring the quality of code that defines and manages database changes involves rigorous testing and review processes to identify potential errors or inefficiencies before deployment. These checks can include syntax validation, performance analysis, and security vulnerability assessments. 

By implementing a systematic approach to code quality, teams can prevent problematic code from affecting the database environment, thereby maintaining high standards of reliability and performance. This practice not only minimizes the risk of disruptions but also enhances the overall security and efficiency of database operations.


Rollback capability is a critical safety net ensuring that, in the event of an error or issue arising from a recent change, teams can quickly revert to a previous state without significant downtime or data loss. This capability requires thorough version control and testing of rollback procedures to ensure they can be executed smoothly when necessary. 

By maintaining robust rollback processes, organizations can minimize the impact of failed changes, maintaining system stability and user trust. It also encourages innovation and experimentation, knowing that changes can be safely undone if they do not produce the desired outcomes.


Governance in database change management encompasses the policies, procedures, and standards that guide how changes are initiated, evaluated, approved, and audited. Effective governance ensures that all changes align with organizational goals, compliance requirements, and best practices. 

It involves setting clear roles and responsibilities, establishing approval workflows, and maintaining thorough documentation for accountability and auditability. Strong governance frameworks help mitigate risks, enforce data integrity, and ensure that changes contribute positively to the pipeline’s stability and security.

Continuous feedback

Like any process in the world of data and technology, iterating and optimizing is the way to achieve compounding success. Incorporating continuous feedback mechanisms into database change management processes allows teams to learn and improve from each change cycle. This involves collecting and analyzing feedback from all stakeholders, including developers, DBAs, operation teams, and end-users, to identify areas for improvement. 

Continuous feedback helps in refining processes, tools, and approaches based on real-world experiences and outcomes. This adaptive approach fosters a culture of continuous improvement, where lessons learned from each deployment are used to enhance future change management practices, ensuring that the database change management processes remain efficient and aligned with evolving business needs.

That leads to the final best practice recommendation, one so pivotal it earns its own section: automating database change management.

Automating database change management

The most important, pivotal best practice in database change management is automation. A tool like Liquibase takes the process outlined above and builds it into a system-driven workflow incorporating automatic processes, plus advanced governance and observability capabilities. Automating database change management and looping it into the CI/CD pipeline means you can release database code just like application code – quickly

The most impactful benefits of DCM automation include: 

  • Faster, more efficient pipelines
  • Better and easier alignment with compliance rules
  • Better collaboration on and across teams
  • Enhanced data integrity 
  • Streamlined, predictable database release cycles
  • Reduced errors, risk, and downtime
  • Easier rollbacks and audits
  • Database change workflow observability

By implementing database change management automation, teams can turn slow, manual workflows into self-service deployments complete with version control, tracking, configurable CI/CD, drift detection, and integration across the application and data pipeline. While reducing manual errors and enhancing efficiency, teams can push changes more frequently and reliably.

Automating database change management empowers a significantly more user-centric approach to handling database deployments, with benefits obvious to every team. Developers can push through their own changes confidently and with a self-service workflow that bypasses the need for DBAs to spend hours manually reviewing code. DevOps leaders measure faster, more efficient processes with fewer failed deployments, while at the executive level, IT leaders witness happier, less burdened teams with more capacity to innovate and deliver value. 

How Liquibase automates database change management

Through a process refined by more than 100 million open-source and Pro downloads, Liquibase database change management automation streamlines the workflow by treating database change as code and adopting the database schema migration approach. Automatic change tracking and version control build a record of activity tracking the request’s progress and status, while custom checks on code quality ensure nothing gets by that could cause hiccups later on. 

This setup allows all changes to be tracked in one place, improving visibility. It also enables teams to selectively deploy to different environments and rollback all or target changes if they cause trouble. With reusable workflows, teams can quickly and consistently enable automation between additional database targets and applications, while continuously monitoring for out-of-process changes. 

This automated workflow gives database teams unprecedented levels of control, making it easy to govern changes, access, and pipelines to ensure compliance. The logs collected throughout the process can be piped into monitoring platforms to enable robust database observability that tracks important deployment metrics. For a more detailed look, check out How Liquibase Works

In essence, Liquibase not only transforms database change management into a streamlined and error-resistant process but also empowers teams with the tools for enhanced governance, compliance, and observability, ultimately facilitating a more agile, secure, and efficient database development lifecycle.