Database Schema Migration (Schema Change)

Guide

In the fast, complex world of software development, every piece of the puzzle matters. From application design to deployment, each step is pivotal. Amidst this, database schema migration stands as a linchpin, bridging the gap between data integrity and software innovation. For developers, DBAs, engineering leaders, technology executives, and DevOps managers, grasping its nuances is vital.

But this critical stage in the software development life cycle (SDLC) is often one of the most low-tech. It’s a cumbersome but necessary evil with a multitude of opportunities for error. So these same professionals are eager to continuously improve the speed, quality, and impact of database schema migration.

With an optimal approach, database change management can be a catalyst for innovation instead of a hindrance to speed.

As the technological landscape continually advances, ensuring that databases evolve cohesively with applications and other data products is more crucial than ever. You’re likely already planning for the data needs of artificial intelligence and machine learning, faster and more ephemeral release cycles, and, generally, a need for more data, faster.

This guide explains database schema migration, including strategies, best practices, and tools like Liquibase that help with successful transitions and innovations.

What is database schema migration?

Database schema migration is the process of managing and applying changes to a database’s structural framework or schema. Sometimes referred to as simply “database migrations,” they allow the schema to evolve alongside the application.

This often involves tasks such as adding or removing tables, columns, or indexes, as well as modifying data types or constraints. Executed correctly, these migrations ensure data integrity, consistency, and optimal database performance throughout the software development life cycle. As systems evolve and requirements change, schema migrations enable databases to adapt cohesively without compromising existing data or system functionalities. Version control and documentation are essential for tracking changes and maintaining a historical record of schema modifications.

For the broader team – developers, DBAs, IT, DevOps, etc. – migrations are preferred over another method of database deployment, the state-based approach. Schema migrations align more closely with DevOps practices and make for faster, more productive teams.

Term confusion

Data migration

The process of moving data from one system to another. This can be from old to new databases, different formats, or varying storage environments.

In contrast, database schema migration is the process of changing a database’s structure. Movement of the underlying data is not necessary.

Platform/cloud migration

Moving the entire database (schema, software, hardware, data) from one environment to another. This might be a new vendor or technology.

A cloud database migration involves moving a physical, on-premise database into a cloud-based environment.

Sometimes, schema migration will also take place to accommodate new technical requirements.

Back up - what is database schema?

A database schema is an architectural framework that defines the structure, relationships, and constraints of data within a database management system (DBMS). It represents the logical configuration of the entire database, detailing how data is organized and how relational database entities are interrelated. Schemas group objects, including tables, views, indexes, and procedures, into a single logical unit for easier database change management and access.

The four key components of database schema include tables, columns, relationships, and constraints.

Components of database schema

Tables

Structures that store data within the database, containing records (rows) of data defined by attributes (columns)

Columns (fields, attributes)

Columns define the type of data that can be stored in specific sections of a table. For instance, a table tracking software bug reports might have columns such as ‘BugID’, ‘Severity’, ‘Status’, and ‘LastUpdated’.

Relationships

In relational databases, tables can be interlinked or related based on certain conditions. These relationships (one-to-many, many-to-many, one-to-one) ensure data consistency and integrity across different tables.

Constraints

Rules enforced on data columns and tables to ensure accuracy and reliability of the data. Examples include primary keys (which uniquely identify records in a table) and foreign keys (which ensure correct relational data mapping).

Logic

Things like views, functions, procedures, packages, and triggers that manipulate, move, or present data contained in the tables and columns.

Database schema is important in software development to ensure effective communication between applications and data. It acts as a foundation, ensuring that data storage is logically aligned with application functionalities. It’s crafted to reflect the data needs of the application and must be updated accordingly – hence, schema migrations.

Developers interact with the schema to read, modify, or store data as per the application’s requirements. The schema’s integrity, performance, and resilience are tested against potential data scenarios and edge cases. As the application evolves, the schema may undergo migrations or alterations to accommodate changing requirements, without disrupting existing operations. These schema changes also keep pace with query complexity and performance, so that new features requiring new data queries don’t slow down development or user experiences.

Much like the data it holds and the code it supports, the database is a complex and critical business asset. However, it rarely receives as much attention to innovation and productivity as data and development. By extending DevOps to the level of database schema, improvements to productivity, security, and capability can be just as transformative.

How schema migration can accelerate your pipelines

Database schema migration has to happen – whether it slows down or speeds up your pipeline is determined by how effective and automated the process becomes. Manual schema change reviews, which can take hours or even days, are the enemy of acceleration. Such reviews are critical for ensuring that changes are made without disruptions, even as data storage complexities increase. But they slow things down nonetheless.

Change management agility – how quickly you can iterate on database evolution without risking safety, access, security, integrity, etc. – is crucial to accommodate the growth of applications, enhancements in data integrity, and evolving compatibility needs. This necessitates meticulous planning and proactive management, allowing database teams to match the pace of software development and embrace infrastructural advancements.

As applications advance, database schemas must adapt swiftly, maintaining or improving performance, flexibility, and governance. Schema migration allows for changes that meet evolving business and technical needs while maintaining data integrity.

In the CI/CD pipeline, database schema migration is a linchpin. It ensures that database and application changes are harmonized as they progress to production. Keeping the database and application in sync is important to remove bugs or errors caused by data differences, making sure each deployment is accurate and reliable.

6 most beneficial database schema migration use cases

Speeding up the application pipeline is a compelling, yet broad, benefit. Taking a more granular look, database schema migration most commonly pops up in the following use cases. These benefits help organizations achieve DevOps culture changes and meet team or company business goals.

Adding new application features

As applications evolve, new functionalities, data attributes, or other changes can be introduced.

Improving database performance

Optimizations to the database structure for better query, index, and data retrieval performance.

Fixing bugs (sooner, faster)

If bugs, errors, inconsistencies, or other failures occur, they can be corrected through schema migration. Initiated in pre-production environments, schema migration helps catch errors before they go live to users.

Enhancing database security

Strengthen security measures by updating and enforcing access controls, while allowing for governance and observability.

Ensuring data integrity and compliance

Enforcing data validation rules, constraints, and relationships to maintain data accuracy and consistency. Automation tools can also aid in compliance and auditing.Learn more: What is data integrity in database DevOps and change management?

Boosting scalability

Manage the growth and evolution of data stores to handle increasing data volumes, types, and connections.

Benefits of schema migration over state-based deployments

State-based deployment methods compare the current state of a database to its ideal state to find necessary changes. With a migration-based deployment approach, changes are instead described by the user alongside development and applied iteratively. This allows for benefits including:

Small, incremental changes
Consistent processes for all code delivery
Fast feedback loops
Granular feature control
Better testing
Mitigation of database drift

State-based migrations also risk potentially destructive changes, since you’re letting a computer program calculate the steps needed to reconcile differences. For example, if you rename a column, the state based approach may end in the deletion of the original column (and all the data in it) and the creation of a new one. If you change a column’s size or datatype, you might end up in a similar situation in which data is truncated.

When you are descriptive in how the change is made with the migration approach, you are ensuring that your intent for that change is fully realized. When you compare two things and expect a computer to figure out how to get there, it could miss nuances that necessitate special handling and cause serious problems.

While a database schema migration tool like Liquibase can handle state-based workflows by analyzing differences and generating change scripts, the migration-based approach is best applied holistically.

Learn more: Get Started | Database Change Management Approaches

Schema migration in action: real-world examples

Database schema migration practices look differently across various industries and distinct organizations. Each will prioritize different migration benefits, such as customer satisfaction, stability, compliance, cost, security, or other concerns.

Schema migration for finance, banking, and insurance

A major bank implements database schema migration to integrate new regulatory requirements into its transactional databases. The bank updates its systems to follow new rules without interrupting its normal operations. It adds new columns for customer data verification and changes existing tables to store more compliance information.

An insurance company uses database schema migration to introduce new database structures for digitizing and streamlining claims processing. They deploy infrastructure improvements that automate claims tracking, modify existing tables for better fraud detection algorithms, and improve data integrity checks to ensure accurate risk assessment and policy pricing.

Explore the benefits of schema migration automation for financial services.

Schema migration for SaaS

A CRM software company uses database schema migration to roll out a major feature. They add new tables to handle additional customer interaction data and alter existing schemas to incorporate AI-driven analytics features. Without downtime, they enhance service offerings and data processing capabilities for their customers.

In another example, learn how software/data/analytics company i360 leveraged automated migrations to deploy more often, ensure audit trails, get instant failure notifications, and mitigate manual drift detection.

Schema migration for government agencies & public sector organizations

A municipal utility company enhances its service delivery, operational efficiency, and customer service by modernizing its legacy systems. Using database schema migration, the utility creates new tables for smart grid management and integrates customer records with its new online portal. Without disrupting utility or customer service, it streamlines customer interactions, enhances real-time resource allocation, and supports sustainable energy initiatives.

Explore the benefits of automated schema migration for the public sector, like governments and utilities.

Schema migration for healthcare

A healthcare provider utilizes database schema migration to adopt Electronic Health Records (EHR). By restructuring their database to include new tables for patient records, modifying existing ones to link with diagnostic codes, and ensuring HIPAA compliance through enhanced security constraints, they improve patient care coordination and data accuracy.

In another example, check out how MedImpact used schema migration to create consistent, rigorous frameworks for deployments of various database types, integrate with DevOps tools, and create an audit trail.

Schema migration for IT

An IT service provider, managing cloud infrastructure for clients, uses database schema migration to enhance its performance and monitoring capabilities. This includes adding new tables for more granular tracking of resource usage, adjusting relationships for better multi-tenant data isolation, and ensuring scalability as their client base grows.

Schema migration for telecomm

A global telecommunications organization employs database schema migration to support a new 5G network rollout. They use schema migration to accommodate new data types related to 5G, optimize for better real-time data processing, and enhance data models for improved network performance analytics.

How to plan your database schema migration strategy

Adopting the database schema migration approach comes with many benefits. But to maximize its value and of the satisfaction and productivity of those involved, it takes a thoughtful strategy. That strategy needs to be tailored to your industry, business, team, tech, and appetite for automation.

Most importantly – the schema migration process should align with the application update process. In the essential DevOps spirit, the two must be considered two parts of a whole to avoid drifting approaches.

Curate your current schema and set business-aligned goals

Start with a clear understanding of your current database schema and how it aligns with your business objectives. Document why migrations make sense and which potential benefits you plan to observe. Next, establish specific, achievable goals for the migration project that align with business objectives, technology needs, and industry trends. Common top-line objectives include performance improvement, data integrity enhancement, scalability, or handling new compliance regulations.

Document everything

From the beginning of your journey into database schema migration workflows, documentation is critical. Detailed records of schema, changes, migration processes, and challenges make troubleshooting and process evolutions easier to handle. These documented learnings not only build organizational wisdom, but help in training and for launching new iterations.

Keep everyone informed

Document every stakeholder involved in the process and keep them regularly informed of progress, concerns, challenges, and objectives. Be sure to provide the details necessary for each individual to provide their value to the conversation, whether they sit at the C-Suite or on the database team.

To garner enthusiasm that encourages participation and feedback, consider a concerted effort to build the business case for database DevOps. Then you can show how schema migration supports the business’s tech, growth, and efficiency goals.

Ensure consistency and shift left

Consistently manage database updates across pipeline stages to test the change process and result multiple times before deploying to production. Testing in pre-production environments minimizes risk and ensures efficacy.

Reduce risk and back it up

Database schema migration risks can include data loss, extended downtime, or compatibility issues. Before starting, it is crucial to have strong backup and recovery plans to ensure data security and business continuity. Phased rollouts, additional testing phases, or contingency plans for data recovery all build confidence and ensure proper guardrails.

Measure impact

Gauge the effectiveness, efficiency, and impact of database schema migrations by tracking KPIs such as:

Downtime
Query response time
Resource utilization (pre- and post-migration)
DevOps metrics such as:
Deployment frequency
Lead time to change
Failure rate

Based on these measurements, you can decide what to do next with schema migration. This could include troubleshooting, adding more environments, changing goals, or planning the next project.

Now to get granular – what happens during each migration?

Step-by-step: database schema migration

With strategy in place and approved by all stakeholders, you can get to the business of migrating database schema. Here’s each step in the process:

Pre-migration
Evaluate the current schema and outline the migration plan.
Back up data
Prevent potential data loss in case of migration failure with comprehensive data backups (as part of your broader IT backup/recovery process).
Schema changes
Prepare scripts for necessary schema modifications.
Modify tables/columns
Update the database structure to add or alter tables and columns.
Define relationships
Establish how different tables interrelate.
Apply constraints
Implement constraints for data integrity.
Data migration/transformation
Move and transform data to fit the new schema.
Version control
Manage schema changes over time using database version control systems.
Testing and validation
Conduct extensive testing to ensure correct migration implementation.
Unit testing
Verify individual migration components.
Integration testing
Test the overall system functionality post-migration.
Release migration
Carefully deploy changes to the production environment.
Post-migration
Update downstream data systems and application code.
Monitor performance
Keep track of system performance after migration.
Rollback plan
Have a contingency plan for reverting changes if necessary.
Monitor drift
Watch for out-of-process changes (drift) to catch them quickly and avoid downstream problems.

Opportunities for efficiency and automation abound from start to finish. Learn how Liquibase automates database schema migration to extend CI/CD to the database.

Schema migration challenges

While tools and integrations solve many challenges, others require strategic decisions, cultural shifts, and cross-department communication. The most common include:

Data loss or corruption
Compatibility issues
Conflicts and dependencies among concurrent changes
Negative impact on application performance
Challenges in schema versioning
Security vulnerabilities
Insufficient testing
Manual processes slowing down the SDLC
Migration errors

With careful planning, best practices, automation tools, and cultural buy-in these challenges are easily overcome.

Database schema migration best practices

Embrace the following best practices to mitigate migration challenges. Build an efficient deployment process that covers your bases on flexibility and governance while readying your workflow for automation.

Minimize migration downtime

Smaller, more frequent schema changes and automated validation can reduce downtime. So can techniques like blue-green deployment, canary releases, and migrating at off-peak hours.

Proactive rollback planning

If it can’t be easily rolled back, it shouldn’t be deployed. Rollback plans – and tools that automate or even offer targeted rollbacks – are a must-have before any migrations happen. Ensure you can quickly revert to the previous version if something goes wrong. Regularly test your backup and recovery procedures.

Documentation and database version control

Capturing the state of the database (for historical purposes) and detecting drift ensure it maintains fidelity with their expected state.

Enforce data consistency

Quality checks and validations, as well as drift detection, can ensure consistency and raise the alarm on any post-migration differences.

Database DevOps

Schema migration, and subsequent automation, best occurs within an enthusiastic database DevOps culture. Strong collaboration between teams ensures the most value, efficiency, and flexibility.

Database schema migration tools

Manual schema migration can be a tedious, error-prone, and time-intensive process. But it can be automated into one-click deployments, by DBAs or developers, with the right tool.

Common options include Liquibase and Flyway, as well as homegrown solutions that leverage existing tech to connect workflows. Each of these offers solutions for:

Tracking database changes
Applying version control to database schemas
Automating the migration process

However, internally developed solutions are often created as short-term fixes, which can make it difficult to scale and maintain the solution as you grow and expand to new databases. These homegrown fixes also devote precious development cycles to solving your own problems instead of your customers’ problems. Developing the requisite expertise in the ever-increasing stable of data platforms is best left to dedicated solution providers.

Adopting a database schema migration tool protects data integrity while:
Speeding up the process
Validating changes across environments
Detecting issues as early as possible (shifting left)
Facilitating process testing to ensure reliability and consistency
Providing documentation that enhances audit trails and troubleshooting

With more than 100 million downloads and a decade leading the category, Liquibase offers standout capabilities including:

The broadest database support in the industry—more than 60, including NoSQL databases, SQL databases, and data warehouses.
Artifact-based deployments that make it easy to update, manage, and deploy changes.
Advanced features that allow you to control the execution of updates and batch releases.
Automated, multi-step workflows to standardize best practices, simplify repeatable actions, and improve governance.
Full database observability to monitor DevOps metrics to enable continuous improvement for your databases.

Holistically, the best database schema migration tool is really a database DevOps tool. This handles change management as well as database observability and governance for continuous improvement and steadfast compliance.

Get started with database schema migration

Dive into database CI/CD with Liquibase and discover how database schema change automation works. To get your broader team on board with the cultural and technology investments needed for successful database DevOps, learn how to build the business case for database DevOps.

Resources like Liquibase University can help you and your teams quickly onboard and get certified in Liquibase’s capabilities and advanced database DevOps concepts.