How to manage database schema changes

Schema changes are a source of dread for pretty much any data team. No matter how many times you’ve checked for downstream impact before implementing, it seems like something always breaks, and there’s always cleanup to do afterward.

Schema changes gone wrong have more than just back-end implications, too. Depending on the end point of your data pipeline, everything from your revenue dashboards to your sales pipeline to your product itself could be affected.

Effective schema change management amplifies the trust and credibility of your entire data operation. Below, we’ll examine how to address these challenges and implement schema changes that keep your data accurate and actionable.

What is a database schema change?

A database schema change encompasses any modification to a database’s fundamental structure. Whenever you modify a schema, you’re essentially changing the blueprint that defines how your data is organized, stored, and accessed.

Typical schema changes include: 

• Adding new tables or removing existing tables 
• Modifying column properties or data types
• Changing relationships between tables
• Adding or removing constraints
• Altering indexes

And, of course, in the software world, things change quickly, so it’s common to see changes or new fields as your teams want to track different aspects of their work.

So, why might you need to manage schema changes? Sometimes, schema changes are required to fix structural issues or address technical debt that has accumulated over time. Or, perhaps you need to:

• Support new features or functionality in your application
• Improve system performance by optimizing data structures
• Enhance data security through better constraints
• Adapt to evolving business requirements

Schema changes are a regular part of every data team’s job, and it’s important to have reliable processes for managing them.

Upstream vs. downstream effects of database schema changes

Managing database schema changes is a little bit like rock climbing. On one hand, you need to be on the lookout for rocks or debris coming from climbers ahead of you, and on the other, you want to be conscious not to throw stones down onto anyone underneath you, too. For schema changes, we’d call that upstream and downstream effects.

Let's say you have a `CUSTOMER_ORDERS` table in Snowflake, and you want to add a `CHANNEL` column to show where each order was placed. This might seem harmless, but the implications are broad. Upstream, if you don't update your pipelines, you'll end up with incomplete data. Meanwhile, downstream, dashboards or models relying on `SELECT *` could start pulling the new column unexpectedly, breaking transformations and dashboards.

Potential downstream risks of database schema changes

Don't be fooled by the scale of a schema change—even a small adjustment can cause big problems for connected systems.

When modifying your database schema, the most immediate concerns often arise in downstream systems that consume your data. When you change one schema, you're not just tweaking code—you're hurling a stone into a digital pond, creating waves that can swamp business intelligence, machine learning, and every other department that relies on them.

Here are some of the most common downstream risks to look out for when managing schema changes.

Broken references

Renaming or deleting database objects can create a cascade of broken references throughout your data ecosystem—leading to issues like dashboards suddenly stopping their regular refreshes because they can’t find a renamed column.

The results can be more subtle, too. Reports might continue to run but show incorrect data. Automated processes might fail silently, leading to data gaps that aren’t immediately apparent. 

Complex systems that intertwine multiple dependencies can create a house of cards. Without clear documentation, and you're left searching for a specific culprit among a maze of broken components.

Invalid data types for operations

Changes to data types can introduce particularly insidious problems. When you modify a column’s data type, you’re potentially affecting every operation that uses that data. 

For example, changing a numeric column to a string might seem harmless, but it can cause calculations to fail, lead to type conversion errors, and result in incorrect data aggregations.

Data problems can creep up when you least expect them. A report that previously summed numerical values might start concatenating them as strings. Statistical calculations might fail entirely or, worse, produce incorrect results that appear correct but are fundamentally wrong.

Decreased performance

Schema modifications can have far-reaching consequences on system performance that aren’t immediately obvious during testing. 

Tweaking indexes or data types is like rewriting the rulebook for your database engine, directly affecting how queries get processed. Even a seemingly simple change—like adding a new column with a default value to a large table—can cause performance degradation during the modification process.

Beyond the initial effect, there's often a ripple that spreads far and wide. Queries that previously performed well might suddenly slow down as the query optimizer chooses different execution plans based on the modified schema. 

This can lead to increased resource consumption, slower data refreshes, and extended processing times for dependent operations—not to mention frustrated stakeholders.

Inconsistencies in data

Schema changes can introduce subtle data inconsistencies, particularly when dealing with related tables or denormalized data structures. 

For example, if you modify a column in one table but forget to update corresponding columns in related tables, you might create mismatches that negatively affect data integrity. It's bad news when inconsistencies pop up in complex systems or those relying on materialized views. The web of relationships quickly becomes overwhelming.

The ripple effects of these inconsistencies often surface in join operations, where mismatched data types or column names can lead to incorrect results or missing data. These issues might not be immediately apparent, only popping up for specific edge cases or when reconciling data across different systems.

Data governance issues

Schema modifications might inadvertently expose sensitive data, violate privacy requirements, or break established security controls. Removing a column-level encryption requirement or changing access controls, for example, might expose personally identifiable information (PII) to unauthorized users.

Governance concerns extend far beyond the protective borders of data privacy. When you make changes to your schema, you're also affecting audit trails, data retention, and regulatory reporting. Regulatory red tape and technical nuance are the challenges you'll face when attempting to make schema changes while keeping compliance in check.

How to mitigate downstream schema change risks

While downstream risks from schema changes can seem daunting, they can be effectively managed with the right combination of tools, processes, and preventive measures. A solid plan in place helps your schema changes go live without a hitch, while keeping your downstream systems intact and your data users confident in the results.

1. Use lineage tracking

One of the most effective ways to manage the impacts of schema changes is through data lineage tracking. Data lineage tools can automatically track these relationships, making it easier to assess the potential impact of any proposed changes.

By maintaining a clear map of table-to-table dependencies and column-level relationships, you can better understand how changes will propagate through your system. 

Effective lineage tracking should go beyond documenting direct dependencies. It should capture transformation logic, business rules, and data quality requirements. This holistic view enables you to predict how schema changes might affect not just the immediate consumers of your data but also downstream processes and business operations.

2. Run end-to-end tests

Testing schema changes requires a systematic approach that goes beyond simple functional verification. You need to establish a comprehensive testing strategy that includes regression testing, performance testing, and validation of business logic. This testing should occur in an environment that closely mirrors production, with realistic data volumes and usage patterns.

You can't afford to get bogged down in manual testing. Automated tools are the key to streaming your process, weeding out hiccups, and pushing through to launch. These should include:

• Data quality validation to verify that transformations maintain data integrity
• Performance testing under various load conditions
• Integration testing with downstream systems
• Validation of business rules and calculations
• Testing of edge cases and error conditions

3. Monitor performance

Performance monitoring should be implemented before, during, and after schema changes. Numbers to watch include query execution times, resource usage, and latency. 

Tracking your baseline performance metrics before making adjustments will help you instantly spot any areas that need attention.

You'll want to monitor performance both in the initial burst and the long haul. Some performance issues might only be discovered under specific conditions or load patterns. Stay one step ahead of performance glitches by automating alerts for specific thresholds. This safety net will catch any drops in performance before they escalate, giving you room to troubleshoot and resolve the issue before it becomes a bigger problem.

4. Ensure data consistency

Maintaining data consistency during schema changes requires a multi-faceted approach. Before you begin, assess the connections between affected tables and columns, considering both the rules set in stone and the unwritten rules of the business. You need to be surgical when making changes to your data model to avoid downstream failures.

For example, if you modify a customer identifier format in one table, you need to make sure that this change is reflected in all related tables and views. This might involve creating temporary staging tables, implementing parallel processing strategies, or using transitional periods where both old and new formats are supported.  

It's time-consuming, but being thorough about proactively maintaining data quality and consistency can prevent even bigger problems down the line.

5. Enforce data governance protocols

Strong governance protocols mean not only reviewing security implications but, ensuring compliance with data privacy regulations and internal policies, too.

Document all changes thoroughly, including the rationale behind them and their potential impact on sensitive data. Regularly audit your schema changes to ensure they align with governance policies and maintain appropriate access controls. Consider implementing automated checks that flag potential governance issues before deploying changes.

6. Communicate with stakeholders

Clear communication is often the difference between a successful schema change and a problematic one. Be as clear and specific as possible, letting stakeholders know exactly what to expect during a change.

Your communication should include:

• Detailed documentation of the changes being made
• Expected impact on different systems and processes
• Implementation timeline and maintenance windows
• Contact information for support during the transition
• Rollback procedures in case of any issues”

7. Deploy changes during low-traffic periods & have a rollback plan

Even with careful planning, schema changes can go wrong. Schedule a time to implement changes when they'll have minimal impact on business users and systems.

A recovery plan is also essential. This plan should include detailed rollback procedures, backup points, and clear criteria for when to initiate a rollback. You should also test your recovery procedures regularly to ensure they work as expected.

Your recovery strategy should account for different types of failures, like:

• Data corruption or loss
• Performance degradation
• Application compatibility issues
• Integration failures
• Security or compliance violations

8. Use staging environments

As mentioned, staging environments are a good idea for testing schema changes safely. Your staging environment should be as close to production as possible, including similar data volumes, access patterns, and integrations. By catching potential problems early, you can sidestep the hassle of fixing them in live systems.

9. Implement versioning

Version control for database schemas is as important as version control for application code. Implement a system that tracks all schema changes, including who made them, when they were made, and why. This history becomes invaluable when troubleshooting issues or planning future changes.

Additionally, using version control allows you to roll back to previous versions of your schema if needed. This can be especially helpful when a deployment introduces unexpected problems and you need to quickly revert to a previous working state.

Potential upstream risks of database schema changes

While downstream impacts often get the most attention, schema changes can be equally damaging to upstream systems that feed data into your database. The effects could be vast—from bad data to performance issues to broken systems.

Data integrity issues

Upstream systems often rely on specific schema structures to maintain data integrity. When you modify these structures, you risk creating situations where incoming data no longer meets your database’s requirements. This can lead to failed insertions, constraint violations, and data quality issues.

For example, if you add a new required column without providing a default value, upstream systems might fail to insert new records. Similarly, changing data type constraints might cause previously valid data to be rejected. These issues can be particularly problematic in systems with real-time data ingestion requirements.

Data entry conflicts

When schema changes aren’t properly coordinated with upstream data providers, you risk creating misalignments between what these systems are sending and what your database expects to receive. In systems where data pours in from multiple sources, it's trial by fire to manage the flow.

Common data entry issues include:

• Forms and applications submitting data in outdated formats
• ETL processes using incorrect field mappings
• API integrations failing to adapt to new requirements
• Batch processes sending incompatible data structures

Performance degradation

Schema changes can hurt the performance of upstream write operations. Adding new indexes, for instance, means the database needs to update these indexes with each insert or update operation. This data pipeline can get clogged when additional overhead starts piling up, causing slowdowns.

Say you want to make sure you're not getting duplicate records, so you add a rule that checks for this. While this does help keep your data clean, it also means your database now has to stop and check every new piece of information against what's already there.

Those bulk uploads that used to take no time might take a lot longer because of extra checks. Finding the right balance can be tricky, but it’s a worthwhile undertaking to maintain good performance.

Broken ETL jobs

ETL processes are vulnerable to schema changes because they often rely on specific table structures and column names. When these change, the entire ETL pipeline can break. If you're dealing with complex processing logic or have to hit tight deadlines, the fallout can be catastrophic.

Failed ETL jobs can lead to:

• Missing or incomplete data in your warehouse
• Delayed reporting and analytics
• Broken data dependencies
• Resource contention from retry attempts

Compatibility issues with external systems

External systems that integrate with your database often make assumptions about its structure. When you modify the schema, these assumptions may no longer hold true. This can lead to failed API calls, broken integrations, and synchronization issues between systems.

Integration problems frequently surface in:

• Third-party applications using your database
• Custom-built interfaces and middleware
• Data replication systems
• Backup and recovery tools

How to mitigate upstream risks

When you have clear guidelines, a culture of transparency, and thorough testing in place, you're better equipped to update your database structure without disrupting your data flow.

Stay one step ahead of upstream risks with these simple yet effective strategies:

1. Setting expectations—mistakes will happen

Changing your database structure always involves risks. The key is to proactively plan for them rather than fear them. Make sure your dev and database teams communicate and work together. When everyone's in sync, it's easier to catch potential issues early on.

Establish clear channels for:

• Discussing proposed changes before implementation
• Sharing impact assessments
• Coordinating testing efforts
• Planning contingency measures

2. Create a checklist before making changes

Break down your change management into three key steps: plan it out, put it into action, and double-check your work. Having a clear checklist and documentation for each step is like a safety net that helps keep everything organized and helps reduce the things that may fall through the cracks. 

Your change management checklist should include:

• Impact assessment on upstream systems
• Communication plan for affected stakeholders
• Testing requirements and acceptance criteria
• Rollback procedures and triggers
• Post-implementation monitoring plan

3. Implement data lineage

Use data lineage tools to map the complete flow of data through your systems. Schema changes don't happen in a vacuum—this visibility shows you which processes stand to be affected and the often complex path data takes as it moves through your organization.

4. Create a data contract

Data contracts create clear expectations between upstream providers and your database. These contracts should specify exactly what data is expected, in what format, and under what conditions. As schema modifications pop up, contracts facilitate a smooth handoff.

Data contracts should be built from a few core components:

• Required fields and data types
• Validation rules and constraints
• Quality expectations
• Service level agreements
• Change notification requirements

5. Isolate the incident

When schema changes cause problems, containing the impact is one of the first things you should set out to do. Quarantining trouble spots will help prevent breakdowns so your entire system stays online. You might need to set up intermediate holding tanks, flip switches on specific features, or reroute processing through multiple channels. 

The specifics will vary depending on the systems you have in place, but make sure isolation procedures are thought out and practiced.

Stay ahead of database schema changes with Metaplane 

While the impact can be high, managing database changes doesn't have to be overwhelming. Having the right mix of technical know-how, planning, and communication allows you and your team to handle any changes confidently. 

Metaplane’s data observability platform tackles schema change management head-on, serving up real-time snapshots of changes and their effects on your entire data landscape. Whether you're modifying a single table or performing a massive overhaul, Metaplane's automated schema monitoring, impact analysis, and intelligent alerting features help ensure your schema changes are successful.

Ready to take control of your database schema changes? Sign up for Metaplane today and discover how data observability can transform your schema management process.