Schema changes are never ‘just’ schema changes.

A simple schema standardization within your CRM system could easily turn into days of a data reconciliation nightmare if overlooked, making your data completely unreliable and unusable. 84% of stakeholders report serious production issues caused by database change errors, making schema changes one of the most common causes of downtime.

But why does it have so much power over your data’s reliability? To understand their impact, it’s important to first define what a schema change entails.

What is Meant by a Schema Change?

Schema changes refer to modifications made to the structure of a database, impacting how data is organized, stored, and accessed. These changes alter the blueprint of the database and are often made during maintenance windows to minimize disruptions. 

Common examples of schema changes include: adding or removing tables, updating column properties or data types, modifying relationships between tables, altering constraints, and indexes.

Such changes often arise from evolving business requirements, the need to support new features, or addressing technical debt. While schema changes are inevitable for maintaining and evolving databases, they pose significant challenges with risks. 

Poorly managed changes can disrupt data pipelines, break dashboards, and impact critical business operations like revenue tracking and product functionality. This makes managing schema changes a critical aspect of database administration.

Schema Change and Schema Drift: How do they Differ?

Often schema change and schema drift are confused and used interchangeably. Understanding their differences is important for managing your data systems. The below table covers the differences between them in essence.

Potential Risks of Schema Changes

Schema changes, while necessary, can introduce significant risks to both downstream and upstream systems if not carefully managed. For example, if a column is renamed without updating downstream ETL processes, it could lead to failed data pipelines and inaccurate analytics. Therefore, these changes impact not only how data is structured and processed but also how dependent systems interact with the database.

1. Data Integrity Issues: Changing column data types or adding required columns can cause insert failures, causing problems with data quality. Dropping columns or changing data types without proper validation can result in the loss of critical data too.
2. Broken Downstream Processes: Downstream processes that rely on specific schema structures for eg: applications, reports, and analytics pipelines may fail if the changes are not propagated correctly, causing delays, downtime and inaccuracies in data delivery.
3. Increased Technical Debt: Poorly managed schema changes can lead to inconsistencies across systems, making it harder to maintain and scale your data infrastructure. Altering data types can create mismatches between source and target systems, leading to inconsistent data.
4. Overheads and Misalignment: Frequent or unplanned schema changes can strain your team’s resources and increase the risk of errors. Schema changes can render existing metadata (e.g., data dictionaries, lineage maps) obsolete, making it harder to understand and trust your data.

What Causes Schema Changes?

Schema changes occur as organizations adapt to evolving business needs, technical requirements, and industry standards. These changes are often necessary to support growth, improve system performance, and maintain compliance.

Some of the key causes are listed below:

1. Adapting to New Business Offerings: When businesses expand or alter their offerings, new types of data must be stored. For example, an e-commerce bookstore adding digital downloads to their offerings requires fields for download links, file sizes, and formats, prompting schema updates.
2. Scaling for Performance Optimization: Scaling applications often necessitate performance optimization, such as denormalizing data, adding indexes, or creating new columns to speed up query performance. For instance, if search functionality slows down, schema modifications may include indexed fields for search optimization.
3. Data Migration and Reconciliation: Migrating data across platforms can result in schema changes, especially if data becomes corrupted or formats differ. In that case, data reconciliation ensures accuracy post-migration.
4. Feature Enhancements and Structural Improvements: Enhancements like new features, performance improvements, or normalized structures to reduce redundancy often require schema adjustments.
5. Database Platform Transitions: Switching to a new database system might mandate schema redesigns to meet platform-specific requirements.
6. Compliance with Regulations and Standards: Industry regulations or technological updates frequently necessitate schema changes to ensure compliance or alignment with updated standards.
7. Integration of New Data Sources: Integrating new data sources into existing systems may require schema modifications to accommodate varying data structures.

How to Detect and Monitor Schema Changes

Schema change detection involves systematically tracking modifications to database structures across environments. Think of it like having security cameras monitoring your house – you want to know whenever something changes and who changed it. 

Here’s how you can successfully detect and monitor schema changes in databases with the help of key strategies, expert-recommended best practices, and modern tools.

Strategies for Detecting Schema Changes

1. Proactive Automated Monitoring: Implement data observability tools that continuously monitor your database schema and alert you to any changes. Some of these tools also come with data lineage tracking capabilities, so they understand the impact of these changes downstream and allow you to prioritize them accordingly.
2. Version Control: Use version control systems to track schema changes and ensure consistency across environments. Implementing version control for schema changes is important in order to avoid confusion and inconsistencies. Without tracking changes, it becomes difficult to understand the evolution of the schema and its impact on data reliability.
3. Data Profiling: Regularly profile your data to identify discrepancies between expected and actual schema structures. This will also help you to manage your metadata effectively. Managing metadata changes is vital, as alterations to metadata can lead to significant impacts on the outcomes of database queries.
4. Cross-Team Communication: Establish clear communication channels between data engineers, analysts, and stakeholders to ensure everyone is aware of schema changes. Formal change management processes are needed to avoid chaotic schema changes, and ensure proper oversight to prevent errors that affect data reliability.

You can also follow these best practices to proactively identify and manage these schema changes before they disrupt your business operations and analytics.

• Documentation: Maintain a comprehensive record of all schema changes, including the reason for the change and its impact. Adequate documentation of schema changes is necessary to avoid misunderstandings among team members. Without clear documentation, maintaining data reliability over time becomes challenging.
• Test Changes in Staging: Ensure you test schema changes in a staging environment prior to deploying them to production. Rigorous testing of schema changes is essential to ensure that changes do not negatively impact data reliability. Insufficient testing of schema changes can lead to undetected issues.
• Establish Governance Policies: Define clear policies for who can make schema changes and under what circumstances. Elements like data contracts help you define clear agreements between data providers and databases, detailing the expected structure, format, and quality of data. They set validation rules, service expectations, and protocols for managing changes. These contracts not only help you ensure smooth communication, reduce risks during schema updates but also help you to maintain consistency and reliability across data workflows.
• Automate Data Quality Checks: Schema changes may require updates to data quality checks to avoid undetected data quality issues. Use tools like DQLabs to automatically re-run data quality checks after schema changes. It’s essential to ensure that data remains accurate and consistent during the transition to a new schema. In addition to that, modern data quality tools also come with lineage tracking capabilities.

Wrapping Up: Detect Schema Changes Early on with DQLabs

Schema changes can introduce risks that compromise data reliability if they go undetected. While they help businesses adapt to changing business and technical needs, it is important to understand the risks associated with them. Monitoring schema updates is one way to help ensure your data quality without degrading it. The right tool, like DQLabs, can make this process easier by automating the detection and alerting based on severity to the respective users. It also offers an extra layer of security with automated quality checks post any changes or updates, maintaining the integrity of your data.

Want automatic alerts to the schema changes that happen in your data systems? Schedule a personalized demo today.