How to Handle Schema Changes Without Breaking Your ETL Pipeline?

Schema change is an inevitable part of dynamic data systems. It can occur due to many reasons, some common ones being the inclusion of an additional column and changing data format. These changes can disrupt ETL—extract, transform, and load—pipelines and degrade data migration performance.

Although migrating the existing table schema is effective, it is not a feasible way to manage schema changes, as updates might happen quite often. To overcome this challenge, you can create certain strategies that help manage database schema changes.

This article will demonstrate how you can handle schema changes without breaking your ETL pipeline.

What Are Database Schema Changes?

Database schema changes highlight any modification in the structural component of a database. With growing business requirements, you might need to alter the existing schema to incorporate new data structures and relationships.

In most real-world scenarios, database schema changes include adding a new table, modifying column properties, changing indexes, and deleting an existing table.

For example, an e-commerce website can include another column to an existing products table to track each item’s performance. This will update the existing structure of a table with an additional feature highlighting the consumer sentiments for a product.

Types of Schema Changes

Data professionals often encounter two types of schema changes—source and sink schema changes.

Source schema changes occur on the source end of the data pipeline. When migrating data between diverse platforms, this schema change can break the data pipeline. The schema of the destination platform must adapt to the changes to store the data efficiently.

On the other hand, sink schema changes happen on the destination end of the data pipeline. If the sink schema transforms to a different structure but the data pipeline remains as it is, the pipeline might fail to replicate data accurately. This can result in incomplete data in the destination platform.

Why Handle Schema Changes?

As the database schema design changes, it becomes essential to tackle the issues that it brings. This involves fixing data integrity challenges, performance degradation, compatibility issues, and broken ETL jobs. Let’s explore the need to handle schema changes.

• Data Integrity: Data integrity is the process of maintaining consistency, reliability, and accuracy throughout the data lifecycle. Unhandled database schema changes can lead to data loss, resulting in skewed analysis.‍
• Data Pipeline Reliability: Abrupt schema changes can break the data pipeline your infrastructure relies on to move data. This can cause system failure and disruptions in data relationships due to missing fields.‍
• Data Security: If not appropriately managed, schema changes can create vulnerabilities that compromise data privacy. When modifying access controls, it is crucial to ensure that sensitive data remains safe from unauthorized access.

How to Handle Schema Changes?

When handling schema changes, it is essential to strategize the process for both source and sink ends to prevent further issues. This involves following various methods that streamline the management of schema changes. The following subsections will comprehensively explain how to address schema changes without breaking your ETL pipeline.

1. Data Lineage

Data lineage is the process of tracking information as it passes from a source of origin to a destination via a data pipeline. It is a detailed map that describes the journey of data, which can explain the final state of the data and its relationships.

To optimally trace data movement, you can use data lineage tools. These tools enable you to automate the process of identifying the impact of schema changes. Monitoring the data relationships and table-to-table dependencies aids in understanding how the schema changes propagate through the data infrastructure.

2. Ensure Data Consistency

Data consistency is a core principle of determining data quality. It defines the state where the data remains the same across various locations within your workflow. Maintaining this state requires you to conduct practices like data profiling. You must access the relationships between the affected tables and columns to identify how to proceed with schema change.

For example, if you change the address of a customer in a table, ensure that the changes are replicated across all the related tables and views. To accomplish this, you can create a temporary staging table or implement parallel processing to perform operations simultaneously across different platforms.

3. Create Testing Strategies

A beneficial step to handle schema changes is to incorporate comprehensive testing strategies involving performance evaluation, regression testing, and business logic validation. The test strategies must be conducted in a production-like environment to ensure the rules apply to large-scale data.

Instead of creating custom testing solutions, you can rely on automated platforms that streamline the performance assessment process. These tools can support you in determining whether the transformations maintain data integrity in the ETL process. You can also execute tests to check responses under various load conditions and validate business rules.

4. Enforce Data Governance Rules

Data governance is the process of ensuring high-quality, secure, and compliant management of data throughout its lifecycle. By enforcing certain governance policies, you can ensure that the data remains compliant with industry standards.

Documenting all the schema updates and their impact on sensitive data is crucial. With regular audits, you can affirm that, eventually, the data aligns with the governance policies. Implementing security checks and flagging potential issues before deploying changes is a vital step in any data workflow. You can use prominent data governance tools to automate the process of ensuring regulatory compliance.

5. Communicate with Stakeholders

Effective communication is a key advantage enabling seamless schema change management. Being transparent and specific about the structural changes in the data with the stakeholders facilitates understanding the updates on the existing system.

For impactful communication, you must properly create documentation detailing the expected effect of schema changes on insight generation. The document can also include a rollback procedure in case of any failure, as well as information about the implementation timeline and maintenance window.

6. Use Staging Environment

A staging location can work as an intermediary between the development and production environments, supporting the management of schema changes. Keeping this staging phase similar to production is advantageous, as it allows changes to an environment with identical data volumes, integrations, and access patterns.

Using the staging environment, you can catch potential vulnerabilities that affect the performance of the data management system.

7. Adopt Automated Schema Management Tools

A prominent method that most organizations choose is to adopt tools that enable automated schema management. These tools can assist you in handling schema changes without breaking your data pipeline. One of the go-to choices for schema management is Airbyte.

Airbyte is a data integration tool that allows you to replicate data from various sources to your preferred destination. It offers more than 550 pre-built connectors to facilitate the migration of structured, semi-structured, and unstructured data. If the connector you seek is unavailable, you can use Airbyte’s Connector Builder or Connector Development Kits (CDKs) for custom connector development.

Let’s look at some of the key features offered by Airbyte:

• Automated Schema Management: The Schema Change Management feature allows you to specify how Airbyte must handle source schema changes. This feature enables automated detection of any change in the source schema and helps ensure accurate data synchronization.‍
• Change Data Capture (CDC): CDC allows you to identify incremental changes made to the source systems and replicate them to the destination. Using this feature, you can track data updates and maintain consistency between diverse platforms.‍
• Automated RAG Techniques: With automated chunking, embedding, and indexing options, you can convert raw data into vector embeddings and store them in popular vector databases. These stored embeddings can further be used to streamline the development of AI applications.‍
• Data Security: Airbyte offers robust security features, including role-based access control, personally identifiable information (PII) masking, and multitenancy to safeguard sensitive data. Along with these features, it complies with popular industry standards and regulations, such as GDPR, ISO 27001, HIPAA, and SOC 2.

Although Airbyte supports EL(T) pipelines, it offers ETL functionality with PyAirbyte—a Python library. This library allows you to use Airbyte connectors in a Python development environment. Using PyAirbyte, you can extract data from various systems and load it into SQL caches.

The cached data is compatible with Python libraries, which can further be used to perform custom transformations to manage source schema changes. After performing modifications and applying strategies that enhance data compatibility, you can load the data into a target system.

8. Create Recovery Strategies

It is fundamental to understand that schema changes can impact decision-making even with pre-planning. Before deploying the updates, you must select an appropriate time so the changes do not affect the end users.

Effective recovery strategies, such as backup points, rollback procedures, and triggers for initializing rollbacks, can account for failures such as integration issues, data corruption, performance degradation, and security violations. However, these strategies must be regularly monitored and updated according to requirements.

9. Implement Versioning

Implementing version control is another method to manage schema changes. This will enable you to efficiently store data structure updates, which can be rolled back depending on the scenario.

For example, you can almost effortlessly rollback to the previous state if you encounter unexpected issues during deployment.

Creating an additional validation stage can also benefit schema management. This stage can be used to compare the schema involved in the incoming data stream with the expected schema.

Key Takeaways

Handling schema changes is a crucial component of an organization’s data workflows that enables you to maintain data consistency and integrity across multiple platforms. There are various steps that you can take to manage schema changes efficiently.

Some of the most optimal methods include performing data lineage, creating testing strategies, developing recovery strategies, and using staging environments. You can also automate schema change management procedures with tools like Airbyte.

To learn more about Airbyte, sign up to access a 14-day free trial.