Managing Schema Evolution in Data Lakes

But in reality, data is messy and constantly changing. New fields are added, data types are modified, and old fields are removed. This phenomenon, known as schema evolution, is a natural part of any data-driven organization. While managing these changes in a relational database is relatively straightforward, it becomes a major challenge in a data lake, where data is often in semi-structured formats and comes from many different sources. Unmanaged schema evolution can lead to pipeline failures, data corruption, and a complete breakdown of trust in your data.

The Challenges of Schema Evolution

Managing schema evolution in a data lake presents several unique challenges.

• Pipeline Failures: The most immediate problem is a broken data pipeline. If a source system suddenly adds a new column, a rigid pipeline that is not designed to handle the change will likely fail. This leads to costly downtime and data loss.
• Data Corruption: When a field’s data type changes (for example, from an integer to a string), downstream systems that are not updated may misinterpret the data, leading to quality issues and inaccurate analytics.
• Lack of Central Governance: In a data lake, data is often ingested by many different teams. Without a central process for managing schema changes, there can be a chaotic mix of versions and inconsistent data formats.
• Backward and Forward Compatibility: Downstream applications that consume data from a lake need to be able to handle both old and new versions of the schema. A new application should still be able to read old data, and an old application should be able to ignore new fields. This is a complex problem to solve without a clear strategy.

Core Strategies for Managing Schema Evolution

To successfully manage schema evolution, you need a combination of policies, tools, and best practices.

• Establish a Schema Registry: A schema registry is a central repository for all data schemas. Think of it as version control for your data structures. When a new schema is proposed, it is registered, validated against a set of rules, and assigned a version. This single source of truth ensures that all data producers and consumers are using the same schemas. Apache Avro and Protobuf are popular formats that are often used with a schema registry.
Embrace Compatibility Rules:
• Backward Compatibility: A new schema can be read by a consumer that was written for an old schema. This is achieved by only adding new fields and making them optional.
• Forward Compatibility: An old schema can be read by a consumer that was written for a new schema. This is more difficult to achieve and often involves the consumer gracefully ignoring any unexpected new fields.
• Full Compatibility: Both backward and forward compatibility are supported.
• Use Data Formats That Support Schema Evolution: Not all data formats are created equal. Formats like Avro and Parquet were designed with schema evolution in mind. They store the schema as part of the data file, which allows readers to understand the structure of the data even if it has changed. This is a key advantage over formats like JSON, where the schema is not embedded in the data itself.
• Adopt the Expand-Contract Pattern: This is a safe way to make a breaking change, such as renaming or removing a field. It’s a multi-step process:
  1. Expand: Add the new field to the schema and write data to both the old and new fields.
  2. Migrate: Update all data consumers to read from the new field.
  3. Contract: Once all consumers are updated, you can safely remove the old field from the schema.
• Automate and Monitor: Manual schema management is prone to human error. Automation is key. You can use tools to automatically detect and validate schema changes, trigger alerts when a non-compatible change is made, and monitor your data pipelines for any issues.

Integrating Schema Evolution into Your Data Pipeline

Managing schema evolution should not be an afterthought. It needs to be an integral part of your data pipeline design.

• Ingestion: The ingestion layer should be robust enough to handle schema changes from source systems. Tools like Apache Spark or Flink can automatically infer schemas and handle minor changes, but for more complex changes, a schema registry and data format are essential.
• Transformation: In the transformation layer, you can use a schema registry to enforce and validate schemas. This ensures that the data being transformed adheres to the business rules you have defined.
• Consumption: Downstream consumers (BI dashboards, machine learning models) should be aware of schema versions and be designed to handle backward-compatible changes gracefully.

Conclusion: A Foundation of Trust

Managing schema evolution is a fundamental task in modern data engineering. By implementing a proactive strategy that includes a schema registry, clear compatibility rules, and a robust data format, organizations can build a resilient and reliable data lake. This approach not only prevents costly pipeline failures but also builds a foundation of trust in the data, which is essential for making informed business decisions.