· Data Engineering · 2 min read
Surviving the Shift: Handling Schema Evolution in Production
Data changes. Columns are added, types are modified. If your pipeline can't handle this, it's brittle. Here is how to handle Schema Evolution gracefully.
In a perfect world, data structures would never change. In reality, the marketing team adds a “TikTok Campaign ID” to the tracking pixel on a Friday afternoon. If your ingestion pipeline expects a fixed list of 5 columns, it will fail when it sees the 6th.
Strategy 1: Schema Merge (The “Just Make it Work” approach)
Modern formats like Delta Lake allow for automatic Schema Evolution. You can set mergeSchema=true when writing.
- If a new column appears in the source, Delta adds it to the destination table automatically.
- Risk: You might end up with a messy table full of garbage columns if the source keeps sending random data.
Strategy 2: The Schema Registry (The Strict approach)
You use a central registry (like Confluent Schema Registry).
- The producer must register the new schema before sending data.
- If they send data that doesn’t match the registry, the pipeline rejects it to a “Dead Letter Queue” (DLQ).
- Benefit: Keeps your data clean.
- Downside: Can slow down development.
Strategy 3: Semi-Structured (The Variant approach)
New databases like Snowflake allow a VARIANT or JSON column.
- You keep your core columns (ID, Timestamp) strict.
- You dump everything else into a
metadataJSON blob. - This gives you the flexibility of NoSQL with the power of SQL.
At Alps Agility, we typically recommend a hybrid approach: strict schemas for core business entities, and flexible evolution for event streams.
Are your pipelines brittle? Let us help you build robust systems that bend without breaking. Contact us.
