[FAQ Bot] NEW: How does dlt handle schema evolution?

_questions/data-engineering-zoomcamp/module-3/040_3a53549d08_dlt-schema-evolution.md

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+id: 3a53549d08
+question: How does dlt handle schema evolution?
+sort_order: 40
+---
+
+dlt automatically detects and adapts to most schema changes during ingestion, so you usually don't need to manually alter tables.
+
+What happens when the source schema changes:
+- If new columns appear, dlt adds the new columns to the destination table.
+- If new nested fields appear, dlt also creates the required child tables or columns.
+- If existing columns disappear, the columns remain in the table (they are not dropped).
+- If existing columns change their data type, dlt will try to safely coerce the data; if that's not possible, it raises an error so you can handle it explicitly.
+
+How it works under the hood:
+dlt infers the schema from the incoming data and it stores the schema and pipeline state locally (in the `.dlt` folder). On the next run, it compares the incoming data with the stored schema and applies the necessary migrations to the destination.
+
+Why this is useful in the course:
+- You can ingest evolving APIs or semi-structured JSON without writing DDL.
+- Your pipelines keep working even when new fields appear.
+- It's safe for incremental loading — schema updates don't require a full refresh.
+
+When you may need to intervene:
+- If a column changes to an incompatible type
+- If you want to enforce a specific schema or data type
+- If you want to drop or rename columns
+In those cases you can define the schema explicitly in your dlt resource.
+
+Note: If you want to know more, there is a page dedicated to schema evolution in the official dlt documentation: https://dlthub.com/docs/general-usage/schema-evolution