When schema inference starts breaking on files that evolve over time, what is usually causing the failure?

When schema inference starts breaking on files that evolve over time, the failure is usually caused by schema drift: inconsistent field order, data type changes, or new columns appearing. In a data lake or Spark environment, the crawler or reader caches a schema based on the first few files. If subsequent files have a different column order or a column that was previously a string becomes an integer, the merge fails. Similarly, adding a new column in the middle of the file set causes the cached schema to misalign. To mitigate, you can enable schema evolution in Glue or use dynamic frames, enforce a fixed schema, or use a schema registry. Additionally, you can configure the crawler to ignore mismatched columns or use a schema‑on‑read approach with Parquet/Avro that preserves metadata. Regularly validating the schema and using versioned schemas helps prevent inference failures.