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Fail-fast

As explained in Architecting an Apache Iceberg Lakehouse

Fail-fast ingestion rejects records or halts processing when incoming data does not conform to the expected schema.

Fail-fast ingestion rejects records or halts processing when incoming data violates the expected schema. It protects Iceberg tables from invalid data and exposes schema problems early, but can cause repeated failures and downtime when source quality varies. section 6.1.3

Apply fail-fast checks to:

  • Data type mismatches that cannot safely conform to the target definition. section 6.1.3
  • Missing fields or other schema inconsistencies in incoming records. section 6.1.3
  • Critical fields where preventing invalid records matters more than uninterrupted processing. section 6.1.3

Selective enforcement

A pipeline validates incoming records against the current Iceberg table definition and stops ingestion when a critical field has an incompatible type, rather than allowing the record into the table. section 6.1.3

How Architecting an Apache Iceberg Lakehouse covers it

How deep it goes
Defines rejection and halting behavior, explains tradeoffs, and stops short of prescribing pipeline-specific implementation.
Examples
Uses data type mismatches, missing fields, malformed records, and schema inconsistencies as validation cases.
What it assumes
Assumes an expected table schema and schema validation process, while treating source-data variability as an operational concern.
What you can do after
Diagnose schema problems earlier and selectively prevent invalid records from entering Iceberg tables.
How it connects
Connects schema compatibility, schema evolution, data quality, strong typing, Iceberg, and controlled casting.

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This explainer was generated by Manning's knowledge graph from the text of Architecting an Apache Iceberg Lakehouse. The section links point to the parts of the book it draws on.