Declarative file data ingestion experience in Fabric Materialized Lake Views

Data Engineering · Public preview · Planned

Data Engineering Public preview Planned
Last Modified: July 30, 2026 Release Date: Q3 2026

Description

Fabric data engineers today write 80-100+ lines of boilerplate PySpark to ingest CSV and Parquet files into Delta tables - handling file discovery, schema inference, incremental refresh, schema drift, and error recovery manually. This feature introduces a simple Spark SQL DDL surface (CREATE MATERIALIZED LAKE VIEW ... FROM OneLake_Files OPTIONS (...)) that declaratively handles all of this. It includes automatic schema evolution (or strict fixed-schema mode), three refresh modes (append_only, mirror, full), built-in error handling, structured user telemetry on files and lakehouse-level DAG lineage.

📰 Related Blog Article

Mastering Declarative Data Transformations with Materialized Lake Views
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Change History

  1. 2026-07-30
    Release Date 2026-06-30 -> 2026-09-30
  2. 2026-06-16
    Restored to Roadmap
  3. 2026-06-16
    Removed from Roadmap
  4. 2026-06-02
    Roadmap Item Added
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