{"active":true,"blog_title":"File Data Ingestion with Materialized Lake Views in Microsoft Fabric (Preview)","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/File-Data-Ingestion-with-Materialized-Lake-Views-in-Microsoft/ba-p/5367801","feature_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.","feature_name":"Declarative file data ingestion experience in Fabric Materialized Lake Views","last_modified":"2026-07-30","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q3 2026","release_item_id":"14254506-0f3f-f111-88b5-6045bd00f798","release_status":"Planned","release_type":"Public preview"}