Organizations today manage data across multiple storage systems, often in formats like CSV, Parquet, and JSON. While this data is readily available, turning it into analytics-ready tables typically requires building and maintaining complex ETL pipelines.
Shortcut transformations remove that complexity.
With Shortcut transformations, you can convert structured files referenced through OneLake shortcuts into Delta tables without building pipelines or writing code.
This release applies to structured file formats such as CSV, Parquet, and JSON. AI powered transformations are currently available in public preview as we continue to expand and refine these capabilities.
Why this matters
Preparing data for analytics has traditionally required building ingestion pipelines, managing compute jobs, and orchestrating refresh schedules. This introduces complexity, increases operational overhead, and slows down time to insight.Shortcut transformations change this model.
Instead of moving and transforming data through pipelines, you can reference data where it lives using OneLake shortcuts and let Fabric handle ingestion, transformation, and synchronization.
Fabric automatically converts files into Delta tables and keeps them continuously synchronized with source data. This ensures your data is always current and ready for analysis, without requiring pipeline orchestration.
The result is simpler architecture, reduced operational overhead, and a faster path from raw data to insights.
Key capabilities
Shortcut Transformations bring together a set of capabilities designed for modern data workflows.No pipelines or code required
Convert files into analytics‑ready Delta tables with a fully managed ingestion and sync experience.
Always in sync with source data
Shortcut transformations continuously detect changes and apply them incrementally, keeping tables current without requiring scheduled jobs.
Support for nested folder structures
Automatically detect and process files across hierarchical folders, ensuring changes are captured regardless of how data is organized.
Automatic schema handling
Fabric automatically infers schema and safely evolves tables as new columns appear, with built in support for semi structured data such as nested JSON.
Native Delta Lake output
All transformations produce Delta tables that are immediately available across Microsoft Fabric, including SQL, Spark, and Power BI. These tables support analytics, reporting, and AI workloads on a unified data foundation.
Improved cost efficiency
Eliminates always on pipelines and unnecessary compute. Transformations run only when changes are detected, minimizing compute and storage overhead.
Getting started
Create a new table shortcutIn your Lakehouse, select New Shortcut under the Tables section. For Lakehouses with schema, select New table Shortcut for a particular schema.
Connect to your data source
Choose from supported sources such as Azure Data Lake Storage, Azure Blob Storage, Amazon S3, Google Cloud Storage, Dataverse, SharePoint, OneDrive, and more.
Select files and configure the transformation
Browse to your data and configure how the data should be interpreted directly in the Fabric user experience, without writing code
- Define the delimiter (comma, semicolon, pipe, tab, etc.)
- Indicate whether the first row contains headers.
- Provide a friendly name for the table.
Screenshot_of_a_data_transformation_step_showing_options_to_convert_CSV_data_to
Figure: Auto‑transform applied during shortcut creation, converting CSV data to Delta.
Create the table
Fabric automatically transforms the selected files into a Delta table in your Lakehouse /Tables folder.