If you haven’t already, check out Arun Ulag’s hero blog “FabCon and SQLCon 2026: Unifying databases and Fabric on a single, complete platform” for a complete look at all of our FabCon and SQLCon announcements across both Fabric and our database offerings.
Introduction
In today’s data landscape, success depends not just on powerful analytics, but on how seamlessly and reliably you can move data across systems.With Fabric Data Factory, Microsoft continues to deliver best-in-class connectivity for enterprise-grade data movement. The latest enhancements to Copy job and Pipeline bring expanded connector coverage and native incremental capabilities, making it easier than ever to ingest, synchronize, and operationalize data at scale.
What's new
Data movement in Fabric must support a wide range of patterns from simple bulk copy to orchestrated, enterprise-scale pipelines. With the newest releases, we’re expanding support across both Copy job and Pipeline to ensure customers can connect wherever their data lives.SharePoint Online File Connector (Source & Destination) (General Available)
SharePoint Online File connector is consistently one of the tops requests from customers. This connector supports both source and destination in Copy job and Pipeline.This enables seamless:
- Ingestion of files from SharePoint Online into Fabric Lakehouse or other supported destinations,
- Publishing data or generated outputs back to SharePoint Online,
- Automated orchestration of SharePoint-based workflows within pipelines,
Google BigQuery Connector (Preview)
With destination support added to Google BigQuery connector, Fabric Data Factory now supports outbound data movement into Google BigQuery.This enables:
- Cross-cloud data publishing
- Hybrid analytics architectures
- Federated data platform strategies
- Scheduled or event-driven synchronization through Pipelines
Screenshot_of_Google_BiqQuery_connector_as_destination
Figure: Screenshot of Google BiqQuery connector as destination.
MySQL Connector (Preview)
Destination capability is also available for MySQL, allowing Fabric Data Factory to write data back into MySQL systems. This enables bi-directional data movement scenarios.
Figure Screenshot of MySQL connector as destination.
PostgreSQL Connector (Preview)
Similarly, destination support for PostgreSQL enables flexible bi-directional movement between Fabric and PostgreSQL environments.
Figure: Screenshot of PostgreSQL connector as destination.
SAP Datasphere Connector (Generally Available)
The new connector for SAP Datasphere enables sourcing data directly from SAP’s modern data warehouse cloud solution.Capabilities with Copy job:
- Effortlessly move SAP data—bulk, incremental, or CDC—to any destination across clouds, with flexible table and column mapping for total control.
- Rely on enterprise-grade security and compliance, leveraging VNet gateways and robust authentication.
- Unify SAP data with other sources for holistic analytics and AI, orchestrating end-to-end data movement and transformations into a single pipeline.
- Seamless extraction from SAP-managed business data models.
- Streamlined ingestion into Fabric for unified analytics.
- Reduced friction between SAP estates and Microsoft Fabric.
Screenshot_of_SAP_Datasphere_connector_in_Copy_job
Figure: Screenshot of SAP Datasphere connector in Copy job.
These additions strengthen Fabric Data Factory’s role in supporting multi-cloud modern data architectures.
Native incremental copy: more efficient data movement with simplicity
Beyond expanding connector coverage, we’ve significantly enhanced native incremental copy capabilities in Copy job with no-code experience.
Incremental copy allows you to move only newly added or updated records and thus reduce load on source systems as well as improve the data movement efficiency and minimize the costs.
With these enhancements, Fabric Data Factory empowers customers to implement scalable CDC-style patterns and high-efficiency data synchronization without complex custom logic.
The following connectors are newly added to support native incremental copy in Copy job:
- Amazon RDS for SQL Server
- Amazon RDS for Oracle
- Azure Data Explorer
- Azure Files
- Google Cloud Storage
- IBM Db2
- ODBC
- Fabric Lakehouse tables / files
- SharePoint Lists
- SharePoint Online Files
Auto-Partitioning: Faster Large-Table Loads
When you move a table with millions of rows, the difference between a single-threaded read and a partitioned parallel read can be the difference between hours and minutes. Partitioning splits a large dataset into smaller chunks that can be read and written concurrently, dramatically increasing throughput.The challenge is that partitioning has traditionally been a manual exercise including identifying and configuring partition logic and tuning it to perform the best all by users. In many times this is not a one-time effort as it requires re-tuning when the data changes.
Copy job now handles all of this automatically. When Copy job detects a large dataset, it intelligently analyzes the source schema and data characteristics to determine the optimal partitioning strategy — selecting the right partition column, computing balanced boundaries, and executing parallel reads — all without any user input.
What this means:
- No partition configuration — You don't specify columns, ranges, or parallelism. Copy job analyzes the source and makes the optimal decision.
- Adaptive throughput — The partitioning strategy scales with the data. Larger tables get more partitions; smaller tables proceed without partitioning overhead.
- Consistent performance across tables — Whether you're copying a 100-row lookup table or a 500-million-row transaction log, Copy job applies the right strategy automatically.
- Amazon RDS for SQL Server
- Azure SQL Database
- Azure SQL Managed Instance.
- Azure Synapse Analytics
- Fabric Data Warehouse
- Fabric SQL database
- SQL Server