What is Copy job in Data Factory for Microsoft Fabric?

Copy Job is the go-to solution in Microsoft Fabric Data Factory for simplified data movement from many sources to many destinations — no pipelines required. With native support for multiple delivery styles, including bulk copy, incremental copy, and change data capture (CDC) replication, Copy job offers the flexibility to handle a wide range of data movement scenarios — all through an intuitive, easy-to-use experience. Whether you’re new to data integration or just want a faster way to get your data where it needs to go, Copy job offers a flexible and user-friendly solution.

Advantages

Some advantages of the Copy job over other data movement methods include:

• Easy to use: Set up and monitor data copying with a simple, guided experience — no technical expertise needed.
• Efficient: Copy only new or changed data from the last run to save time and resources, with minimal manual steps.
• Flexible: Choose which data to move, map columns, set how data is written, and schedule jobs to run once or regularly.
• High performance: Move large amounts of data quickly and reliably, thanks to a serverless, scalable system.

You can also visit the data movement strategy to see how Copy job compares with Mirroring and copy activity in pipelines.

Concepts

Copy modes (Full copy, Incremental copy)

You can choose how your data is copied from source to destination:

• Full copy: Every time the job runs, it copies all data from your source to your destination.
• Incremental copy: The first run copies everything, and subsequent runs only move new or changed data since the last run.

Incremental copy (CDC, Watermark)

In incremental copy, every run after the initial full copy (called a "subsequent load") transfers only certain changes. Copy job automatically tracks and manages the state of the last successful run, so it knows what data to copy next.

• When Copy job copies from a database using an incremental column (“watermark column”), each subsequent load copies only rows with a value in that column larger than any row previously copied.
• When Copy job copies from a database that has CDC enabled, each subsequent load copies all rows inserted, updated, or deleted since the last successful run.
• When Copy job copies files, each subsequent load copies only those files created or modified since the last successful run.

Copy job supports the following watermark column types for incremental copy from a database:

• ROWVERSION: A binary column that automatically changes whenever a row is modified. It’s ideal for SQL-based systems with high-throughput transactional workloads, because every insert or update is captured reliably without depending on application-managed timestamps.
• Datetime: Datetime columns such as LastUpdatedDatetime or ModifiedAt that store both date and time. Copy job uses the precise timestamp to track incremental progress across runs. Datetime is preferred when your source tracks changes with high-frequency precision.
• Date: Date-only columns such as LastUpdatedDate. Because date values don’t include a time component, Copy job automatically applies delayed extraction from the last day to ensure there’s no data loss or overlap between runs, safely managing incremental windows. Date is suitable for daily batch processes.
• String (interpreted as datetime): String columns whose values can be interpreted as datetime. This lets you use incremental copy even when timestamps are stored as strings, with no need to cast or transform columns or make schema changes in the source.
• Integer: An increasing number that tracks row changes.

If your database has CDC enabled, you don’t need to choose an incremental column — Copy job automatically detects the changes.

If a copy job fails, you don’t need to worry about data loss. Copy job always resumes from the end of the last successful run. A failure does not change the state managed by Copy job.

See more details for Change data capture (CDC) in Copy Job.

Reset incremental copy

You have the flexibility in managing incremental copy, including the ability to reset it back to a full copy on the next run. This is incredibly useful when there’s a data discrepancy between your source and destination—you can simply let Copy Job perform a full copy in the next run to resolve the issue, then continue with incremental updates afterward.

You can reset incremental copy either per entire job or per table, giving you fine-grained control. For example, you can re-copy smaller tables without impacting larger ones. This means smarter troubleshooting, less disruption, and more efficient data movement.

In some cases, when you edit a copy job — for example, updating the incremental column in your source table — Copy job will reset the incremental copy to a full copy on the next run. This ensures data consistency between the source and the destination.

Update methods (Append, Overwrite, Merge, SCD Type 2)

You can also decide how data is written to your destination:

By default, Copy job appends new data, so you keep a full history. If you prefer, you can choose to merge (update existing rows using a key column), overwrite (replace existing data), or SCD Type 2 (preserve change history with effective dating). If you select merge or SCD Type 2, Copy job uses the primary key by default, if one exists.

• When copying to a database: New rows are added to your tables. For supported databases, you can also choose to merge, overwrite, or use SCD Type 2 for existing data.
• When copying to storage: New data is saved as new files. If a file with the same name already exists, it's replaced.

When performing an incremental copy from the source and merging into the destination, rows from the source are inserted or updated in the destination. When performing CDC replication from the source and merging into the destination, rows from the source are inserted, updated, or deleted in the destination. When using SCD Type 2 with CDC replication, changes are preserved as versioned rows with effective dating, and deletes are handled as soft deletes.

See more details for SCD Type 2 in CDC Copy job.

Automatic table creation and truncation on destination

Copy job can automatically create tables in the destination if they don’t already exist. If the destination tables are already available, you can simply select them as your target. With flexible column mapping options, you can easily define how to map schemas from the source tables to the destination tables.

You can also optionally truncate destination data before the full load, ensuring their source and destination are fully synchronized without duplicates.

By default, Copy job does not delete any data in destination. When you enable this option:

• The first run of incremental copy will truncate all data in the destination before loading the full dataset.
• Subsequent incremental copies will continue to append or merge data without affecting existing records.
• If customers later reset incremental copy to full copy, enabling this option will again clear the destination before loading.

This approach ensures that your destination remains clean, fully synchronized, and free of duplicates, providing a reliable foundation for their data ingestion solution.

Connector	Automatic table creation	Truncate destination before full load
Azure SQL DB
Azure SQL Managed Instance
Azure Synapse SQL Pool
Fabric Lakehouse table
Fabric Warehouse
On-premises SQL Server
Oracle
Snowflake
SQL database in Fabric (Preview)

Audit columns

Audit columns are additional metadata columns that Copy job can automatically append to every row it writes to the destination. When you enable audit columns, each row in your destination table can be enriched with information such as:

• Data extraction time
• Source file path
• Workspace ID, Copy job ID, Copy job run ID, and Copy job name
• Incremental window lower bound and upper bound
• Custom user-defined values

With audit columns, you get row-level data lineage without custom code, enabling compliance reporting, data quality debugging, and ingestion freshness tracking.

See more details in Audit columns in Copy job.

Performance

Copy job automatically optimizes copy performance based on the data volume, so you get fast data movement without manual tuning. Whether you're copying a small lookup table or a large transaction log, Copy job applies the right strategy for each table automatically.

When copying data from large tables, you can also optionally enable auto-partitioning (Preview). With auto-partitioning, Copy job analyzes the source schema and data characteristics to determine the optimal partitioning strategy. It automatically selects the right partition column, computes balanced boundaries, and executes parallel reads — all without any user input. This can dramatically increase throughput for large datasets. You can turn on the auto-partitioning toggle under Advanced settings in your Copy job.

Auto-partitioning is supported for watermark-based incremental copy including both initial full copy and incremental copy, on the following connectors: Amazon RDS for SQL Server, Azure SQL Database, Azure Synapse Analytics (SQL Pool), Fabric Data Warehouse, SQL database in Fabric, SQL Server, and Azure SQL Managed Instance.

Run options (Run, Schedule, Event Trigger)

You have full flexibility to decide when a copy job runs — it can run once or on a schedule. Even if a job is scheduled, you can still select Run at any time to trigger it manually. In incremental copy, the manually triggered job will still only transfer changes since the last run.

With support for multiple schedules in copy job, you gain even greater control. A single copy job can have multiple schedules—for example, one running daily at 6 AM and another running weekly on Sundays. All schedules can be managed directly within the same copy job, making orchestration simpler, cleaner, and more efficient.

If you use the copy job activity in a pipeline, you can also take advantage of the pipeline’s orchestration and trigger capabilities. For example, you can use event triggers to start a copy job activity when specific events occur, such as new files arriving in a data lake or changes in a database.

See more details for copy job activity.

Hosting options (Virtual network, On-premises, Cloud)

You can use Copy job to move data from any source to any destination, whether your data is on-premises, in the cloud, or within a virtual network. On the connection page of Copy job, you can choose from multiple host options, including an on-premises gateway or a virtual network gateway, to securely access data behind a firewall or within a virtual network.

See more details to Secure your data movement with Copy Job and Virtual Network Data Gateway.

Operationalization（GIT/CICD, Variable library）

You can use source control, continuous integration, continuous deployment, and a collaborative environment to run successful data analytics projects with Copy job.

Additionally, with Variable library support, you can parameterize connections in Copy Job. This powerful capability streamlines CI/CD by externalizing connection values, enabling you to deploy the same Copy Job across multiple environments while the Variable library injects the correct connection for each stage.

See more details in CI/CD for Copy job.

Observability

See more details in How to monitor a Copy job and Workspace monitoring for Copy job

Region availability

Copy job has the same regional availability as Fabric.

Pricing

You can get the details in pricing Copy job.

Supported connectors

With Copy job, you can move your data between cloud data stores or from on-premises sources that are behind a firewall or inside a virtual network using a gateway.

See our supported connectors page for the full list of supported sources and destinations:

• Sources and destinations
• Change data capture (CDC) replication (Preview)

Submit your feedback on Fabric Ideas and join the conversation on the Fabric Community.

Data type mapping

Copy activity in pipelines and Copy job perform source types to destination types mapping with the following flow:

1. Convert from source native data types to interim data types used by Fabric Data Factory.
2. Automatically convert interim data type as needed to match corresponding destination types.
3. Convert from interim data types to destination native data types.

Copy activity in pipelines and Copy job currently support the following interim data types: Boolean, Byte, Byte array, Datetime, DatetimeOffset, Decimal, Double, GUID, Int16, Int32, Int64, SByte, Single, String, Timespan, UInt16, UInt32, and UInt64.

The following data type conversions are supported between the interim types from source to destination.

(1) Date/Time includes DateTime, DateTimeOffset, Date and Time.

(2) Float-point includes Single and Double.

(3) Integer includes SByte, Byte, Int16, UInt16, Int32, UInt32, Int64, and UInt64.

To learn the detailed data type conversions for a particular connector, go to the copy activity configuration article for that connector from here.

Related content

• How to create a Copy job
• How to monitor a Copy job
• Audit columns in Copy job