Microsoft Fabric Updates Blog

Announcing Public Preview: Copy Job in Microsoft Fabric

We are happy to announce the preview of Copy Job in Data Factory, elevating the data ingestion experience to a more streamlined and user-friendly process from any source to any destination. Now, copying your data is easier than ever before. Copy job supports various data delivery styles, including both batch copy and incremental copy, offering the flexibility to meet your specific needs.  

With Copy Job, you can enjoy the following benefits:

Simple: Seamless experience data copying with no compromises, making it easier than ever.

Data Factory in Fabric offers seamless data integration through over 100 built-in connectors, and it provides essential tools for data operations. Copy activity within Data pipeline manages data ingestion. Dataflow Gen 2 handles data transformation. Data pipeline orchestrates the entire integration process.

While copy activity embedded within Data pipeline handles data ingestion with bulk/batch operations, creating Data pipeline in Data Factory still proves to be a challenge for many users to copy data that are new to the field.

Copy Job, elevates the data ingestion experience to a more streamlined and user-friendly process from any source to any destination. With just a few clicks, you can create a Copy Job to move your data. Once the job is run, you’ll receive a comprehensive view of the data movement, ensuring seamless and transparent monitoring.

Efficiency: Enable incremental copying effortlessly, reducing manual intervention

With Copy Job, you can automatically capture changes in your data, drastically reducing manual oversight and keeping your information consistently up to date. This efficiency not only saves time but also enhances accuracy, letting you concentrate on more strategic tasks while the system handles incremental updates seamlessly.

This efficiency translates to less resource utilization and faster copy times. Which leads to less strain on your system and faster access to updated data, boosting overall performance and productivity.

Flexibility: Take full control of your data copying

In today’s fast-paced world, users crave simplicity but also want the power to control how their data is moved, without relying on a “black box” tool. With Copy Job, you get the best of both worlds—an intuitive experience with full customization. Choose which tables and columns to copy, map your data, and configure read/write behaviors to suit your specific needs. You can even set flexible schedules for one-time transfers or automated recurring copies. It’s all about empowering you to move data on your terms, with precision and ease.

High Performance:  Move Your PB-Scale Data

Copy Job, like copy activity, is designed for high-performance data movement, allowing you to efficiently transfer vast amounts of data across a wide range of sources and destinations. With it’s serverless architecture and parallelism at multiple levels, Copy Job maximizes data movement throughout, fully utilizing network bandwidth and data store IOPS for optimal performance. Whether handling bulk transfers or incremental updates, Copy Job ensures smooth, scalable performance delivering faster data transfers, reduced costs, and greater efficiency for all your data integration needs.

How to Get Started with Copy Job:

Ready to experience seamless data movement? Here’s how to get started with Copy Job in just a few easy steps:

Prerequisite for enabling the preview on your Microsoft Fabric tenant: Enable the Copy Job preview using admin portal or reach out to your Fabric admin.

1. Navigate to Data Factory: Head to the Data Factory homepage in Fabric and click the Copy Job icon.

2. Create Your Copy Job: Follow the creation wizard, after just a few clicks, your Copy Job will be ready.

3. Run and Monitor: Run your job to move your data and track its progress in real-time.

4. Manage with Ease: Just like any other artifact in Fabric, you can easily manage and configure your Copy Job as needed.

Resources:

What is the Copy job in Data Factory

How to create a Copy job

How to monitor a Copy job

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