Optimizing Dataflow Performance

1. Optimizing Dataflow Performance

In this video, we’ll cover optimizing Dataflow Gen 2 performance using Staging, Fast Copy, and Default Destinations to make dataflows faster and more efficient!

2. Staging in Dataflows Gen2

In Dataflows Gen2, staging works like a temporary holding space for your data while it’s being transformed, helping everything run more smoothly and efficiently. The data is stored in staging artifacts, which are internal Lakehouses managed by Dataflows. These artifacts are handled automatically, so no need to manage them yourself! By default, staging is on for SQL endpoints to boost performance. But, for direct Lakehouse loading, it’s off to keep things speedy. You can re-enable it if needed. To clear data from the staging area, either disable staging to auto-clear in 30 days, or delete the dataflow or workspace for immediate removal.

3. Accelerating Data Ingestion with Fast Copy

Fast Copy is a feature in Dataflows Gen2 that helps you quickly ingest large datasets. When your data size gets big, it automatically scales up, letting you handle terabytes of data smoothly. The architecture shifts heavy tasks from the slower Power Query engine to the faster pipeline Copy Activity. This makes processing large data faster and more efficient. As you can see in the diagram, Fast Copy keeps things moving at top speed by smartly redistributing the workload. With Fast Copy, you minimize wait times and enjoy faster data ingestion for those bigger tasks!

4. Optimizing Fast Copy: Prerequisites and Key Settings

To use Fast Copy, you need to meet a few important prerequisites. For file data, the size must be at least 100 MB, and for databases like Azure SQL DB, you’ll need 5 million rows or more. Fast Copy works with specific connectors such as ADLS Gen2, Blob Storage, and SQL DB. Moreover, only a limited number of transformations are supported right now, like combining files and selecting columns. Keep in mind that for now, Lakehouse is the only direct destination option. If you need more complex transformations, or a different destination you’ll need to break queries into separate steps. The Require Fast Copy setting makes sure your dataflow either uses Fast Copy or stops if it can't, saving you from slow processing. It's also great for debugging, as it quickly flags issues, helping you avoid long refresh times.

5. Dataflow Gen2 Default Destination

Let’s explore Dataflows Gen2 Default Destinations, a feature that streamlines setting up dataflows for specific destinations like Lakehouses, Warehouses, or KQL Databases. Creating a Dataflow for these automatically configures the data destination with default settings, saving you time and simplifying development! The behaviors are preset: Lakehouses use the Replace update method with a Dynamic schema, while Warehouses and KQL Databases use the Append update method with a Fixed schema. These settings can’t be modified, so you always know how your data will be handled.

6. Let's practice!

The concepts are clear, now let's take the next step and dive into the practical side!

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