Choosing the Right Data Ingestion Method with Microsoft Fabric

Microsoft Fabric provides various options for data ingestion, each with its own strengths and use cases. Understanding which option to use and when can significantly impact your data workflow efficiency. In this video, we'll explore the different data ingestion methods offered by Microsoft Fabric—Dataflow (with and without Fast Copy), Data Pipeline, and Notebooks—and discuss their pros and cons.

Dataflow:

• Fast Copy is not effective with lower volumes of data.

• However, with larger volumes of data, using Fast Copy is recommended.

• Dataflow is the quickest to market but also the most expensive and least performant option.

Data Pipeline:

• Offers low cost and great performance.

• Power User and Developer friendly.

Notebooks:

• Offers low cost and great performance.

• Developer-friendly experience.

• High time to market.

• Notebooks can be used not only for data ingestion but also for tasks like Data Science and Data Engineering.

In order to provide a benchmark for comparison, I conducted tests by copying data from Azure SQL to Lakehouse with no transformations. I compared data ingestion for two different volumes of data—approximately 2 million rows and approximately 70 million rows—using two different SKUs: F64 and F4.

For a detailed analysis and further insights, check out the video.

With this information, you'll be better equipped to choose the right data ingestion method for your specific needs and optimize your data workflow with Microsoft Fabric.