Datasets vs Inline Data in Azure Data Factory for Seamless Data Integration

Introduction:

Azure Data Factory (ADF) is the game-changer in data integration, and its success lies in harnessing the potential of “datasets” and “inline data.” In this blog post, we’ll show you how to wield these tools like a pro, achieving seamless and efficient data integration to fuel your organization’s success. Join us on this data-driven journey as we explore real-life examples, insider tips, and the most sought-after techniques that top data professionals swear by.

In the context of Azure Data Factory (ADF) or Azure Synapse Analytics, both “dataset” and “inline” refer to different approaches for handling and manipulating data. Let’s break down the differences and advantages of each:

Dataset:

Definition:

A dataset in ADF or Synapse is a named view of the data that abstracts the physical format and location of the data. It represents the structure, schema, and location of the data, whether it’s in a file (like CSV, Parquet), a database table, or any other supported data source.

Advantages:

• Abstraction: Datasets abstract the underlying data storage, making it easier to switch between different data sources without changing your data transformation logic.