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azure data factory

Azure Data Factory

Simplifying Hybrid Data Integration at Enterprise Scale with Azure Data Factory

Azure Data Factory is a cloud-based data integration service that automates and orchestrates the movement and transformation of data. With this service, you can create and schedule data-driven workflows (known as pipelines) to ingest data from a variety of sources.

Moreover, Azure Data Factory enables you to process and transform data using advanced compute services, such as:

  • Azure HDInsight (Hadoop)
  • Spark
  • Azure Data Lake Analytics
  • Azure Machine Learning

As a result, organizations across industries can use Azure Data Factory for a wide range of applications, including:

  • Data engineering
  • Migrating on-premises SSIS packages to Azure
  • Operational data integration
  • Analytics
  • Ingesting data into data warehouses

How Does Azure Data Factory Work?

Pipelines in Azure Data Factory follow four main steps:

  1. Connect and Collect: Gather data from multiple sources.
  2. Transform and Enrich: Process and refine the data.
  3. Publish: Load the transformed data into its destination.
  4. Monitor: Track performance and ensure accuracy.

Key Concepts in Azure Data Factory

  • Pipeline: is a logical grouping of activities that performs a unit of work.
  • Activity: it represents processing step in a pipeline.
  • Linked Services: Connection information needed to access external data sources.
  • Datasets: Data structures within data stores, connected via linked services.
  • Integration runtimes: The action engine that connects linked services to activities.
  • Data flows: Graphical tools for managing data transformation logic.

What is a Data Lake?

A data lake is a crucial component of Azure Data Factory. Essentially, it is a highly scalable, distributed, parallel file system in the cloud. It is designed to handle both structured and unstructured data, working seamlessly with various analytics frameworks.

With Azure Data Factory, you can build workflows that integrate data from a wide range of sources. These workflows transform the data to support your analytical objectives, enabling better decision-making and operational efficiency.