Azure Data Factory

Azure Data Factory (ADF) is a cloud-based data integration service that enables the creation of data-driven workflows for orchestrating and automating the movement and transformation of data. It serves as an efficient ETL (Extract, Transform, Load) tool in the cloud.

ETL Process in Azure Data Factory

The ETL process in Azure Data Factory generally involves four key steps:

1. Connect & Collect:
   • Utilize the copy activity in a data pipeline to move data from both on-premises and cloud source data stores.
2. Transform:
   • Once data is centralized in the cloud, you can process or transform it using compute services such as HDInsight, Spark, Data Lake Analytics, and Azure Machine Learning.
3. Publish:
   • Refined raw data is loaded into Azure Data Warehouse, Azure SQL Database, Azure Cosmos DB, or other destinations.
4. Monitor:
   • Azure Data Factory provides built-in support for pipeline monitoring via Azure Monitor, APIs, PowerShell, Log Analytics, and health panels on the Azure portal.

Components of Azure Data Factory

Azure Data Factory comprises four key elements that work together to facilitate data-driven workflows:

• Pipeline:
  • A logical grouping of activities that perform a unit of work. For instance, a pipeline can ingest data from an Azure Blob Storage and execute a Hive query on an HDInsight cluster to partition the data.
• Activity:
  • Represents a processing step within a pipeline. For example, a copy activity can transfer data from one data store to another.
• Datasets:
  • Define data structures within data stores, referencing the data used in activities as input/output.
• Linked Services:
  • Function like connection strings, containing the necessary connection information for Data Factory to connect to external resources, including data stores and compute resources.
• Triggers:
  • Control when a pipeline execution should occur. Triggers can be scheduled to perform activities at specific times or can disable certain activities.
• Control Flow:
  • Orchestrates pipeline activities, allowing you to chain activities in a sequence, branch workflows, define parameters at the pipeline level, and pass arguments when invoking the pipeline on-demand or via a trigger.

Creating Azure Data Factory Using the Azure Portal

Follow these steps to create an Azure Data Factory instance:

1. Step 1: Click on Create a Resource and search for Data Factory, then select Create.
2. Step 2: Provide a name for your Data Factory, select the appropriate resource group, and choose the location for deployment. Select the desired version (V2 is recommended).
3. Step 3: After filling in all the required details, click on Create to initiate the creation process.
4. Step 4: Once created, Azure Data Factory will have its own portal for managing pipelines, datasets, and activities.