Azure Data Factory Overview

Overview

This lecture provides a comprehensive, hands-on guide to mastering Azure Data Factory (ADF), an essential cloud ETL/ELT tool for data engineers. It covers setup, core concepts, real-world pipelines, triggers, transformations, and best practices for building automated, production-ready data workflows.

Introduction to Azure Data Factory

• Azure Data Factory (ADF) is a cloud ETL/ELT tool for extracting, loading, and transforming data across diverse sources and destinations.
• ADF is foundational for Azure-based data engineering, used in Synapse Analytics and Microsoft Fabric.
• Learning ADF is crucial due to industry demand and its centrality in data migration, orchestration, and automation.

Setting Up Azure Resources

• Prerequisites: PC/laptop, stable internet, and a free Azure account (with credit).
• Resources needed: Resource Group (container for resources), Storage Account (for data lake).
• Data Lake is configured within Storage Account using hierarchical namespace (for folders/subfolders).
• Understand redundancy options: LRS (cheapest, single datacenter), ZRS (zone-redundant), GRS, and GZRS (geo-redundant).

Key Concepts: Linked Services and Datasets

• Linked Service: Connection information to data sources/destinations (e.g., SQL, Blob, API).
• Dataset: Schema or file/table/view specification within a linked service.

Core Activities and Pipelines

• Copy Activity: Copies data from a source to a destination, essential for almost all data movement in ADF.
• Pipelines organize and orchestrate sets of activities.
• Supports moving data from Azure Data Lake, APIs, HTTP sources, and more.
• Dataset parameterization allows dynamic file selection during iteration.

Real-World Pipeline Scenarios

• Build pipelines to move data between containers and from APIs/GitHub directly into Data Lake.
• Use Get Metadata activity to retrieve folder contents and iterate files.
• Implement ForEach and If Condition activities to process only files matching specific patterns (e.g., those starting with 'fact').
• Parameterized datasets enable dynamic data processing within loops.

Data Transformation with Data Flows

• Data Flows provide GUI-based, code-free transformations powered by Spark clusters.
• Common transformations: Select columns, Filter rows, Conditional Split, Derived Column, Group By, and Aggregates.
• Sync activity writes transformed data back to Azure Data Lake or other sinks.

Automation with Triggers

• Schedule Trigger: Runs pipelines at preset intervals (cannot backdate).
• Tumbling Window Trigger: Supports historical intervals and backfilling.
• Storage Events Trigger: Automatically triggers pipelines when a file is uploaded to a specified path.

Advanced Orchestration and Variables

• Use Set Variable activity to store dynamic values (e.g., file lists, IDs) between steps.
• Parent pipelines can execute multiple child pipelines using the Execute Pipeline activity.
• Integrate all components to form a fully automated, event-driven ETL solution.

Key Terms & Definitions

• ETL/ELT — Extract, Transform, Load/Extract, Load, Transform: data integration processes.
• Resource Group — Logical container in Azure for related resources.
• Storage Account — Azure service to store blobs (files), tables, and more.
• Blob Storage — Object storage for unstructured data in Azure.
• Data Lake — Storage with hierarchical namespace for big data analytics.
• Linked Service — Connection to external data source/destination.
• Dataset — Data structure representing data to move or transform.
• Pipeline — Set of activities in ADF to perform a workflow.
• Copy Activity — ADF activity to move data from source to destination.
• Get Metadata Activity — Retrieves information (e.g., file names) from a dataset.
• ForEach Activity — Loops over a collection of items in a pipeline.
• If Condition Activity — Branches pipeline logic based on conditions.
• Data Flow — Visually designed, scalable transform logic in ADF.
• Trigger — Mechanism to automate pipeline execution.
• Set Variable Activity — Stores and passes values between activities.
• Execute Pipeline Activity — Runs one pipeline from another.
• Sync — Destination in copy or data flow activity.

Action Items / Next Steps

• Create a free Azure account and set up the required Resource Group and Storage Account.
• Practice building pipelines with copy, metadata, and iteration/condition activities.
• Experiment with Data Flows to perform transformations.
• Set up schedule and storage event triggers for your workflows.
• Review and reimplement complex sections (e.g., file iteration, dynamic datasets) for mastery.
• Consider watching additional videos on related topics (e.g., PySpark, Delta Lake) to further expand your skills.