Azure Data Factory
Learn to orchestrate and automate data workflows using Azure Data Factory for efficient cloud-based ETL and data integration.
6 Months
Online & Offline
4.8
1000+Course Modules
Introduction to Azure & Data Factory
Microsoft Azure is a cloud platform offering 200+ services for building and managing applications, including Azure Data Factory for cloud-based ETL/ELT data integration and automation.
ADF Architecture and Components
Azure Data Factory uses a layered architecture separating control and data planes for scalable, secure data integration.
Creating Your First Pipeline
Creating your first pipeline involves building and running a workflow to move and transform data in Azure Data Factory.
Linked Services in ADF
Linked Services in Azure Data Factory define secure connections that link ADF to external data sources and services.
Datasets in ADF
Datasets in Azure Data Factory define the structure, format, and location of data used by activities.
Activities in Pipelines
Copy Activity in Azure Data Factory moves data between source and target stores.
Copy Activity in Detail
Copy Activity in Azure Data Factory facilitates efficient data movement and transformation between diverse sources and destinations, ensuring reliable and scalable data integration.
Mapping Data Flows - Basics
Mapping Data Flows in Azure Data Factory offer a code-free, visual interface to transform data using Spark clusters.
Mapping Data Flows - Advanced
Mapping Data Flows - Advanced in Azure Data Factory allows sophisticated data transformations using a drag-and-drop visual interface.
Control Flow Activities in ADF
Control Flow Activities in Azure Data Factory manage the execution order and logic of pipeline tasks, including loops, conditions, and triggers.
Working with Triggers
Triggers in Azure Data Factory automate pipeline execution based on schedules, events, or dependencies.
Parameters and Expressions in ADF
Parameters and Expressions in Azure Data Factory enable dynamic pipeline behavior and flexible data handling.
Debugging and Monitoring Pipelines
Debugging and Monitoring in Azure Data Factory help test, track, and manage pipeline execution and performance.
Azure Integration Runtime (IR)
Azure Integration Runtime (IR) in ADF provides the compute environment for data movement, transformation, and activity execution.
Incremental Load in ADF
Incremental Load in Azure Data Factory transfers only new or changed data, optimizing performance and efficiency.
Using Azure Functions and REST APIs
Using Azure Functions and REST APIs in ADF enables custom logic and external service integration within pipelines.
Git Integration with ADF
Git Integration in Azure Data Factory enables version control, collaboration, and source management for pipelines and data flows.
CI/CD in ADF using Azure DevOps
CI/CD in Azure Data Factory using Azure DevOps automates pipeline deployment and ensures consistent, reliable updates across environments.
Security and Key Vault Integration
Security and Key Vault Integration in Azure Data Factory protect sensitive data by securely managing credentials, secrets, and connection information.
End-to-End Project & Recap
Build a complete data pipeline in Azure Data Factory, covering data ingestion, transformation, and storage.
Course Overview
Master Azure Data Factory to design, schedule, and manage scalable ETL pipelines for moving and transforming data across sources. Learn to integrate databases, cloud storage, and SaaS applications, ensuring seamless data flow and transformation. Gain hands-on experience in monitoring, debugging, and optimizing data pipelines, preparing you for real-world cloud data engineering projects.
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