Скачать или смотреть Nested ForEach pipeline Implementation in Azure Data Factory

Azure Data Factory (ADF) is a cloud-based data integration service provided by Microsoft Azure. It enables users to create, schedule, and orchestrate data workflows across various data sources, making it an essential tool for data engineers and analysts to build efficient data pipelines in the cloud.

Key Features of Azure Data Factory:
Data Integration: ADF supports integrating data from a wide range of sources, including on-premises databases, cloud storage, SaaS applications, and more. It has over 90 built-in connectors for data ingestion from sources like SQL Server, Oracle, SAP, Amazon S3, Google BigQuery, etc.

ETL and ELT Capabilities: ADF allows you to perform both ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) operations. You can transform data using Data Flows within ADF or through external services like Azure Databricks, Azure HDInsight, or SQL.

Pipeline Orchestration: Pipelines are the core structure in ADF. They help organize and manage workflows that involve multiple tasks or transformations. You can control the flow of data from one activity to another, enabling complex workflows.

Data Flows: ADF’s data flows enable data transformation at scale. You can perform operations like aggregations, joins, and data cleansing within ADF without the need to write extensive code.

Triggers and Scheduling: ADF supports event-based and time-based triggers. You can schedule pipelines to run at specific intervals or trigger them based on events, such as the arrival of a new file in storage.

Monitoring and Management: ADF offers monitoring tools for tracking pipeline runs, diagnosing errors, and viewing metrics. This feature helps maintain the health of data workflows and makes it easy to troubleshoot issues.

Data Movement: With ADF, you can move large volumes of data in a secure, reliable, and performant way, across both on-premises and cloud environments.

Typical Use Cases:
Data Migration: Move data from on-premises to Azure or between Azure services.
Data Lake Ingestion: Load raw data from various sources into Azure Data Lake for processing and analysis.
ETL for Data Warehousing: Integrate and transform data from multiple sources and load it into Azure Synapse Analytics (formerly SQL Data Warehouse).
Hybrid Data Integration: Combine data from on-premises and cloud sources for hybrid applications.
Big Data Processing: Use ADF with services like Azure Databricks or HDInsight for big data processing workflows.
Learning Tips:
Start with Basics: Familiarize yourself with the basic concepts of ADF, such as Pipelines, Datasets, Linked Services, and Activities.
Use the Azure Portal: The Azure portal provides an interactive experience to build and monitor pipelines visually.
Experiment with Data Flows: Data Flows allow you to transform data without extensive coding, so experiment with different transformations.
Practice with Real Data: Try building end-to-end pipelines with real or sample data to understand how to manage and schedule workflows effectively.
Learn via Tutorials and Videos: Microsoft's documentation, YouTube tutorials, and courses on platforms like Udacity, Coursera, and Pluralsight can be helpful.
Explore Best Practices: Learning best practices for performance, security, and cost management in ADF will improve your skills over time.