The objective of this project is to copy data from one dataset (the source) to another dataset (the destination) using Azure Data Factory. This could involve moving data between storage services (like Azure Blob Storage, SQL Database, or on-premises databases) while ensuring reliability, monitoring, and handling errors during data movement.
This project demonstrates how to use Azure Data Factory (ADF) to copy data between datasets. Azure Data Factory is a cloud-based data integration service that allows you to create data-driven workflows for orchestrating data movement and transformation at scale.
This GitHub repository contains the necessary code and configurations to set up an ADF pipeline that copies data from a source dataset to a destination dataset.
This is a common task in data engineering, where data needs to be moved or transformed between different storage systems or formats.
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ETL (Extract, Transform, Load): This project demonstrates the Extract and Load parts of ETL, as data is extracted from a source (Blob Storage) and loaded into a destination (SQL database or another storage service).
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Azure Integration Runtime: Azure IR handles the data movement between cloud services, ensuring that data is securely transferred and that pipelines can scale.
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Linked Services and Datasets: Linked Services define the connection information, while Datasets point to the specific data structures being copied.
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Fault Tolerance and Error Handling: The project may involve configuring error handling to ensure that the pipeline continues running even in the face of minor issues, such as bad data.
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Triggers: This project may use time-based or event-based triggers to automate data transfers at specific intervals.