Design Patterns for Efficient Data Pipelines

Design patterns are an effective way to build scalable and efficient data pipelines in Azure Data Factory (ADF). A design pattern is a reusable solution that can be applied to common problems in data pipeline development. Here, we’ll discuss some of the design patterns that can be used in ADF for efficient data pipeline development.

Note: Microsoft's official documentation provides a detailed look into design patterns possible for implementation in Azure Data Factory (ADF).

Design patterns

Design patterns in Azure Data Factory (ADF) refer to best practices or reusable solutions for solving specific problems commonly encountered in data integration workflows. These patterns can help developers design and build ADF pipelines that are scalable, efficient, maintainable, and cost-effective.

Fan-in/fan-out pattern

Fan-in/fan-out is a common design pattern used in software engineering and Azure Data Factory (ADF) for creating efficient and scalable data pipelines. The pattern is used to distribute workloads across multiple concurrent processing threads, maximizing the efficiency of the system. In ADF, fan-in/fan-out is often used to process large volumes of data in parallel by splitting the data into smaller chunks and then processing the chunks in parallel across multiple processing nodes. This can help to reduce processing times and improve overall system performance.

For example, let’s say we have a large dataset that needs to be processed by ADF. Instead of processing the entire dataset on a single node, we can split the dataset into smaller chunks and process each chunk in parallel across multiple processing nodes.

Here is an example of how the fan-in/fan-out pattern can be implemented in ADF:

1. First, we define the input dataset that needs to be processed and split it into smaller chunks using the Split activity.

2. We then use the ForEach activity to iterate over the smaller chunks and distribute the workloads to multiple parallel processing nodes.

3. Each processing node then performs its assigned task on the smaller dataset chunk, which could involve processing, filtering, or aggregating the data. ...