To optimize and improve the performance of your data ingestion pipelines in Azure Data Factory (ADF), you can follow these best practices:
Use Parallel Execution: Instead of running your pipeline sequentially, consider using parallel execution to process multiple tables simultaneously. This can significantly improve the overall performance. You can achieve parallel execution by using the "ForEach" activity in ADF and configuring it to process tables in parallel.
Use Incremental Loading Techniques: Incremental loading reduces the amount of data processed by only capturing and processing the changes since the last run. Since you're already using SQL Change Data Capture (CDC), make sure to leverage it effectively to fetch only the incremental data instead of processing the entire dataset. This can greatly reduce processing time and improve pipeline performance.
Optimize Batch Size: The default batch size in ADF is set to 20. However, you can experiment with different batch sizes to find the optimal value for your scenario. Increasing the batch size can reduce the number of round trips and improve throughput. Consider testing different batch sizes to find the most efficient one for your specific workload.
Utilize Data Compression: If your data allows for it, consider compressing the output data in Azure Blob storage. Compressed data takes up less storage space and reduces the time required for data transfer and processing. You can use compression techniques like GZip or Snappy to compress the JSON data before storing it in Azure Blob storage.
Monitor and Optimize Performance: Regularly monitor the performance of your pipeline using Azure Monitor and ADF pipeline monitoring tools. Identify any bottlenecks, long-running activities, or data processing issues. Optimize your pipeline accordingly based on the insights gathered from monitoring data.
Use Optimized Data Movement: When moving data between different sources and sinks, utilize optimized connectors and integration runtimes provided by ADF. For example, use the PolyBase feature for SQL Server or use the Azure Data Lake Storage connector for Blob storage to achieve faster data movement.
Implement Data Partitioning and Filtering: If possible, partition your data based on relevant columns during extraction, such as date or region. This allows for more efficient data processing, as you can filter and process smaller subsets of data at a time.
Provision Adequate Resources: Ensure that you have provisioned enough resources for your ADF pipeline. This includes the appropriate number of data integration units (DIUs) and integration runtime (IR) nodes based on your workload and data volume. Scaling up or out your ADF resources can improve performance and reduce processing time.
By following these best practices, you can optimize the performance of your data ingestion pipelines in Azure Data Factory and achieve faster data processing and transfer times.