How to improve synapse notebook performance while storing data to blob

To improve the performance of your Azure Synapse notebook when storing data to Blob storage, consider the following strategies:

Optimize Data Retrieval and Storage

1. Batch Processing: Instead of processing data row by row, utilize batch processing techniques. This can significantly reduce the time taken for data insertion into databases and Blob storage. Use the COPY INTO command for bulk loading data from Blob storage into Synapse, which is optimized for performance.
2. Use Efficient Data Formats: When storing data in Blob storage, prefer using columnar formats like Parquet or ORC over CSV. These formats are optimized for performance and can lead to faster read and write operations.
3. Compression: Enable GZip or Snappy compression when storing files in blob storage. Compressed files reduce the amount of data transferred and can improve performance during read/write operations.
4. Parallel Processing: Leverage the parallel processing capabilities of Spark. Ensure that your spark jobs are configured to utilize multiple cores effectively. This can be done by adjusting the no. of partitions in your df to match the number of available cores in your spark pool.

Optimize Synapse Notebook Execution

1. Reduce Overhead: When running notebooks via Synapse pipelines, there may be additional overhead compared to manual execution. To mitigate this, ensure that your Spark pool is already warmed up and that you minimize any initialization code that runs every time the notebook is executed.
2. Optimize Code: Review your code for any inefficiencies. For example, avoid unnecessary transformations or actions that could slow down execution. Use caching for df when you need to reuse them multiple times within the same notebook.
3. Use Data Flows: If applicable, consider using Synapse Data Flows for ETL processes instead of notebooks. Data Flows are designed for performance and can handle large datasets more efficiently.
4. Monitor and Adjust Resources: Regularly monitor the performance of your spark pool and adjust the resources (ex., increase the no. of nodes) based on the workload requirements. This can help ensure that your jobs run efficiently without resource contention.

By implementing these strategies, you should see an improvement in the execution time of your synapse notebooks when working with blob storage.

Please 'Upvote'(Thumbs-up) and 'Accept' as an answer if the reply was helpful. This will benefit other community members who face the same issue.