Synapse Spark Implementation​

Business Problem

Largest IT organization wanted to reduce the latency when data was made available to customers​

Current system processed data in 4-hour batches and data was not available to customer for 24 hours​

Data had to go through several layers after integration before being made available to customer​

They were seeking to develop a solution in Azure Synapse that handles concurrent customer requests and reduce the lag time in data availability.


Separate pipelines for serving data while concurrently refreshing view when new batches arrive​

New batch of data is loaded into memory and indexed in the background​

Creating a view post data pre-processing. New requests under process to be completed and the new view is context-switched in​

Hyperspace Indexing to reduce query time on data​

  • Open-source indexing on Spark developed by client.​
  • Reduced query response time by half.​


Data is loaded in the background without interrupting service to customers​

Seamless context-switching to updated data when pre-processing and indexing are completed​

Reduced complexity, saved money, and made it easier for development​

Handled large amounts of incoming data​

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