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Case Study

Finra

Starburst gives FINRA a scalable, cost-effective way to analyze its constantly growing volumes of data

Address the challenges of massive data growth and increasing demand for efficient computing in the public sector

The Financial Industry Regulatory Authority (FINRA) carries out this mission by analyzing billions of daily trading events from financial institutions to detect fraud, insider trading, and abuse. To address the challenges of massive data growth and increasing demand for efficient computing, FINRA migrated its legacy data warehousing systems to an Amazon Web Services (AWS) data lake. When redesigning its data platform, FINRA chose to separate compute and storage and query its multi-PB AWS data lake using Starburst Enterprise, the world’s fastest distributed SQL query engine.

 

Scalability and Elasticity

With Starburst, there is no need to worry about data storage or compute resources, FINRA could now scale compute up and down as desired with no need to provision for peak usage anymore.

Accessibility

Starburst helped eliminate FINRA’s data silos, increasing time to insight and overall data accessibility in the cloud.

Performance

Query performance optimization with Starburst resulted in faster analytics as well as a significant reduction of Amazon Elastic Compute Cloud (Amazon EC2) costs.

Starburst separates compute and storage, making it possible to scale economically and analyze 25PB of data— 100B rows of new data per day from 25+ sources.

Ivan Black,
Director, FINRA

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Starburst Galaxy

Up to $500 in usage credits included

  • Query your data lake fast with Starburst's best-in-class MPP SQL query engine
  • Get up and running in less than 5 minutes
  • Easily deploy clusters in AWS, Azure and Google Cloud
For more deployment options:
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