Analytic Platform as a service in financial services

Summary

Providing advanced analytics capabilities as a service for multiple workloads across multiple lines of business. These would include post trade analytics to help with trade visibility, where the IT teams have to support brokerage functions (customer service, regulatory reporting, surveillance, advisory, prospecting) that need to interrogate deep histories of customer transactions across all assets and businesses. Other use cases in this space include tick analytics where data from real time feeds are streamed into SnappyData and the stream provides analytics (stream analytics and deep analytics) on windowed streams, which are then returned to the user. Market surveillance is another use case that is supported by APaaS. The primary goal of market surveillance is to detect patterns that point to insider trading and market manipulation by insiders. These use cases require more concurrency than is what is provided by traditional analytics systems. Data volumes in APaaS use cases will typically be in the 2-10TB range.

Challenge

  • Data ingestion from multiple sources
  • Higher concurrency requirement from multiple groups
  • Need for database class security
  • Latency requirements in seconds

Solution

SnappyData is being used to build out an APaaS at a large financial services bank to support multiple use cases with high concurrency and mixed workloads. Mixed workloads will typically involve a collection of aggregation class queries mixed in with point lookups and large scale distributed joins

Snappy Capabilities in Use

  • Optimized data ingestion
  • Use of both row and column tables
  • Stream processing
  • Mixed SQL querying
  • Persisting to HDFS
  • Data unification with Netezza and HBase/li>

The Spark Database

SnappyData is Spark 2.x compatible and open source. Download now