Hyper-Scale Data Processing and Storage - StreamAnalytix Case Study

Case Study

Hyper-Scale Data Processing and Storage Using StreamAnalytix

Enterprises generally need to compromise with running and maintaining multiple batch processes on the accumulated data due to throughput and management constraints. The overall business process turnaround can be improved if the data is made available after processing in real-time. A real-time system of such a large scale requires easy provisioning and monitoring.


We developed a Proof of Concept (PoC) for financial service companies who face challenges in processing/storing large amounts of streaming data in real-time. The use case was applied to stock ticker data being streamed from over 80 different global exchanges and various sources with the following goals:

  • Receive simulated tick data from 80+ stock exchanges across the globe
  • High data ingestion rate of the incoming ticks
  • Real-time analytics on high volumes of data
  • Easy installation, configuration, and monitoring of required infrastructure
  • Real-time dashboard showing key statistics of all components and ingestion rates


The solution of the Stock Ticker PoC was divided in six segments:

  • Data generator: A multi-threaded process that can generate mocked-up stock tick data from multiple exchanges around the globe, with flexibility for increasing/decreasing data velocity
  • Real-time data processing and storage: A parallel processing engine, which can process high volumes of stock tick data and publish it to the UI in real-time with high data processing rate
  • Data store: A data store that stores and makes available huge volume of incoming stock tick data in real-time
  • Reporting on historical data: A reporting tool that can generate reports on the historical data, which can be viewed on the UI for analysis and decision-making
  • User Interface (UI): A UI that can handle huge real-time data and is capable of showing it as graphs in real-time
  • Provisioning and monitoring tool for cluster management


  • Real-time data processing on hyper scale and storage
  • Seamless provisioning, management, and monitoring of clusters
  • Visualization of real-time graphs and reports on historical data

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