Perspective of Big Data from people who do high frequency electronic trading (HFT)

There’s limited potential for improvements in throughput of high performance transactional databases. Financial institutions are looking to Hadoop to supplement their application stack, but need to accept these cultural differences.

  • Data quality is not 100%. Must use algorithms to refine on an ongoing basis during the transaction. Otherwise look for use cases where close is close enough. For example, Spotify uses Hadoop (HortonWorks) to select song recommendations. You probably wouldn’t use Big Data results to make decisions on which stocks to trade, at what second, at what price to trade them.
  • Batch will never be real time. Some users are able to get algorithms to complete in hours rather than days, but even if the hours can be reduced to some number of minutes, the evolution of Hadoop does not seem to be approaching real time.

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