Processing and analyzing data streams to assess
usage patterns and wifi strength across airports.
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About The Company

Boingo is one of the leading Wi-Fi company in the world, and the largest indoor DAS (cellular) provider in the United States. Boingo has grown from a tiny LA start-up into the world’s leading small cell company, publicly traded on NASDAQ (WIFI). Boingo is a market leader in technologies that fuel todays connected lifestyle. Their Wi-Fi and cellular networks reach more than a billion people each year – at airports like LAX, JFK, London and Dubai, in stadiums and universities like Soldier Field and the University of Arizona, throughout complexes like the World Trade Center, and on military bases worldwide.


  • Data loss minimised and monitored.
  • Data retention maintained to control loss of logs.
  • Streamlined process for logs and data that can be stored in a structured model.
  • Making querying faster for all the data.
  • Giving the Boingo business clean data pipelines to create direct insignt into customer usage patterns thereby improving customer accessibility and quality of service.
  • Business Challenges

  • Boingo wanted to analyse data from multiple sources to understand usage patterns, hotspot strengths and customer movements across the airport.
  • A solution to define and stream data from multiple sources without any drops.
  • Capability to read the data, process, apply a schema, store and map it for further analysis
  • Create a structured database, parse data and extract key values while saving it to an Impala table.
  • Key Technologies

    The Solution

    Clairvoyant installed and provisioned a 20 node Cloudera CDH cluster. We implementation of proof-of-concept solution to demo model. A Data Pipeline to inject incrementally aggregated data was developed. The Clairvoyant team created and executed data ingestion, restatement and backfill processes while developing an intuitive visualization dashboard in Zoomdata for Bidtellect to be able to track their data.

    The Impact

    Reduction in data loss
    Faster querying speed
    Representation of required logs


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