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How Bloomberg is using machine learning and data science to keep users hooked to its terminals

By Scott Carey | March 17, 2017
The task for the data scientists at Bloomberg is to make this ocean of information discoverable and relevant for its subscribers

Mann not only spends a lot of his time engaging with academia, either through publications, bringing in guest speakers on a monthly basis or Bloomberg's own faculty grant programme, but Bloomberg itself promotes attending conferences for its technical staff.

"We send a tremendous amount of people to academic conferences, with the main aim being for them to learn and to be challenged by the experience of what is happening in academia," he said. For example, Bloomberg registered for 44 staff members to attend the the Machine Learning Symposium in New York this month.

 

Open Source

In terms of tooling, Bloomberg has steadily shifted away from proprietary systems and vendors for data collection, processing and search to more open source solutions like Apache Spark and Solr.

Mann admits that moving from vendors and proprietary software was something of a culture shift.

"When people talk about free software they say 'it is not free like beer, it is free like puppies' because it needs a lot of love and care," he said, adding that the people at Bloomberg eventually saw the benefit of contributing to open source and the sense of control it brings.

"Open source has really changed the way that we do business," Mann said. "Traditionally we built most of our stuff from scratch, for example generations of database technology, which created speed and reliability constraints.

"With big data processing, over the past five to ten years the impact of Hadoop and now Spark has given us a whole new set of tools, and we are investing heavily in both of those. There was a time we were involved heavily with HBase but we are very aggressive with Spark right now. I don't know if we are an early adopter but we are certainly all in."

 

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