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Data Science for the Enterprises (Spark, Kafka, Mesos and Cassandra) - Andy Petrella, Kensu

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The event will feature Andy Petrella, Co-founder of Kensu (formerly Data Fellas), who will take us through a journey of how to use Scala for doing interactive programming, distributed programming and machine learning.

The talk will cover what Data Science applied to the Enterprise means, the challenges, the takeaway and the expected advantages.

In accord with the constraints imposed by real life businesses, we’ll have a demonstration of an environment focused on production readiness preserving the capacity to prototype, enabling deployment and favouring continuity.

We’ll close the debate with a note about the next challenges: how to ensure the maintainability, the data governance and the perennity and evolution of knowledge in a long life Enterprise with a growing number of datasets, data, data scientists, processes and use cases.

Meetup event: https://www.meetup.com/Data-Science-Milan/events/235594132/

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Data Science for the Enterprises (Spark, Kafka, Mesos and Cassandra) - Andy Petrella, Kensu

  1. 1. Kensu Data Science for the Enterprise
  2. 2. Kensu Created in 2015 Mission: Lifting Data Science to the Enterprise level Product: ADALOG Team Andy Petrella Math, Spark Notebook Xavier Tordoir Physics, Genomics 2 -> 11 scientists in 2016 Plenty of positions more in 2017
  3. 3. Data Science Team minded Platform dedicated to data science team Using production data Including tooling prototyping (local) project (distributed) Test Accept Prod Data Science Spark Mesos HDFS Cassandra Kafka Spark-Notebook JupyterCloudian Marathon Chronos Git Artifactory Enterprise
  4. 4. Production oriented Integration of the whole production toolchain (in scala / JVM) Satisfying the established processes One-click deployment Schema oriented micro services Extensibility (support for libraries, …) Test Accept Prod Data Science Spark Mesos HDFS Cassandra Kafka Spark-Notebook JupyterCloudian Marathon Chronos Git Artifactory Enterprise
  5. 5. Context awareness Data lineage Data statistics live-harvesting Automatic pipelining User awareness Use case awareness Runtime awareness Open SDK (integration with all data science tools)
  6. 6. Efficiency catalyser Data recommendation Model recommendation User recommendation Quality monitoring Root cause analysis Risk analysis and more…
  7. 7. DEMO! (probably long ^^) (there are/will be some pizza…)
  8. 8. Stay tune (@kensuio) Demo accesses on the way Specially for O’Reilly trainees
  9. 9. We’re hiring! http://kensu.io contact@kensu.io
  10. 10. Kensu Data Science for the Enterprise Q/A

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