Peter Harrington explains what you do with machine learning, and what are the building blocks for an application that uses machine learning from collected data to creating predictions for customers.
Jonathan Bell & Gail Kaiser introduce Phosphor, a dynamic taint tracking system for the JVM, describing the approach used to achieve portable taint tracking.
Bob Kelly presents case studies on how Platfora uses Hadoop to do analytics for several of their customers.
Seth Juarez shares insight on how to create applications that use dashboards to drive value, convert raw data into answers, and simplify business processes.
The authors explain how the Pivotal team leveraged familiar SQL-based queries to analyze fine-grained cluster utilization using Spring XD.
Steve Hoffman, Ken Dallmeyer share their experience integrating Hadoop into the existing environment at Orbitz, creating a reusable data pipeline, ingesting, transporting, consuming and storing data.
Wesley Chow presents Chartbeat's real-time analytics platform and how able to handle the requests in a cost efficient manner using a custom written analytics engine in C and Lua.
Bryan Nehl makes an introduction to the data science: data formats, ETL tools, NoSQL databases, languages, libraries, techniques and approaches for exploring data and extracting value from it.
Stefan Edlich discusses big data systems -Spanner, Presto- and the future of data persistence, data analytics, data formats and of NoSQL/NewSQL in general.
The authors present design patterns and use cases of capital market firms that are incorporating big data technologies into their credit risk analysis, price discovery or sentiment analysis software.
Erik Hinton discusses the successes and failures of making a cultural shift in the newsroom at NYT to accept Haskell and some of the projects Haskell has been used for.
Cliff Click introduces a coding style & API for in-memory analytics that handles datasets from 1K to 1TB without changing a line of code and clusters with TB of RAM and hundreds of CPUs.