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Users of Big (and not so Big) Data roughly divide into three groups, developers like us, traditional data analysts, and a hybrid called data scientists. The analysts prefer SQL, SAS, and similar, traditional tools. The scientists (mostly statisticians, really) prefer Python and R, with Julia emerging. The Developers started with Java, but they are being seduced by Scala, because it offers ideal tools for data-centric applications.
This talk explains why data-centric applications are driving Scala adoption. Scala already provides these essential features:
- Expressive DSLs.
- The JVM.
- Actors for distributed scaling.
- Optimizations for primitives, but uniform source abstractions.
- Functional combinators.
These features combine to yield powerful, scalable tools with concise APIs:
- Scalding and Summingbird - for Hadoop and Storm
- Spark and H2O - the Next Generation...
- Spire and Algebird - Mathematics
Finally, we'll discuss what's missing and what's ahead.
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