Advanced in-database analytics and data science with R, Java, Python, Lua or your language of choice

The ability to run sophisticated analytics that incorporates all kinds of available data sources has become crucial for organizations as they strive to be ever more successful.

While big data has seen the emergence of massive data volumes, more importantly, it has redefined the role of data in business. As a consequence, data science has matured into a well-established field of expertise that continues to receive attention as organizations need to do more with data than ever before.

In response to the two top trends of recent years – big data and data science (now fused together as big data science) – EXASOL offers a new systems architecture for big data science that allows users to perform advanced analytical tasks on large volumes of data in an interactive fashion – right in the database, using any programming language.

Whitepaper: Big data science – the future of analytics

A new technological approach for big data science systems

Learn more about EXASOL’s new systems architecture for big data science that allows users to perform advanced analytical tasks on large volumes of data in an interactive fashion – right in the database, using any programming language!

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Enabling big data science

EXASOL executes pre-defined or user-defined (UDF) analytical functions fully in parallel and distributed across the database cluster. Not only does EXASOL support R – the standard language among statisticians and data miners for developing statistical software and for advanced data analysis – but also Java, Python, and Lua as well as a framework to integrate any analytics programming language you choose.

The performance and scalability of EXASOL combined with capabilities in R, such as clustering, exponential smoothing, predictive modeling, and machine learning, enable a new generation of advanced analytic applications. Organizations use EXASOL with R for customer segmentation, market basket analysis, real-time offers, predictive asset maintenance, and other applications.

Features at a glance

  • Algorithms are pushed down to the data and run where the data resides
  • Massive parallel execution of algorithms
  • Embedded into SQL database processing with user-defined functions (UDF)
  • Support for R, JAVA, Python, Lua and universal/pluggable language support (Scala, Julia, C++, etc.)
  • Integrated into standard tools like RStudio
  • Seamless integration into existing architectures

EXASOL delivers an ever-increasing range of advanced in-database analytics functions so that you can run analytic computations where the data resides and get answers fast - instead of exporting it to a separate location for processing and analysis. As your data volumes grow over time, keeping the data in-database is advantageous from a management, processing and analysis perspective.

Furthermore, with EXASOL, all analytic functions and UDFs fully leverage the database’s features, such as massively parallel processing (MPP), columnar storage, compression and in-memory so that they run faster than ever before.

Whitepaper: Big data science – the future of analytics

Are you interested in learning more about big data science and advanced analytics? Download your copy of the whitepaper now.

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