New programming interface feature simplifies big-data analytics
Nuremberg, Germany, June 12, 2012 – EXASOL AG present their new EXAPowerlytics feature that gives customers the possibility to program their own analyses for their data with full flexibility. Powerlytics empowers the evaluation of polystructural social media data, the full integration of external data from Hadoop frameworks and the convenient implementation of Map/Reduce algorithms that allow for processing Loading...big data on distributed systems. Applications of the new feature include time series analysis and sample analysis, statistical analysis, text search, data transformation or data mining.
EXASOL customers who use EXAPowerlytics benefit from the even faster access to relevant information and useful results. These serve as future-looking, evidence-based decision aids for business-critical processes, such as in risk, fraud, invoicing and customer management or marketing. Positive side effects: IT resources are saved, the data volume is reduced and sourcing is significantly accelerated.
The objective of Loading...In-Database Analytics is to directly move the analysis from external systems directly to theEXASolution database in order to avoid data replication and to tap the full power of the high-performance cluster. This eliminates the need to transfer the data into an analysis system, only the calculation results are used.
The EXAPowerlytics algorithms can be conveniently stored in EXASolution as User Defined Scripts, where they can be directly integrated into the SQL execution. This way, scalar, analytical and aggregate functions, Map/Reduce algorithms or data import/export are integrated into the SQL processing. Users have the choice between R, Loading...Python or Lua programming languages, which allows them to program their own custom algorithms. EXASolution automatically executes these programs in parallel by means of the embedded scripting engine in the cluster, guaranteeing maximum scalability and performance.
- In-Database Analytics empowers new analyses directly in EXASolution where heritage SQL and BI tools reach their limits
- Designed for scalable, parallel computing by the embedded scripting engine of the EXASolution cluster
- Empowers Map/Reduce algorithms, Hadoop integration and computing unstructured data
- Includes numerous libraries for simplified development, e.g. computing unstructured data, integration of external data sources, statistical functions, text analysis and data transformation
- Seamless integration with native SQL features for creating customized analysis appli-cations with highest performance
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“Gaining information from social media, blogs and e-mails requires Loading...big data analytics and suitable data warehouse management solutions as a driver”, says Steffen Weissbarth, CEO of EXASOL AG. “With EXAPowerlytics, we take our innovation strategy further, companies can adjust to constant change and achieve remarkable results in real time.“
”With EXAPowerlytics, comprehensive analysis that takes heritage SQL and BI tools to their limits, happen right in the database – this provides a fast and reliable basis for decisions in the company“, says Mathias Golombek, Head of R&D at EXASOL AG. “By using numerous auxiliary libraries we integrated into EXAPowerlytics, it’s easy to process xml documents, integrate external http interfaces, compute statistical functions or run string manipulations.”
Nuremberg-based EXASOL AG develop and distribute the high-performance database EXASolution, which is based on in-memory technology and that has been specifically designed to meet the needs of data warehouseapplications and business intelligence solutions. It allows for the rapid analysis and evaluation of even very large data volumes. Thanks to its high performance and low administration effort, EXASolution not only help companies to derive valuable decisions bases from their data but also for reducing their total cost of ownership. In April 2011, Gartner recognized EXASOL AG as”„Cool Vendor“ in the data management category and integration, 2011. In 2012, the solution was added to the Magic Quadrant “Data Warehouse database Management Systems“.