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Datawatch Angoss Simplifies Data Science and Analytic Tasks on the Apache Spark Platform

November 13, 2018

KnowledgeSTUDIO for Apache Spark provides scalable data analysis across large and small data sets to build analytic workflows without complex coding or scripting

BEDFORD, Mass., Nov. 13, 2018 (GLOBE NEWSWIRE) -- Datawatch Corporation (NASDAQ-CM: DWCH ) today announced the general availability of Datawatch Angoss KnowledgeSTUDIO for Apache Spark, enabling organizations to act more confidently with their data and rely on consistent, trustful results in making better business decisions. In combination with its market-leading data visualization approach for building, exploring and segmenting data using patented Decision Tree technology, Datawatch Angoss enables data science teams to create predictive analytic models using Apache Spark by means of a drag-and-drop / point-and-click interface.

Customers now have a clearer path to augment client and server-based analytics tool sets with a solution that is specifically built for Big Data solutions like Apache Spark. “Efficient model building and easy-to-understand visuals that Decision Trees bring to data science teams allows users to not only create analytic models to generate insights and predictions, but they can also manipulate, combine and profile data sources entirely within a Spark cluster,” said Rami Chahine, Vice President, Product Management. “All while delivering the same workflow building experience that customers have come to value, with intuitive, interactive workflows and no need for coding.”

Data science teams that are modeling in a Big Data environment, and outside of it, can use Angoss KnowledgeSTUDIO for Apache Spark to efficiently build analytic workflows using large, small and wide datasets in a Spark environment. Datawatch Angoss market-leading decision tree interface can now be used by data scientists and business analysts, without having to move data out of Spark.

As with other Datawatch solutions, Angoss KnowledgeSTUDIO for Apache Spark requires no coding expertise. Users working to address business problems can now support advanced modeling with open source packages such as SparkML, Spark SQL and file systems accessible via Spark interfaces. Data preparation and profiling allow for easy data extraction and manipulation, and data can easily be transformed for modeling. “Angoss KnowledgeSTUDIO for Apache Spark allows business users to create predictive models at scale, from a variety of datasets regardless of their size,” continued Chahine. “This more efficient use of compute resources, especially in cloud environments, shortens processing cycles and can reduce costs.”

About Datawatch Corporation Datawatch Corporation (NASDAQ-CM: DWCH ) is the data intelligence provider with market leading enterprise data preparation, predictive analytics and visualization solutions that fuel business analytics. Only Datawatch can confidently position individuals and organizations to master all data – no matter the origin, format or narrative – resulting in faster time to insight. Datawatch solutions are architected to drive the use of more data, foster more trust and incorporate more minds into business analytics. Thousands of organizations of all sizes in more than 100 countries worldwide use Datawatch products, including 93 of the Fortune 100. The company is headquartered in Bedford, Massachusetts, with offices in New York, London, Toronto, Stockholm, Singapore and Manila. To learn more about Datawatch please visit: www.datawatch.com.

Safe Harbor Statement under the Private Securities Litigation Reform Act of 1995Any statements contained in this press release that do not describe historical facts may constitute forward-looking statements as that term is defined in the Private Securities Litigation Reform Act of 1995. Any such statements contained herein, including but not limited to those relating to product performance and viability, are based on current expectations, but are subject to a number of risks and uncertainties that may cause actual results to differ materially from expectations. The factors that could cause actual future results to differ materially from current expectations include the following: rapid technological change; Datawatch’s dependence on the introduction of new products and product enhancements and possible delays in those introductions; acceptance of new products by the market, competition in the software industry generally, and in the markets for next generation analytics in particular; and Datawatch’s dependence on its principal products, proprietary software technology and software licensed from third parties. Further information on factors that could cause actual results to differ from those anticipated is detailed in various publicly-available documents, which include, but are not limited to, filings made by Datawatch from time to time with the Securities and Exchange Commission, including but not limited to, those appearing in the Company’s Annual Report on Form 10-K for the year ended September 30, 2015. Any forward-looking statements should be considered in light of those factors.

Media Contact:Frank MorenoVice President Worldwide Marketing, Datawatch Corporation frank_moreno@datawatch.com 978-275-8225Twitter: @datawatch

© 2018 Datawatch Corporation. Datawatch and the Datawatch logo are trademarks or registered trademarks of Datawatch Corporation in the United States and/or other countries. All other names are trademarks or registered trademarks of their respective companies.

Source: Datawatch Corporation

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