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Oct 30, 2020
LONDON and BOSTON – October 30, 2020 – Privitar, the leading data privacy platform provider, and StreamSets®, provider of the industry’s first DataOps platform, today announced a new partnership and native product integration designed to help organizations accelerate access to data-driven insights by streamlining and minimizing the privacy risk in data pipelines. Through the new native integration, users can leverage StreamSets to design and run data pipelines to their data science and data analytics applications, seamlessly applying Privitar’s data privacy policies across all of their execution environments.
“Business users are increasingly demanding continuous access to shared data as it is generated across organizations and ecosystems, enabling fast decisions on fresh data, while simultaneously complying with increasingly complex regulation,” said Jobi George, General Manager, StreamSets Cloud. “We’re delighted in this partnership, extending Privitar’s privacy protections to StreamSets’ smart data pipelines enables teams to get analytical value out of broader and wider data sets by safely integrating and combining sensitive data.”
“By partnering and integrating our platforms, Privitar and StreamSets ensure that data owners and users can be confident the privacy protections they need are applied consistently, every time and everywhere, regardless of the tools they use to analyze their data or the environments to which it flows,” said Marc Moesse, VP of Product at Privitar. “Users will be able to deliver a continuous supply of data to every part of their organization, and safely utilize sensitive data to gain timely insights and support data-driven decisions. This, in turn, can help businesses develop better products, services, and customer experience, decrease time-to-market and increase revenue and profit.”
StreamSets enables users to quickly build data pipelines, complete with visual, full-lifecycle tools for designing, operating, managing and optimizing data pipelines across the enterprise.
The Privitar Data Privacy Platform™ provides a unique combination of privacy techniques, governance and management features that are essential to any organization embracing data-driven insight. The integration of Privitar and StreamSets enables data-driven organizations to both streamline and de-risk their data pipelines, by:
For more information, visit privitar.com.
Organizations worldwide rely on Privitar to realize the promise of one of their most valuable assets – safe, usable data.
Privitar empowers organizations to use sensitive data to gain valuable insights, and to support data-driven decisions. By delivering comprehensive data privacy techniques and streamlining data provisioning, Privitar enables enterprises to extract the maximum value from the data they collect, manage and use, while minimizing risk.
Founded in 2014, Privitar is headquartered in London, with regional headquarters in Boston and Singapore, a development center in Warsaw, and sales and services locations throughout the US and Europe. For more information, please visit privitar.com.
StreamSets built the industry’s first multi-cloud DataOps platform for modern data integration, helping enterprises to continuously flow big, streaming and traditional data to their data science and data analytics applications. The platform uniquely handles data drift, those frequent and unexpected changes to upstream data that break pipelines and damage data integrity. The StreamSets DataOps Platform allows for execution of any-to-any pipelines, ETL processing and machine learning with a cloud-native operations portal for the continuous automation and monitoring of complex multi-pipeline topologies.
Founded in 2014, StreamSets is backed by top-tier Silicon Valley venture capital firms, including Battery Ventures, New Enterprise Associates (NEA), and Accel Partners. For more information, visit www.streamsets.com.
StreamSets and the StreamSets Logo are the registered trademarks of StreamSets, Inc. All other marks referenced are the property of their respective owners.
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