About Us
StreamSets provides an innovative data operations platform that reinvents how enterprises deliver timely and trustworthy data to their critical applications. Its mission is to bring performance management to data in motion, so that data continually arrives on-time and with quality, empowering business-critical analysis and decision-making.
Our Story
StreamSets was founded in 2014, by Girish Pancha, former chief product officer of Informatica, and Arvind Prabhakar, an early employee and engineering leader at Cloudera. They formed the company to address the growing challenge of managing data in motion in a world marked by constant change — to data sources, to data processing infrastructure and to the data itself, a problem called data drift. StreamSets was created to re-envision the management of data flows from the ground up, avoiding the pitfalls of legacy offerings and enabling a new way to manage data in motion.
Our first product, StreamSets Data Collector, is used by hundreds of companies to build complex any-to-any dataflows, including Fortune 500 firms across a variety of industries such as financial services, manufacturing, healthcare, media, pharmaceuticals and technology.
Our latest product, StreamSets Dataflow Performance Manager, or DPM, takes on the challenge of operating end-to-end dataflows. DPM is an operational control center that lets you map, measure and master your data in motion. First, it maps your diverse dataflows into topologies that support each of your key business processes. Then it measures the day-to-day operations of these topologies, and finally lets you master their performance by implementing service level agreements that ensure you are always delivering timely and trustworthy data.
StreamSets has offices in San Francisco, Silicon Valley and Barcelona and is backed by top-tier Silicon Valley venture capital firms, including Accel Partners, Battery Ventures, Ignition Partners and New Enterprise Associates (NEA). Our industry-leading partners include Cloudera, Databricks, MapR and Microsoft.
Our Team
Investors
Latest Press Coverage
2018
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DATAVERSITY
Case Study: Cox Automotive Solves Data Drift and ETL Challenges
"According to Pat Patterson, Community Champion at StreamSets, “data drift” is such a problem now that “only about one fifth of a data analyst’s time is actually spent analyzing the data.” The remainder is spent “wrangling it into shape and getting it from where it is to the actual analysis platform.” Speaking at the Enterprise Data World 2017 Conference in a session called Dealing with Drift: Building an Enterprise Data Lake, Patterson and Michael Gay (Lead Technical Architect at Cox Automotive) shared how StreamSets solved the data drift problem for Cox Automotive."
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TechTarget
Streaming tool from StreamSets eyes data in motion for GDPR
"StreamSets software for inspecting big data brings governance to data in motion. Such capabilities may find more use as the European Union's GDPR deadline looms."
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DZone
Managing Data Operations on the Edge: StreamSets [Videos]
"See how to install SDC Edge on a Raspberry Pi, how to build edge pipelines, and how SDC tools can move data from sensors to the heart of your data infrastructure."
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Datanami
Fueled by Kafka, Stream Processing Poised for Growth
"Once a niche technique used only by the largest organizations, stream processing is emerging as legitimate technique for dealing with massive amounts of data generated every day."
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Forbes
Biting The Data Management Bullet At GlaxoSmithKline
"There comes a time in the life of almost every large organization when it has to admit that it doesn’t have the data environment it needs to succeed. For GlaxoSmithKline’s (GSK) Research and Development (R&D) organization, that time was in early 2015. There comes a time in the life of almost every large organization when it has to admit that it doesn’t have the data environment it needs to succeed. For GlaxoSmithKline’s (GSK) Research and Development (R&D) organization, that time was in early 2015. GSK uses Streamsets to move the data into the lake—a million pipelines for thousands of data sources. "
