Stream

Stream Raises $38 Million Series B Led by Felicis Ventures

Retrieved on: 
Thursday, March 4, 2021
Software, Technology, Professional services, Venture capital, Felicis Ventures, API, California, Stream, Felicis Ventures, GGV Capital, 01 Advisors

Stream , the creator of enterprise-grade activity feed and chat APIs, today announces the close of its $38 million Series B funding round, led by Felicis Ventures Aydin Senkut with participation from Series A lead investor GGV Capital and 01 Advisors .

Key Points: 
  • Stream , the creator of enterprise-grade activity feed and chat APIs, today announces the close of its $38 million Series B funding round, led by Felicis Ventures Aydin Senkut with participation from Series A lead investor GGV Capital and 01 Advisors .
  • The Series B follows just six months after the companys $15 million Series A round in August 2020, bringing total financing to $53 million.
  • The API economy creates a golden age for engineers and product teams, said Stream Co-founder & CEO Thierry Schellenbach.
  • Stream is at the forefront of the API wave, and were very pleased to have them join the Felicis portfolio.

Lightbend to Showcase Best Streaming Data Practices for Machine Learning At O’Reilly Strata Data Summit This Week in New York

Retrieved on: 
Monday, September 23, 2019
Computing, Digital technology, Java platform, Apache Software Foundation, Apache Spark, Cluster computing, Hadoop, University of California, Berkeley, Apache Kafka, Stream, Pipeline

NEW YORK, Sept. 23, 2019 (GLOBE NEWSWIRE) -- This week at OReilly Strata Data conference in New York, the foremost experts in the world of data and business will focus on best practice data techniques and technologies.

Key Points: 
  • NEW YORK, Sept. 23, 2019 (GLOBE NEWSWIRE) -- This week at OReilly Strata Data conference in New York, the foremost experts in the world of data and business will focus on best practice data techniques and technologies.
  • The better way is to treat the model as data and have this model data exchanged between the training and scoring systems, which allows updating models in the running context.
  • The advantage of this approach is that model serving is implemented as part of the larger data-transformation pipeline.
  • Such pipelines can be implemented using streaming enginesSpark Streaming, Flink, or Beamor streaming librariesAkka Streams or Kafka Streams.