Building Real-Time Data Pipelines through In-Memory Architectures [Webcast]


Kevin White

Kevin is on the marketing team and is responsible for marketing operations and content strategy.

Building Real-Time Data Pipelines through In-Memory Architectures [Webcast]

In the era of universal connectivity, the faster you can move data from point A to B the better. Equipping your organization with the ability to make frequent decisions in an instant offers information and intelligence advantages, such as staying one step ahead of the competition. This is especially important when incoming data is arriving at a relentless pace, in high volume, and from a variety of devices.

As our customers tap into new sources of data or modify to existing data pipelines, we are often asked questions like: What technologies should we consider? Where can we reduce data latency? How can we simplify data architectures?

To eliminate the guesswork, we teamed up with Ben Lorica, Chief Data Scientist at O’Reilly Media to host a webcast centered around building real-time data pipelines.

Watch the recorded webcast to learn:

  • Ideal technology stacks for building real-time data pipelines
  • How to simplify Lambda architectures
  • How to use memory-optimized technologies like Kafka, Spark, and in-memory databases to build real-time data pipelines
  • Use cases for real-time workloads, and the value they offer
  • Examples of data architectures used by companies like Pinterest and Comcast

webcast-recordingWebcast Recording

webcast-slidesWebcast Slides

about-the-presentersAbout the Presenters

Eric Frenkiel, CEO & Co-Founder, SingleStore — Eric Frenkiel co-founded SingleStore and has served as CEO since inception. Before SingleStore, Eric worked at Facebook on partnership development. He has worked in various engineering and sales engineering capacities at both consumer and enterprise startups.

Ben Lorica, Chief Data Scientist, O’Reilly Media — Ben Lorica is the Chief Data Scientist and Director of Content Strategy for Data at O’Reilly Media, Inc. He has applied Business Intelligence, Data Mining, Machine Learning and Statistical Analysis in a variety of settings including Direct Marketing, Consumer and Market Research, Targeted Advertising, Text Mining, and Financial Engineering. His background includes stints with an investment management company, internet startups, and financial services.