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A Tale of Two APIs: Using Spark Streaming In Production

Tips For When To Use Spark Streaming VS Structured Streaming

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Fast Data architectures are the answer to the increasing need for the enterprise to process and analyze continuous streams of data to accelerate decision making and become reactive to the particular characteristics of their market. To address the challenges of never-ending datasets, Spark offers two API's: The mature Spark Streaming, and its younger sibling, Structured Streaming. But how do you know which one will best suit your needs, as well as which other streaming engines out there to consider like Akka Streams, Flink and Kafka?

In this webinar by Gerard Maas, senior engineer on Lightbend's Fast Data Platform team and co-author of the recent O’Reilly book Learning Spark Streaming, we walk through an introduction of both APIs. At the end of this presentation, you will have a bit more context for guiding your selection of the the right one for your application.

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(Note: due to a delay caused by technical errors, our traditional webinar introduction and Q/A session was not recorded) 

Four Resources For Designing, Building, And Running Fast Data Applications

  1. Embrace Spark Streaming with best practices by François Garillot, and Gerard Maas of Lightbend, Inc.: Learning Spark Streaming

  2. Demystify Machine Learning with the O’Reilly eBook by Boris Lublinsky of Lightbend, Inc.: Serving Machine Learning Models

  3. Consider important production requirements for monitoring and management: The Secrets To Successfully Monitoring Fast Data And Streaming Applications

  4. Tie it all together in a single platform: Lightbend Fast Data Platform - A Technical Overview For Decision Makers

As always, a Lightbend representative is always available to schedule a 20-min introductory chat with you and your team. Simply contact us here:


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