Presented By
O’Reilly + Cloudera
Make Data Work
March 25-28, 2019
San Francisco, CA

Flink SQL in action

Fabian Hueske (Ververica)
11:50am12:30pm Thursday, March 28, 2019
Secondary topics:  Data Integration and Data Pipelines, Streaming, realtime analytics, and IoT

Who is this presentation for?

  • Data engineers, architects, and data scientists

Level

Intermediate

Prerequisite knowledge

  • Familiarity with SQL and stream processing

What you'll learn

  • Understand principles and best practices for running SQL on streams
  • Explore common applications for Flink SQL
  • Learn how to use Flink SQL

Description

Stream processing is being rapidly adopted by the enterprise. While in the past, stream processing frameworks mostly provided Java- or Scala-based APIs, stream processing with SQL is growing increasingly popular because it makes stream processing accessible to nonprogrammers and significantly reduces the effort to solve common tasks.

About three years ago, the Apache Flink community started adding SQL support to process static and streaming data in a unified fashion. Today, Flink SQL powers production systems at Alibaba, Huawei, Lyft, and Uber. Fabian Hueske discusses the current state of Flink’s SQL support and explains the importance of Flink’s unified approach to process static and streaming data. After covering the basics, he shares common real-world use cases ranging from low-latency ETL to pattern detection and demonstrates how easily they can be addressed with Flink SQL.

Photo of Fabian Hueske

Fabian Hueske

Ververica

Fabian Hueske is a committer and PMC member of the Apache Flink project. He was one of the three original authors of the Stratosphere research system, from which Apache Flink was forked in 2014. Fabian is a cofounder of Ververica, a Berlin-based startup devoted to fostering Flink, where he works as a software engineer and contributes to Apache Flink. He holds a PhD in computer science from TU Berlin and is currently spending a lot of his time writing a book, Stream Processing with Apache Flink.

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