Scala is the Lingua Franca for Fast Data ApplicationsAlthough Hadoop has used MapReduce as the officially-supported Big Data engine for writing all compute jobs, its inability to handle event stream processing, a difficult API and recent trends in consumer behavior have driven interest in alternatives.
Scala has taken over the world of “Fast” Data, which is what some are calling the next wave of computation engines that rely more on the speed of data processing rather than the size of the batch, and the ability to process event streams in real-time. Several prominent examples of that movement are Apache Spark, Apache Kafka, and Apache Flink, which are rapidly gaining mainstream momentum.
Just enough Scala for SparkApache Spark is written in Scala. Although Spark provides a Java API, many data engineers are adopting Scala since it’s the “native” language for Spark—and because Spark code written in Scala is much more concise than comparable Java code. Most data scientists, however, continue to use Python and R. If you want to learn Scala for Spark, this is the tutorial for you. Chaoran Yu offers an overview of the core features of Scala you need to use Spark effectively, using hands-on exercises with the Spark APIs. You’ll learn the most important Scala syntax, idioms, and APIs for Spark development.
Sign Up: Just Enough Scala for SparkPresented by Chaoran Yu, Fast Data Engineer, Lightbend
Thursday, June 8, 2017
9:00 AM to 5:00 PM
Galvanize, 44 Tehama St, San Francisco, CA
Become a Hero: Build a Greenfield Fast-Data PipelineLightbend has helped some of the world’s
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Read the O’Reilly eBook:
Fast Data Architectures For Streaming Applications , by Dean Wampler, Ph.D., VP of Fast Data Engineering at Lightbend
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