Scala is the Lingua Franca for Fast Data Applications

Although 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 Spark

Apache 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 Spark
Presented 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 Pipeline

Lightbend has helped some of the world’s most admired brands bridge the gap from their traditional roots to embracing streaming, Fast Data platforms to power their next generation of services. From large-scale enterprise Fast Data platforms atIntel and Samsung and consumer IoT plays by Norwegian Cruise Lines and eero, to urban planning at Swisscom and corporate banking at UniCredit Group, Lightbend has a proven track record of enabling enterprises to ditch obsolete infrastructure, reveal data insights never before seen, and roll out new platforms in record time.

Read the O’Reilly eBook: Fast Data Architectures For Streaming Applications , by Dean Wampler, Ph.D., VP of Fast Data Engineering at Lightbend

Reactive Microservices Matter for Always-On Applications

Still chugging along with a monolithic enterprise system that’s unable to support streams of continuous data, difficult to scale and maintain, and even harder to understand? In this concise O’Reilly eBook, Lightbend CTO Jonas Bonér explains why a microservice-based architecture that consists of small, independent services is far more flexible than the traditional all-in-one systems that continue to dominate today’s enterprise landscape.

Read Reactive Microservices Architecture to learn how a Reactive microservice isolates everything (including failure), acts autonomously, does one thing well, owns state exclusively, embraces streaming with asynchronous message passing, and maintains mobility.