OVERVIEW:

Apache Spark and the Typesafe Reactive Platform

At Typesafe, we’re committed to helping developers build massively scalable applications on the JVM. Because our users are increasingly building Reactive applications that leverage Big Data, we decided to team with Databricks to help developers understand how to better utilize Spark.

What is Spark?

Apache Spark is a distributed computing system written in Scala and leveraging Akka that was developed initially as a UC Berkeley research project for distributed data programming. It has grown in capabilities and recently became a top-level Apache project. Spark is replacing the venerable Hadoop MapReduce for several reasons including better performance, support for natural data processing idioms, and streaming operations.

Typesafe and Databricks: working together

Spark has quickly shifted from a research project to a production-ready library, thanks in large part to the dedication of the fine folks at Databricks.

Because Spark is implemented in Scala and leverages Akka, it presents a logically consistent extension to the Typesafe Reactive Platform. Your Spark-based data analytics can deploy and scale with your existing environment, without the need to deploy a separate cluster dedicated to data analysis. However, when your projects grow to the point where you need a dedicated data cluster, Spark will grow with you and still interoperate with the rest of your Akka- and Play-based applications.

GETTING STARTED:

To get started with the Typesafe Reactive Platform and Spark, we recommend checking out the following resources:

Code Samples & Tutorials

Hello Apache Spark!

Apache Spark is a fast and general engine for large-scale data processing. This Typesafe Activator template will get you started with Spark.

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Apache Spark in Action

A starter application with Apache Spark.

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Spark Workshop

This Activator template teaches you how to write Apache Spark applications that analyze real data sets, using Spark's batch-mode, streaming, and SQL APIs.

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Reading Materials

Apache Spark: Preparing for the next wave of Reactive Big Data

Over 2000 respondents answered a survey on Apache Spark usage and adoption, emphasizing the industry's increasing demand for features like fast processing of large data sets & event steaming.

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Getting Started with Spark

If you are exploring Big Data and wondering if Spark is right for your Reactive application, this white paper is for you. It provides an insightful overview of new trends in Big Data and includes handy diagrams of representative architectures.

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Databricks Application Spotlight: Typesafe

Spark has emerged as the next-generation platform for writing Big Data applications for Hadoop and Mesos clusters. Part of Spark’s success is due to the foundation it is built upon, components of the Typesafe Reactive Platform.

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Spark Programming Guide

Programming guide for getting started with Spark.

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Videos & Webinars

Why Spark Is the Next Top (Compute) Model

In this presentation, Dean Wampler argues that Spark/Scala is a better data processing engine than MapReduce/Java because tools inspired by mathematics, such as FP, are ideal tools for working with data.

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Why Scala Is Taking Over the Big Data World

Those dealing with Big Data are increasingly won over by the power of Scala. In this presentation Dean Wampler, recognized Scala author and Big Data expert, explains why data-centric applications are driving Scala adoption.

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SUBSCRIPTION:

Ensure the Success of Your Spark Application

If you plan to develop a commercial application, your business could benefit from a relationship with Typesafe. The Typesafe Together annual subscription program is designed to mitigate risk and ensure the successful launch and operation of your application by delivering certified builds and amazing service throughout the entire project lifecycle—from prototyping to production.

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New Report

APACHE SPARK

Preparing for the next wave of Reactive Big Data

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White Paper

Introducing theTypesafe
Reactive Platform

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White Paper

Big Data ApplicationsGetting Started
with Spark

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