Azure Apache Spark for Azure HDInsight

Apache Spark is a framework which performs open-source processing to run large-scale data analytics application. At in-memory compute engine Spark is built on by which big data is enabled for high performance queuing. It takes advantage of parallel data processing framework that continues to exist data in memory and disk in need. By this, Spark can deliver faster speed up to 100x and common execution model. You can easily deploy Apache Spark without any hardware to set up or software to configure.

Advantages

For multiple tasks, you use one common execution model. This gives you permission to use Spark for HDInsight by which big data challenges such as financial alerts, telemetry from IoT sensors and devices, clickstream analysis, fraud detection, social analytics can be solved in near real time.

The expectation of customers these days is to have a quick solution to their queries. Persisting data into memory is delivered by Apache Spark to get 100x faster queries while large datasets processing in Hadoop.

To make development easy on Spark, deep integration with IntelliJ IDEA is introduced through which coding can be done with native authoring support for Scala and Java. Debugging can be done here remotely. It provides flexibility for development lifecycle and application can be submitted to Azure when it's ready. You get Python libraries (Anaconda) for machine learning which is pre-loaded at Spark for HDInsight clusters.

To interactively analyze big data you can take advantage of BI tools by which interactive visualization can be built over data of any size.

Jupiter (ipython) is integrated with Spark for HDInsight which gives you out-of-the-box notebook experience.

Spark for HDInsight is combined with R Server, which is equipped with large parallel analytics and machine learning libraries, built to work with R language.

To make sure that Spark runs at highest scale, Microsoft provides you SLA at 99.9% industry’s which is highest in the industry such that your business continuity is insured and you get protected against catastrophic events.

As data grows you can analyze any data of any size without any changes

Real-time scenarios are tackled with real-time processing.