Muscle-up the Apache Spark with these incredible tools!

It’s not just being faster, the Apache Spark revolutionized the world of Big Data with its incredible platform and tools. This powerful tool had impressed the world with this simpler and more convenient features. Spark isn't only one thing; it's a collection of components under a common umbrella. And each component is a work in progress, with new features and performance improvements constantly rolled in.

Spark Core

At the heart of Spark is the aptly named Spark Core. In addition to coordinating and scheduling jobs, Spark Core provides the basic abstraction for data handling in Spark, known as the Resilient Distributed Dataset (RDD).

RDDs perform two actions on data: transformations and actions. The former makes changes on data and serves them up as a newly created RDD; the latter computes a result based on an existing RDD (such as an object count).

Spark APIs

Spark is written mainly in Scala, so the primary APIs for Spark have long been for Scala as well. But three other, far more widely used languages are also supported: Java (upon which Spark also relies), Python, and R.

Spark SQL

Never underestimate the power or convenience of being able to run a SQL query against a batch of data. Spark SQL provides a common mechanism for performing SQL queries (and requesting columnar DataFrames) on data provided by Spark, including queries piped through ODBC/JDBC connectors. You don’t even need a formal data source. Support for querying flat files in a supported format, à la Apache Drill, was added in Spark 1.6.

Spark Streaming

Spark’s design makes it possible to support many processing methods, including stream processing -- hence, Spark Streaming. The conventional wisdom about Spark Streaming is that its rawness only lets you use it when you don’t need split-second latencies or if you aren’t already invested in another stream-processing solution -- say, Apache Storm.

Machine learning technology has a reputation for being both miraculous and difficult. Spark allows you to run a number of common machine learning algorithms against data in Spark, making those types of analyses a good deal easier and more accessible to Spark users.

GraphX (Graph computation)

Mapping relationships between thousands or millions of entities typically involves a graph, a mathematical construct that describes how those entities interrelate. Spark’s GraphX API lets you perform graph operations on data using Spark’s methodologies, so the heavy lifting of constructing and transforming such graphs is offloaded to Spark. GraphX also includes several common algorithms for processing the data, such as PageRank or label propagation.

SparkR (R on Spark)

Aside from having one more language available to prospective Spark developers, SparkR allows R programmers to do many things they couldn’t previously do, like access data sets larger than a single machine’s memory or easily run analyses in multiple threads or on multiple machines at once.