You're reading from Apache Spark Quick Start Guide

Product typeBook

Published inJan 2019

Reading LevelIntermediate

PublisherPackt

ISBN-139781789349108

Edition1st Edition

Languages

Scala

Tools

Apache Spark

Concepts

Data Analysis

Authors (2):

Shrey Mehrotra

Akash Grade

View More author details

What is Spark?

Apache Spark is a distributed computing framework which makes big-data processing quite easy, fast, and scalable. You must be wondering what makes Spark so popular in the industry, and how is it really different than the existing tools available for big-data processing? The reason is that it provides a unified stack for processing all different kinds of big data, be it batch, streaming, machine learning, or graph data.

Spark was developed at UC Berkeley’s AMPLab in 2009 and later came under the Apache Umbrella in 2010. The framework is mainly written in Scala and Java.

Spark provides an interface with many different distributed and non-distributed data stores, such as Hadoop Distributed File System (HDFS), Cassandra, Openstack Swift, Amazon S3, and Kudu. It also provides a wide variety of language APIs to perform analytics on the data stored in these data stores. These APIs include Scala, Java, Python, and R.

The basic entity of Spark is Resilient Distributed Dataset (RDD), which is a read-only partitioned collection of data. RDD can be created using data stored on different data stores or using existing RDD. We shall discuss this in more detail in Chapter 3, Spark RDD.

Spark needs a resource manager to distribute and execute its tasks. By default, Spark comes up with its own standalone scheduler, but it integrates easily with Apache Mesos and Yet Another Resource Negotiator (YARN) for cluster resource management and task execution.

One of the main features of Spark is to keep a large amount of data in memory for faster execution. It also has a component that generates a Directed Acyclic Graph (DAG) of operations based on the user program. We shall discuss these in more details in coming chapters.

The following diagram shows some of the popular data stores Spark can connect to:

Data stores

Spark is a computing engine, and should not be considered as a storage system as well. Spark is also not designed for cluster management. For this purpose, frameworks such as Mesos and YARN are used.

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Apache Spark Quick Start Guide

Published in: Jan 2019Publisher: PacktISBN-13: 9781789349108

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Authors (2)

Shrey Mehrotra

Shrey Mehrotra has over 8 years of IT experience and, for the past 6 years, has been designing the architecture of cloud and big-data solutions for the finance, media, and governance sectors. Having worked on research and development with big-data labs and been part of Risk Technologies, he has gained insights into Hadoop, with a focus on Spark, HBase, and Hive. His technical strengths also include Elasticsearch, Kafka, Java, YARN, Sqoop, and Flume. He likes spending time performing research and development on different big-data technologies. He is the coauthor of the books Learning YARN and Hive Cookbook, a certified Hadoop developer, and he has also written various technical papers.
Read more about Shrey Mehrotra

Akash Grade

Akash Grade is a data engineer living in New Delhi, India. Akash graduated with a BSc in computer science from the University of Delhi in 2011, and later earned an MSc in software engineering from BITS Pilani. He spends most of his time designing highly scalable data pipeline using big-data solutions such as Apache Spark, Hive, and Kafka. Akash is also a Databricks-certified Spark developer. He has been working on Apache Spark for the last five years, and enjoys writing applications in Python, Go, and SQL.
Read more about Akash Grade

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