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Spark Tutorial

Introduction

In this Apache Spark tutorial you will learn Spark from the basics to get a clear idea of this top big data processing engine. Apache Spark is a fast in-memory big data processing engine that helps to compute and analyze streaming data in real-time and it is up to 100 times faster than MapReduce. It is a complete engine that is equipped with the capabilities of Machine Learning as well.

Recommended audience

This Spark tutorial is meant for Big Data analytics professionals, software developers, IT administrators, Data Scientists and graduates who want to make a career in big data analytics domain.

Prerequisites

There are no prerequisites for learning from this Spark tutorial. You can learn Spark better if you have a basic understanding of Java or any other programming language.

What is Spark?

Criteria Spark Hadoop MapReduce
Speed 100 times faster than MapReduce Equal to the speed of MapReduce
Interactive mode Yes No
Processing type Stream processing Batch processing
Latency Low latency due to in-memory processing High latency due to disk-oriented processing

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Why is Spark so widely used?

Spark is a revolutionary big data analytics tool that takes off from where MapReduce left. MapReduce was good up to a certain time, but today the kind of data that we are seeing increasingly getting complex and coming in real fast. So that is where Spark takes on a new role of being the big data processing engine of choice. It has some excellent features like in-memory processing, ability to do massive parallel processing, work for machine learning applications and so. So due to all these features we are seeing a huge deployment of large and small companies constantly deploying Spark and this Spark deployment will only increase in the future.

Features of Spark

  • Super-fast processing which is up to 100 times faster than MapReduce
  • You can deploy dynamic parallel operations with scores of high-level operators
  • It offers in-memory processing wherein data is cached in the memory for rapid read and write
  • The Spark code can be reused for batch processing, running ad-hoc queries, etc.
  • Spark is highly fault-tolerant making ensuring there is no loss of time or data if any node fails
  • Spark is one of the top engines for real-time streaming data processing
  • Spark performs lazy evaluation thanks to DAG making it highly efficient
  • Spark applications can be written in Scala, Python, R, Java thus making it highly versatile

Applications of Spark

Spark is a highly versatile big data processing engine. Here we list some of the top applications of Spark cutting across industry verticals.

  • Providing a holistic customer service by analyzing data from multiple customer touchpoints
  • Building an ecommerce recommender engine based on customer past buying habits
  • Creating customized ad targeting on websites based on customer profiles
  • Text analysis to identify customer sentiments on social media channels like Twitter
  • Machine learning applications for supporting AI initiatives using Spark MLlib.
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Why should you learn Spark?

Spark is the preferred engine of choice for big data problems. Now would be the right time to learn Spark since the market for Spark is just heating up. As we all know Hadoop is slowly being replaced with Spark. Also, Spark has some excellent features making it triumph over Hadoop. Spark works on streaming data, it is very powerful, it has machine learning component and so on. All this makes learning Spark that much more exciting and promising as well. Also, the salaries for Spark professionals are among the best in the technology industry.

Strengths of Spark

As it is widely known in the big data analytics industry, Apache Spark is known as the “Swiss Army Knife” of big data analytics. So from this it is obvious that Spark is an extremely versatile big data engine. It can work for stream processing, batch processing, iterative processing and also used for caching data for better access to data. We can use Spark for machine learning applications as well. The Spark GraphX is an API that is used for graph parallel processing.

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"4 Responses on Spark Tutorial"

  1. Neha says:

    A big thanks to Intellipaat- as a beginner, I could not have understood it better than this tutorial.

  2. Aaron says:

    The material of the tutorial is easy to follow and very informative. It was great, I learned a lot in a clear concise way. Thanks..

  3. Darshan says:

    I really enjoyed this tutorial, it gave me lots of background to understand the basics of apache technologies.This is a wonderful startup tutorial.

  4. Monu says:

    Wonderful tutorial on Apache Spark. Really helpful!

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