Learn Apache Spark: A Comprehensive Guide

Content
▪ Introduction
▪ What is Apache Spark?
▪ Apache Spark Features
▪ Components of Apache Spark Ecosystem
▪ Apache Spark Languages
▪ Apache Spark History
▪ Why You Should Learn Apache Spark
▪ Do We Need Hadoop to Run Spark?

Content
▪ Apache Spark Installation
▪ Apache Spark Example
▪ Apache Spark Use Cases
▪ Apache Spark Books
▪ Apache Spark Certifications
▪ Apache Spark Training
▪ Final Words

Introduction
For the analysis of big data, the industry is extensively using Apache
Spark. Hadoop enables a flexible, scalable, cost-effective, and fault-
tolerant computing solution. But the main concern is to maintain the
speed while processing big data. The industry needs a powerful engine
that can respond in less than seconds and perform in-memory
processing. Also, that can perform stream processing as well as batch
processing of the data. This is what made Apache Spark come into
existence!
This is the comprehensive guide that will help you learn Apache Spark.
Starting from the introduction, I’ll show you everything you want to
know about Apache Spark. Sounds good? Let’s dive right in..

What is Apache Spark?
The Spark is a project of Apache, popularly known as “lightning fast
cluster computing”. Spark is an open-source framework for the
processing of large datasets. It is the most active Apache project of the
present time. Spark is written in Scala and provides APIs in Python,
Scala, Java, and R.
The most important feature of Apache Spark is its in-memory cluster
computing that is responsible to increase the speed of data
processing. Spark is known to provide a more general and faster data
processing platform. It helps you run programs comparatively faster
than Hadoop i.e. 100 times faster in memory and 10 times faster even
on the disk.

Apache Spark Features
▪ Multiple Language Support
Apache Spark supports multiple languages; it provides APIs written in
Scala, Java, Python or R. It allows users to write applications in different
languages.
▪ Fast Speed
The most important feature of Apache Spark is its processing speed. It
allows an application to run on Hadoop cluster, up to 100 times faster in
memory, and 10 times faster on disk.
▪ Runs Everywhere
Spark can run on multiple platforms without affecting the processing
speed. It can run on Hadoop, Kubernetes, Mesos, Standalone, and even
in the Cloud.

Apache Spark Features
▪ General Purpose
The spark is a powered by the plethora of libraries for machine learning i.e.
MLlib, DataFrames, and SQL along with Spark Streaming and GraphX. One is
allowed to use a combination of these libraries coherently in an application.
The feature of combining streaming, SQL, and complex analytics, and using in
the same application makes Spark a general-purpose framework.
▪ Advanced Analytics
Apache Spark is known to support ‘Map’ and ‘Reduce’ that has been
mentioned earlier. But along with MapReduce, it supports Streaming data,
SQL queries, Graph algorithms, and Machine learning. Thus, Apache Spark is a
great mean of performing advanced analytics.

Apache Spark Components
Apache Spark Ecosystem comprises of various Apache Spark components that are
responsible for the functioning of the Apache Spark. There are 5 components of Apache
Spark that constitute Apache Spark ecosystem.
▪ Spark Core
The main execution engine of the Spark platform is known as Spark Core. All the
working and functionality of Apache Spark depends on the Spark Core including
memory management, task scheduling, fault recovery, and others. It enables in-
memory processing and is responsible to define RDD (Resilient Distributed Dataset) by
an API that is the programming abstraction of Spark.
▪ Spark SQL and DataFrames
The Spark SQL is the main component of Spark that works with the structured data and
supports structured data processing. Spark SQL comes with a programming abstraction
known as DataFrames. Spark SQL enables developers to combine SQL queries with
manipulated programmatic data that are supported by RDDs in different languages.

Apache Spark Components
▪ Spark Streaming
This Spark component is responsible for the live stream data processing such as log
files created by production web servers. It provides API for the manipulation of data
streams, thus makes it easy to learn Apache Spark project. This component is also
responsible for throughput, scalability, and fault tolerance as that of the Spark Core.
▪ MLlib
MLlib is the in-built library of Spark that contains the functionality of Machine
Learning, known as MLlib. It provides various ML algorithms such as clustering,
classification, regression, collaborative filtering and supporting functionality. MLlib
also contains many low-level machine learning primitives.
▪ GraphX
GraphX is the library that enables graph computations. GraphX also provides an API
to perform graph computation by allowing users generate directed graph using
arbitrary properties of the edge and vertex.

Apache Spark Languages
Apache Spark is written in Scala. So, Scala is the native language
used to interact with the Spark Core. Besides, the APIs of Apache
Spark has been written in other languages, these are
▪ Scala
▪ Java
▪ Python
▪ R
As the framework of Spark is built on Scala, it can offer some great
features as compared to other Apache Spark languages. Using
Scala with Apache Spark provides you access to the latest features.
According to a Spark Survey on Apache Spark Languages, 71% of
Spark developers are using Scala, 58% are using Python, 31% are
using Java, while 18% are using R language.

Apache Spark History
Apache Spark introduction cannot actually begin without mentioning the
history of Apache Spark. So, let’s state in brief, Spark was first introduced
in the year 2009 in UC Berkeley R&D Lab, now AMP Lab by M. Zaharia.
And then Spark was open-sourced under BSD License in the year 2010.
In 2013, the Spark project was donated to Apache Software Foundation
and the BSD license turned into Apache 2.0. In 2014, Spark became a top-
level project of Apache Foundation, known as Apache Spark.
In 2015, with the effort of over 1000 contributors, Apache Spark became
one of the most active Apache projects as well as most active open source
project of big data. Till date,. Apache Spark version 2.3.0 has recently
been released on Feb 28th, 2018 which is the latest version of Apache
Spark.

Why You Should Learn Apache
SparkWith the generation of big data by businesses, it has become very
important to analyze that data to understand business insights. Spark is a
revolutionary framework on big data processing land. Enterprises are
extensively adopting Spark which in turn is increasing demand for Apache
Spark developers.
According to O'Reilly Data Science Salary Survey, the salary of developers
is a function of their Apache skills. Scala language and Apache Spark skills
give a good boost to your existing salary. Apache Spark developers are
known as the programmers who receive the highest salary in
development. With the increasing demand for Apache Spark developers
and their salary level, it is the right time for development professionals to
learn Apache Spark and thus help enterprises to perform analysis of data.

Why You Should Learn Apache
SparkHere are the top 5 reasons you should learn Apache
Spark to boost your development career.
▪ To get more access to Big Data
▪ To grow with the growing Apache Spark Adoption
▪ To get benefits of existing big data investments
▪ To fulfill the demands for Spark developers
▪ To make big money

Do You Need Hadoop to Run
Spark?Spark and Hadoop are the most popular big data processing
frameworks. Being faster than MapReduce, Apache Spark has taken an
edge over the Hadoop in terms of speed. Also, Spark can be used for
the processing of different kind of data including real-time whereas
Hadoop can only be used for the batch processing.
Although Hadoop and Spark don’t do the same thing but can still work
together. Spark is responsible for the faster and real-data processing of
data in Hadoop. To achieve maximum benefits, one can run Spark in
the distributed mode using HDFS.
So, it is not the case that we always need Hadoop to run Spark. But if
you want to run Spark with Hadoop, HDFS is the main requirement to
run Spark in the distributed mode.

Apache Spark Installation
The installation of Apache Spark is not a single step process but
we need to perform a series of steps. Note that Java and Scala
are the prerequisites to install Spark. Let’s start 7 step Apache
Spark installation process.
Step 1: Verify if Java is Installed
Step 2: Verify if Scala is Installed
Step 3: Download Scala
Step 4: Install Scala
Step 5: Download Spark
Step 6: Install Spark
Step 7: Verify Spark Installation

Spark Example: Word Count
ApplicationLet’s understand Spark with an example i.e. how to run word count
application. The word count application will count the number of each
word in the document. Consider the below-given input text which has
been saved as input.txt in the home directory.
Following is the procedure to execute the word count application –
Step 1: Open Spark shell
Step 2: Create RDD
Step 3: Execute word count logic
Step 4: Apply action
Step 5: Check output

Apache Spark Use Cases
So, after getting through Apache Spark introduction and installation, it’s
time to have an overview of the Apache Spark use cases. What do these
Spark use cases signify? The Apache Spark use cases explain where
Apache Spark can be used. Before reading the Apache Spark use cases,
let’s understand why companies should use Apache Spark. So, the
businesses should adopt or say have adopted Apache Spark due to its
▪ Ease of use
▪ High-performance gains
▪ Advanced analytics
▪ Real-time data streaming
▪ Ease of deployment

Apache Spark Use Cases
Apache Spark helps businesses to understand the types of
challenges and problems where we can effectively use Apache
Spark. Let’s have a quick sampling of top Apache Spark use cases
in different industries!
▪ E-Commerce Industry
▪ Healthcare Industry
▪ Travel Industry
▪ Game Industry
▪ Security Industry

Apache Spark Books
. Here is the list of top 10 Apache Spark Books –
▪ Learning Spark: Lightning-Fast Big Data Analysis
▪ High-Performance Spark: Best Practices for Scaling and Optimizing Spark
▪ Mastering Apache Spark
▪ Apache Spark in 24 Hours, Sams Teach Yourself
▪ Spark Cookbook
▪ Apache Spark Graph Processing
▪ Advanced Analytics with Apark: Patterns for learning from Data at Scale
▪ Spark: The Definitive Guide – Big Data Processing Made Simple
▪ Spark GraphX in Action
▪ Big Data Analytics with Spark

Apache Spark Certifications
With the increasing popularity of Apache Spark in the big data industry, the
demand for Apache Spark developers is also increasing. But the companies are
looking for the candidates with validated Apache Spark skills i.e. professionals with
an Apache Spark Certification.
Apache Spark Certifications will help you to start a big data career by validating
your Apache Spark skills and expertise. Getting an Apache Spark Certification will
make you stand out of the crowd by demonstrating your skills to the employers and
peers. Here is the list of top 5 Apache Spark Certifications:
▪ HDP Certified Apache Spark Developer
▪ O’Reilly Developer Certification for Apache Spark
▪ Cloudera Spark and Hadoop Developer
▪ Databricks Certification for Apache Spark
▪ MapR Certified Spark Developer

Apache Spark Training
As the demand for Apache Spark developers is on the rise in the
industry, it becomes important to enhance your Apache Spark skills. A
good Apache Spark training helps big data professionals to get hands-
on experience as per industry standards. Nowadays, enterprises are
looking for Hadoop developers who are skilled in the implementation
of Apache Spark best practices.
Whizlabs Apache Spark Training helps you to learn Apache Spark and
prepares you for the HDPCD Certification exam. This Apache Spark
online training helps you get familiar with the deployment of Apache
Spark to develop complex and sophisticated solutions for the
enterprises.

Apache Spark Training
Whizlabs online training for Apache Spark Certification is one
of the best in industry Apache Spark training. Whizlabs
Hortonworks Apache Spark Developer Certification Online
Training helps you to
▪ validate your Apache Spark expertise
▪ demonstrate your Apache Spark skills
▪ remain updated with the latest releases
▪ solve your queries by industry experts
▪ get accredited as certified Spark developer
▪ earn more by giving you a raise in your salary

Final Words
In this presentation, we have covered a complete definitive and
comprehensive guide on Apache Spark. No doubt, it is a must-read guide
for those who want to learn Apache and also for those who want to
extend their Apache Spark skills. Whether you want to learn Apache
Spark components or need to find best Apache Spark certifications, you
can find here!
This guide is the one-stop destination where one can find the answer to
all the questions based on Apache Spark. Apache Spark has the power to
simplify the challenging processing tasks on different types of large
datasets. It performs complex analytics with the integration of graph
algorithms and machine learning. Spark has brought Big Data processing
for everyone. Just check it out!

Reference Links
1. https://spark.apache.org/
2. https://www.whizlabs.com/blog/learn-apache-spark/
3. https://www.whizlabs.com/blog/importance-of-apache-spark/
4. https://www.whizlabs.com/blog/best-apache-spark-books/
5. https://hortonworks.com/
6. https://www.cloudera.com/
Thank You!

Learn Apache Spark: A Comprehensive Guide

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Learn Apache Spark: A Comprehensive Guide