Learn Apache Spark and Scala Programming with Hadoop

Though Hadoop had established itself in the market, there were certain limitations associated with it. In the case of Hadoop, data was processed in various batches and, therefore, real-time data analytics was not enabled with Hadoop. As an added component of Hadoop, Apache Spark allows real-time data analytics including data streaming. For this reason, Apache Spark has become quite popular these days. The average salary of a data scientist who uses Apache Spark is around US$100,000.

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Apache Spark is a new processing engine which is part of the Apache Software Foundation that is powering the Big Data applications around the world. It is taking over from where Hadoop MapReduce gave up or from where MapReduce started finding it increasingly difficult to cope with the exacting needs of a fast-paced enterprise.

Businesses today are struggling to find an edge and get new opportunities or practices that drive innovation and collaboration. Large amounts of unstructured data and the need for increased speed to fulfill the real-time analytics have made this technology a real alternative for Big Data computational exercises. So let’s begin with this Apache Spark Tutorial.

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Evolution of Apache Spark

Before Spark, there was MapReduce which was used as a processing framework. Initially, Spark was started as one of the research projects in 2009 at UC Berkeley AMPLab. It was later open sourced in 2010. The major intention behind this project was to create a cluster management framework that supports various computing systems based on clusters. After its release to the market, Spark grew and moved to the Apache Software Foundation in 2013. Now, most of the organizations across the world have incorporated Apache Spark for empowering their Big Data applications.

What does Spark do?

Now, in this Apache Spark tutorial, we will see what does apache spark do? Spark has the capacity to handle zetta and yottabytes of data at the same time it is distributed across various servers (physical or virtual). It has a comprehensive level of APIs and developer libraries, supporting various languages like Python, Scala, Java, R, etc. It is mostly utilized in combination with distributed data stores like Hadoop’s HDFS, Amazon’s S3, and MapR-XD. And, it also used with NoSQL databases like Apache HBase, MapR-DB, MongoDB, and Apache Cassandra. Sometimes, it is also used with distributed messaging stores like Apache Kafka and MapR-ES.

Spark takes the programs that are written in complex languages and distributes to many machines. This is achieved based on an API like datasets and dataframes built upon Resilient Distributed Datasets (RDDs).

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What is Apache Spark used for?

Today, there is widespread deployment of Big Data. With each passing day the requirements of enterprises increase, and therefore there is a need for a faster and more efficient form of data processing. Most of the data is in an unstructured format, coming in thick and fast as streaming data. Here in this Apache Spark tutorial, we look at different sectors and how spark is used there.

Banking: More and more banks are increasingly adopting Spark platforms to analyze and access social media profiles, emails, call recordings, complaint logs, and forum discussions to garner insights which can aid them to take correct business decisions for credit risk assessment, customer segmentation, and targeted advertising.

E-commerce: Spark finds a great application in the e-commerce industry. Real-time transaction details can be sent to streaming clustering algorithms like K-means and collaborative filtering. The results can then be combined with other data sources like product reviews, social media profiles, and customer comments to offer recommendations to clients based on new trends.

Alibaba Taobao uses Spark to analyze hundreds of petabytes of data on its e-commerce platform. A plethora of merchants interact with this e-commerce platform. These interactions represent a large graph and Machine Learning processing on this data.

eBay uses Apache Spark to provide targeted offers, enhance customer experience, and optimize overall performance. Apache Spark engine is leveraged at eBay through Hadoop YARN.YARN manages all the cluster resources to run generic tasks. eBay Spark users leverage Hadoop clusters in the range of 2000 nodes, 20,000 cores, and 100TB of RAM through YARN.

Healthcare: Apache Spark uses advanced analytics on patient records to figure out which patients are more likely to fall sick after being discharged. The hospital can better deploy healthcare services to the identified patients saving on costs for both hospitals and patients.

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Many gaming companies use Apache Spark for finding patterns from their real-time in-game events. With this, they can derive further business opportunities like adjusting the game level automatically according to the complexity of the game level, targeted marketing, player retention, etc. Some media companies like Yahoo uses Apache Spark for targeted marketing, customizing news pages based on readers’ interests. They use tools such as Machine Learning algorithms for identifying the ‘readers’ interests’ category. Eventually, they categorize such news stories in various sections and keep the reader updated on timely bases.

Many people land up to travel planners to make their vacation a perfect one. And these travel companies depend on Apache Spark for offering various travel packages. TripAdvisor is one such company that uses Apache Spark to compare different travel packages from different providers. It scans through hundreds of websites to find the best and reasonable hotel price, trip package, etc.

Prepare yourself for the industry by going through these Top Hadoop Interview Questions and Answers now!

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Who can use Apache Spark?

An extensive range of technology-based companies across the globe has moved toward Apache Spark. They were quick enough to identify the real value possessed by Spark such as Machine Learning and interactive querying. Industry leaders such as Huawei and IBM have adopted Apache Spark. The firms which were based on Hadoop, such as Hortonworks, Cloudera, and MapR, have moved to Apache Spark, already.

Apache Spark can be mastered by professionals who are in the IT domain in order to increase their marketability. Big Data Hadoop professionals surely need to learn Apache Spark since it is the next most important technology in Hadoop processing. Moreover, even ETL professionals, SQL professionals, and project managers can gain immensely if they master Apache Spark. Finally, Data Scientists also need to gain in-depth knowledge of Spark to excel in their careers. So learn Spark through this Apache Spark tutorial.

Spark is extensively deployed in Machine Learning scenarios. Data Scientists are also expected to work in the Machine Learning domain, and hence they are the right candidates for Apache Spark training. Those who have an innate desire to learn the latest emerging technologies can also learn Spark through this Apache Spark tutorial.

There are multiple reasons to choose Apache Spark, out of which the most significant ones are given below:

For large-scale processing of data, Spark is 100 times faster than Hadoop, regardless of the fact that data is stored in memory or on disk. Even if the data is stored on disk, Spark will be performing faster. Spark has a world record in on-disk sorting for large-scale data.

Ease of Use
Spark has a crystal-clear and declarative approach toward a cluster of datasets. It has a collection of operators for data transformation, APIs specific to the dataset domain, or dataframes to manipulate semi-structured and structured data. Spark also has a single-entry point for applications.

Spark is designed in such a way that it can be easily accessible just by rich APIs. It is specially designed for quick and easy interaction in large data scale. APIs are well-documented for application developers and Data Scientists to instantly start working on Spark.

As mentioned earlier, Spark programming is at very easy. Spark supports too many programming languages like Python, Scala, Java, R, etc. Through Spark progamming, it also integrates with other storage solutions based on Hadoop ecosystem, such as MapR, Apache Cassandra, Apache HBase, and Apache Hadoop (HDFS).

This tutorial gives you an insightful introduction to Apache Spark. Spark Hadoop and Spark Scala are interlinked in this tutorial, and they are compared both at various fronts. Now, we will be learning Spark in detail in the coming sessions of this tutorial. In the next section, we will discuss Apache Spark features.

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Table of Contents

Spark Features

Key features of Spark

Developed in AMPLab of University of California, Berkeley, Apache Spark was developed for higher speed, ease of use and more in-depth analysis. Though it was built to be installed on top of Hadoop cluster, however its ability to parallel processing allows it run independently as well. Let's take a closer look at the features of Apache Read More

Apache Spark Architecture

Two Main Abstractions of Apache Spark

Apache Spark has a well-defined layer architecture which is designed on two main abstractions Resilient Distributed Dataset (RDD): RDD is an immutable (read-only), fundamental collection of elements or items that can be operated on many devices at the same time (parallel processing). Each dataset in an RDD can be divided into logical portions, which Read More

Apache Spark Applications

Applications on Apache Spark

Since the time of its inception in 2009 and its conversion to an open source technology, Apache Spark has taken the big data world by storm. It became one of the largest open source communities that includes over 200 contributors. The prime reason behind its success was its ability to process heavy data faster than ever Read More

Downloading Spark and Getting Started

Steps to install Spark

Step 1 : Ensure if Java is installed Before installing Spark, Java is a must have for your system. Following command will verify the version of Java- $java -version If Java is already installed on your system, you get to see the following output which is as follows: java version "1.7.0_71" Java(TM) SE Runtime Environment (build Read More

Spark Components

Introduction to Spark Components

The following procedure gives the clear picture of the different Spark components. Apache Spark Core Spark Core consists of general execution engine for spark platform that all required by other functionality which is built upon as per the requirement approach. It provides in-built memory computing and referencing datasets stored in external storage systems. Fast-track your career Read More

Programming with RDD in Spark

Resilient Distributed Datasets (RDDs)

RDDs are the main logical data unit in Spark. They are a distributed collection of objects, which are stored in memory or on disks of different machines of a cluster. A single RDD can be divided into multiple logical partitions so that these partitions can be stored and processed on different machines of a cluster. RDDs Read More

Working with Key/Value Pairs

7Using RDD in Spark

Motivation Spark provides special type of operations on RDDs containing key or value pairs. These RDDs are called pair RDDs operations. Pair RDDs are a useful building block in many programming language, as they expose operations that allow you to act on each key operations in parallel or regroup data across the network. Creating Pair RDDs Read More

Spark Dataframe

What is Spark Dataframe?

In Spark, Dataframes are distributed collections of data, organized into rows and columns. Each column in a Dataframe has a name and an associated type. Dataframes are similar to traditional database tables, which are structured and concise. We can say that, Dataframes are relational databases with better optimization techniques. Spark Dataframes can be created from various Read More

Loading and Saving your Data

Loading and Saving Data in Spark

File Formats : Spark provides a very simple manner to load and save data files in a very large number of file formats. Formats may range the formats from being the unstructured, like text, to semi structured way, like JSON, to structured, like Sequence Files. The input file formats that Spark wraps all are transparently Read More

Spark SQL

Why Did Spark SQL Come into the Picture?

Spark SQL is one of the main component of the Apache Spark Framework. It is mainly used for structured data processing. It provides various Application Programmable Interfaces (APIs) in Python, Java, Scala, and R. Spark SQL integrates relational data processing with the functional programming API of Spark. It provides a programming abstraction called Dataframe and can also act as a Read More

What is Pyspark? – Apache Spark with Python

Pyspark - Apache Spark with Python

Being able to analyse huge data sets is one of the most valuable technological skills these days and this tutorial will bring you up to speed on one of the most used technologies, Apache Spark, combined with one of the most popular programming languages, Python, to do just that. In this what is PySpark tutorial,  we Read More

Spark and RDD Cheat Sheet

Spark and RDD User Handbook

Are you a programmer looking for in-memory computation on large clusters? If yes, then you must take Spark into your consideration. This Spark and RDD cheat sheet is designed for the one who has already started learning about the memory management and using Spark as a tool, then this sheet will be handy reference sheet. Read More

Machine Learning with Pyspark Tutorial

Introduction to Spark MLlib

Apache Spark comes with a library named MLlib to perform machine learning tasks using spark framework. Since we have a Python API for Apache spark, that is, as you already know, PySpark, we can also use this spark ml library in PySpark. MLlib contains many algorithms and machine learning utilities. Watch this Apache Spark for beginners video by Read More

PySpark SQL Cheat Sheet

PySpark SQL User Handbook

Are you a programmer looking for a powerful tool to work on Spark? If yes, then you must take PySpark SQL into consideration. This PySpark SQL cheat sheet is designed for the one who has already started learning about the Spark and using PySpark SQL as a tool, then this sheet will be handy reference. Don't Read More


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