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How to Install Spark on Ubuntu

Apache Spark is a fast and general-purpose cluster computing system. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming.In this article, we will cover the installation procedure of Apache Spark on the Ubuntu operating system.PrerequisitesThis guide assumes that you are using Ubuntu and Hadoop 2.7 is installed in your system.Java8 should be installed in your Machine.Hadoop should be installed in your Machine.System requirementsUbuntu OS Installed.Minimum of 8 GB RAM.At least 20 GB free space.Installation ProcedureMaking system readyBefore installing Spark ensure that you have installed Java8 in your Ubuntu Machine. If not installed, please follow below process to install java8 in your Ubuntu System.a. Install java8 using below command.sudo apt-get install oracle-java8-installerAbove command creates java-8-oracle Directory in /usr/lib/jvm/ directory in your machine. It looks like belowNow we need to configure the JAVA_HOME path in .bashrc file..bashrc file executes whenever we open the terminal.b. Configure JAVA_HOME and PATH  in .bashrc file and save. To edit/modify .bashrc file, use below command.vi .bashrc Then press i(for insert) -> then Enter below line at the bottom of the file.export JAVA_HOME= /usr/lib/jvm/java-8-oracle/ export PATH=$PATH:$JAVA_HOME/binBelow is the screen shot of that.Then Press Esc -> wq! (For save the changes) -> Enter.c. Now test Java installed properly or not by checking the version of Java. Below command should show the java version.java -versionBelow is the screenshotInstalling Spark on the SystemGo to the below official download page of Apache Spark and choose the latest release. For the package type, choose ‘Pre-built for Apache Hadoop’.https://spark.apache.org/downloads.htmlThe page will look like belowOr You can use a direct link to download.https://www.apache.org/dyn/closer.lua/spark/spark-2.4.0/spark-2.4.0-bin-hadoop2.7.tgzCreating Spark directoryCreate a directory called spark under /usr/ directory. Use below command to create spark directorysudo mkdir /usr/sparkAbove command asks password to create spark directory under the /usr directory, you can give the password. Then check spark directory is created or not in the /usr directory using below commandll /usr/It should give the below results with ‘spark’ directoryGo to /usr/spark directory. Use below command to go spark directory.cd /usr/sparkDownload Spark versionDownload spark2.3.3 in spark directory using below commandwget https://www.apache.org/dyn/closer.lua/spark/spark-2.4.0/spark-2.4.0-bin-hadoop2.7.tgzIf use ll or ls command, you can see spark-2.4.0-bin-hadoop2.7.tgz in spark directory.Extract Spark fileThen extract spark-2.4.0-bin-hadoop2.7.tgz using below command.sudo tar xvzf spark-2.4.0-bin-hadoop2.7Now spark-2.4.0-bin-hadoop2.7.tgz file is extracted as spark-2.4.0-bin-hadoop2.7Check whether it extracted or not using ll command. It should give the below results.ConfigurationConfigure SPARK_HOME path in the .bashrc file by following below steps.Go to the home directory using below commandcd ~Open the .bashrc file using below commandvi .bashrcNow we will configure SPARK_HOME and PATHpress i for insert the enter SPARK_HOME and PATH  like belowSPARK_HOME=/usr/spark/spark-2.4.0-bin-hadoop2.7PATH=$PATH:$SPARK_HOME/binIt looks like belowThen save and exit by entering below commands.Press Esc -> wq! -> EnterTest Installation:Now we can verify spark is successfully installed in our Ubuntu Machine or not. To verify use below command then enter.spark-shell Above command should show below screenNow we have successfully installed spark on Ubuntu System. Let’s create RDD and Dataframe then we will end up.a. We can create RDD in 3 ways, we will use one way to create RDD.Define any list then parallelize it. It will create RDD. Below are the codes. Copy paste it one by one on the command line.val nums = Array(1,2,3,5,6) val rdd = sc.parallelize(nums)Above will create RDD.b. Now we will create a Data frame from RDD. Follow the below steps to create Dataframe.import spark.implicits._ val df = rdd.toDF("num")Above code will create Dataframe with num as a column.To display the data in Dataframe use below commanddf.show()Below is the screenshot of the above code.How to uninstall Spark from Ubuntu System: You can follow the below steps to uninstall spark on Windows 10.Remove SPARK_HOME from the .bashrc file.To remove SPARK_HOME variable from the .bashrc please follow below stepsGo to the home directory. To go to home directory use below command.cd ~Open .bashrc file. To open .bashrc file use below command.vi .bashrcPress i for edit/delete SPARK_HOME from .bashrc file. Then find SPARK_HOME the delete SPARK_HOME=/usr/spark/spark-2.4.0-bin-hadoop2.7 line from .bashrc file and save. To do follow below commandsThen press Esc -> wq! -> Press EnterWe will also delete downloaded and extracted spark installers from the system. Please do follow below command.rm -r ~/sparkAbove command will delete spark directory from the system.Open Command Line Interface then type spark-shell,  then press enter, now we get an error.Now we can confirm that Spark is successfully uninstalled from the Ubuntu System. You can also learn more about Apache Spark and Scala here.
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How to Install Spark on Ubuntu

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How to Install Spark on Ubuntu

Apache Spark is a fast and general-purpose cluster computing system. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming.

In this article, we will cover the installation procedure of Apache Spark on the Ubuntu operating system.

Prerequisites

This guide assumes that you are using Ubuntu and Hadoop 2.7 is installed in your system.

  1. Java8 should be installed in your Machine.
  2. Hadoop should be installed in your Machine.

System requirementsSystem Requirements of Install Spark on Ubuntu

  • Ubuntu OS Installed.
  • Minimum of 8 GB RAM.
  • At least 20 GB free space.

Installation Procedure

Making system ready

Before installing Spark ensure that you have installed Java8 in your Ubuntu Machine. If not installed, please follow below process to install java8 in your Ubuntu System.

a. Install java8 using below command.

sudo apt-get install oracle-java8-installer

Above command creates java-8-oracle Directory in /usr/lib/jvm/ directory in your machine. It looks like below

Installation Procedure in Spark on ubuntu

Now we need to configure the JAVA_HOME path in .bashrc file.

.bashrc file executes whenever we open the terminal.

b. Configure JAVA_HOME and PATH  in .bashrc file and save. To edit/modify .bashrc file, use below command.

vi .bashrc 

Then press i(for insert) -> then Enter below line at the bottom of the file.

export JAVA_HOME= /usr/lib/jvm/java-8-oracle/
export PATH=$PATH:$JAVA_HOME/bin

Below is the screen shot of that.

Installation Procedure in Spark on ubuntu

Then Press Esc -> wq! (For save the changes) -> Enter.

c. Now test Java installed properly or not by checking the version of Java. Below command should show the java version.

java -version

Below is the screenshot

Installation Procedure in Spark on ubuntu

Installing Spark on the System

Go to the below official download page of Apache Spark and choose the latest release. For the package type, choose ‘Pre-built for Apache Hadoop’.

https://spark.apache.org/downloads.html

The page will look like below

Installation Procedure in Spark on ubuntu

Or You can use a direct link to download.

https://www.apache.org/dyn/closer.lua/spark/spark-2.4.0/spark-2.4.0-bin-hadoop2.7.tgz

Creating Spark directory

Create a directory called spark under /usr/ directory. Use below command to create spark directory

sudo mkdir /usr/spark

Above command asks password to create spark directory under the /usr directory, you can give the password. Then check spark directory is created or not in the /usr directory using below command

ll /usr/

It should give the below results with ‘spark’ directory

Go to /usr/spark directory. Use below command to go spark directory.

cd /usr/spark

Download Spark version

Download spark2.3.3 in spark directory using below command

wget https://www.apache.org/dyn/closer.lua/spark/spark-2.4.0/spark-2.4.0-bin-hadoop2.7.tgz

If use ll or ls command, you can see spark-2.4.0-bin-hadoop2.7.tgz in spark directory.

Extract Spark file

Then extract spark-2.4.0-bin-hadoop2.7.tgz using below command.

sudo tar xvzf spark-2.4.0-bin-hadoop2.7

Now spark-2.4.0-bin-hadoop2.7.tgz file is extracted as spark-2.4.0-bin-hadoop2.7

Check whether it extracted or not using ll command. It should give the below results.

Installation Procedure in Spark on ubuntu

Configuration

Configure SPARK_HOME path in the .bashrc file by following below steps.

Go to the home directory using below command

cd ~

Open the .bashrc file using below command

vi .bashrc

Now we will configure SPARK_HOME and PATH

press i for insert the enter SPARK_HOME and PATH  like below

SPARK_HOME=/usr/spark/spark-2.4.0-bin-hadoop2.7

PATH=$PATH:$SPARK_HOME/bin

It looks like below

Installation Procedure in Spark on ubuntu

Then save and exit by entering below commands.

Press Esc -> wq! -> Enter

Test Installation:

Now we can verify spark is successfully installed in our Ubuntu Machine or not. To verify use below command then enter.

spark-shell 

Above command should show below screen

Test Installation in Spark on Ubuntu

Now we have successfully installed spark on Ubuntu System. Let’s create RDD and Dataframe then we will end up.

a. We can create RDD in 3 ways, we will use one way to create RDD.

Define any list then parallelize it. It will create RDD. Below are the codes. Copy paste it one by one on the command line.

val nums = Array(1,2,3,5,6)
val rdd = sc.parallelize(nums)

Above will create RDD.

b. Now we will create a Data frame from RDD. Follow the below steps to create Dataframe.

import spark.implicits._
val df = rdd.toDF("num")

Above code will create Dataframe with num as a column.

To display the data in Dataframe use below command

df.show()

Below is the screenshot of the above code.

Test Installation in Spark on Ubuntu

How to uninstall Spark from Ubuntu System: 

You can follow the below steps to uninstall spark on Windows 10.

  1. Remove SPARK_HOME from the .bashrc file.

To remove SPARK_HOME variable from the .bashrc please follow below steps

Go to the home directory. To go to home directory use below command.

cd ~

Open .bashrc file. To open .bashrc file use below command.

vi .bashrc

Press i for edit/delete SPARK_HOME from .bashrc file. Then find SPARK_HOME the delete SPARK_HOME=/usr/spark/spark-2.4.0-bin-hadoop2.7 line from .bashrc file and save. To do follow below commands

Then press Esc -> wq! -> Press Enter

We will also delete downloaded and extracted spark installers from the system. Please do follow below command.

rm -r ~/spark

Above command will delete spark directory from the system.

Open Command Line Interface then type spark-shell,  then press enter, now we get an error.

Now we can confirm that Spark is successfully uninstalled from the Ubuntu System. You can also learn more about Apache Spark and Scala here.

Ravichandra

Ravichandra Reddy Maramreddy

Blog Author

Ravichandra is a developer and specialized in Spark and Hadoop Ecosystems, HDFS and MapReduce which includes estimations, requirement analysis, design development, coordination, validation in-depth understanding of game design practices. Having extensive experience in Spark, Spark Streaming, Pyspark, Scala, Shell, Oozie, Hive, HBase, Hue, Java, SparkSQL, Kafka, WSO2. Having extensive experience in using Data structures and algorithms.

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Travel and TourismFlight booking sites, IRCTC track the clicks and hits along with IP address, login information, and other details and as per demand can do dynamic pricing for the flights/ trains. Big Data helps in dynamic pricing and mind you it’s real time. Am sure each one of us has experienced this. Now you know who is doing it :DTelecommunications, Public sector, Education, Social media and gaming, Energy and utility every industry have implemented are implementing several of these Big Data use cases day in and day out. If you look around am sure you would find them on the rise.Big Data is helping everyone industries, consumers, clients to make informed decisions, whatever it may be and hence wherever there is such a need, Big Data can come handy.Challenges faced by Big Data in the real world for adaptationAlthough the world is going gaga about big data, there are still a few challenges to implement and adopt Big Data and hence service industries are still striving towards resolving those challenges to implement best Big Data solution without flaws.An October 2016 report from Gartner found that organizations were getting stuck at the pilot stage of their big data initiatives. "Only 15 percent of businesses reported deploying their big data project to production, effectively unchanged from last year (14 per cent)," the firm said.Let’s discuss a few of them to understand what are they?1. Understanding Big Data and answering Why for the organization one is working with.As I started the article saying there are many versions of Big Data and understanding real use cases for organization decision makers are working with is still a challenge. Everyone wants to ride on a wave but not knowing the right path is still a struggle. As every organization is unique thus its utmost important to answer ‘why big data’ for each organization. This remains a major challenge for decision makers to adapt to big data.2. Understanding Data sources for the organizationIn today’s world, there are hundreds and thousands of ways information is being generated and being aware of all these sources and ingest all of them into big data platforms to get accurate insight is essential. Identifying sources is a challenge to address.It's no surprise, then, that the IDG report found, "Managing unstructured data is growing as a challenge – rising from 31 per cent in 2015 to 45 per cent in 2016."Different tools and technologies are on the rise to address this challenge.3. Shortage if Big Data Talent and retaining themBig Data is changing technology and there are a whopping number of tools in the Big Data technology landscape. It is demanded out of Big Data professionals to excel in those current tools and keep up self to ever-changing needs. This gets difficult for employees and employers to create and retain talent within the organization.The solution to this would be constant upskilling, re-skilling and cross-skilling and increasing budget of organization for retaining talent and help them train.4. The Veracity V This V is a challenge as this V means inconsistent, incomplete data processing. To gain insights through big data model, the biggest step is to predict and fill missing information.This is a tricky part as filling missing information can lead to decreasing accuracy of insights/ analytics etc.To address this concern, there is a bunch of tools. Data curation is an important step in big data and should have a proper model. But also, to keep in mind that Big Data is never 100% accurate and one must deal with it.5. SecurityThis aspect is given low priority during the design and build phases of Big Data implementations and security loopholes can cost an organization and hence it’s essential to put security first while designing and developing Big Data solutions. Also, equally important to act responsibly for implementations for regulatory requirements like GDPR.  6. Gaining Valuable InsightsMachine learning data models go through multiple iterations to conclude on insights as they also face issues like missing data and hence the accuracy. To increase accuracy, lots of re-processing is required, which has its own lifecycle. Increasing accuracy of insights is a challenge and which relates to missing data piece. Which most likely can be addressed by addressing missing data challenge.This can also be caused due to unavailability of information from all data sources. Incomplete information would lead to incomplete insights which may not benefit to required potential.Addressing these discussed challenges would help to gain valuable insights through available solutions.With Big Data, the opportunities are endless. Once understood, the world is yours!!!!Also, now that you understand BIG DATA, it's worth understanding the next steps:Gary King, who is a professor at Harvard says “Big data is not about the data. It is about the analytics”You can also take up Big Data and Hadoop training to enhance your skills furthermore.Did the article helps you to understand today’s massive world of big data and getting a sneak peek into it Do let us know through the comment section below?
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What is Big Data — An Introductory Guide

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5 Big Data Challenges in 2020

The year 2019 saw some enthralling changes in volume and variety of data across businesses, worldwide. The surge in data generation is only going to continue. Foresighted enterprises are the ones who will be able to leverage this data for maximum profitability through data processing and handling techniques. With the rise in opportunities related to Big Data, challenges are also bound to increase.Below are the 5 major Big Data challenges that enterprises face in 2020:1. The Need for More Trained ProfessionalsResearch shows that since 2018, 2.5 quintillion bytes (or 2.5 exabytes) of information is being generated every day. The previous two years have seen significantly more noteworthy increments in the quantity of streams, posts, searches and writings, which have cumulatively produced an enormous amount of data. Additionally, this number is only growing by the day. A study has predicted that by 2025, each person will be making a bewildering 463 exabytes of information every day.A report by Indeed, showed a 29 percent surge in the demand for data scientists yearly and a 344 percent increase since 2013 till date. However, the searches by job seekers skilled in data science continue to grow at a snail’s pace at 14 percent. In August 2018, LinkedIn reported claimed that US alone needs 151,717 professionals with data science skills. This along with a 15 percent discrepancy between job postings and job searches on Indeed, makes it quite evident that the demand for data scientists outstrips supply. The greatest data processing challenge of 2020 is the lack of qualified data scientists with the skill set and expertise to handle this gigantic volume of data.2. Inability to process large volumes of dataOut of the 2.5 quintillion data produced, only 60 percent workers spend days on it to make sense of it. A major portion of raw data is usually irrelevant. And about 43 percent companies still struggle or aren’t fully satisfied with the filtered data. 3. Syncing Across Data SourcesOnce you import data into Big Data platforms you may also realize that data copies migrated from a wide range of sources on different rates and schedules can rapidly get out of the synchronization with the originating system. This implies two things, one, the data coming from one source is out of date when compared to another source. Two, it creates a commonality of data definitions, concepts, metadata and the like. The traditional data management and data warehouses, and the sequence of data transformation, extraction and migration- all arise a situation in which there are risks for data to become unsynchronized.4. Lack of adequate data governanceData collected from multiple sources should have some correlation to each other so that it can be considered usable by enterprises. In a recent Big Data Maturity Survey, the lack of stringent data governance was recognized the fastest-growing area of concern. Organizations often have to setup the right personnel, policies and technology to ensure that data governance is achieved. This itself could be a challenge for a lot of enterprises.5. Threat of compromised data securityWhile Big Data opens plenty of opportunities for organizations to grow their businesses, there’s an inherent risk of data security. Some of the biggest cyber threats to big players like Panera Bread, Facebook, Equifax and Marriot have brought to light the fact that literally no one is immune to cyberattacks. As far as Big Data is concerned, data security should be high on their priorities as most modern businesses are vulnerable to fake data generation, especially if cybercriminals have access to the database of a business. However, regulating access is one of the primary challenges for companies who frequently work with large sets of data. Even the way Big Data is designed makes it harder for enterprises to ensure data security. Working with data distributed across multiple systems makes it both cumbersome and risky.Overcoming Big Data challenges in 2020Whether it’s ensuring data governance and security or hiring skilled professionals, enterprises should leave no stone unturned when it comes to overcoming the above Big Data challenges. Several courses and online certifications are available to specialize in tackling each of these challenges in Big Data. Training existing personnel with the analytical tools of Big Data will help businesses unearth insightful data about customer. Frameworks related to Big Data can help in qualitative analysis of the raw information.
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5 Big Data Challenges in 2020

The year 2019 saw some enthralling changes in volu... Read More

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