Last Update November 10, 2020: We improved the overall article.
Apache Spark is a powerful open-source distributed processing framework. Many professionals prefer to use Apache Spark for Big Data processing. It helps us to perform sophisticated analytics at a quicker pace efficiently.
One of the reasons for its popularity is because of its wide range of use and flexibility. You can easily use Apache Spark on the side with Python, R, SQL, Java, and Scala. The demand for Apache Spark will be higher in the future.
Why? According to the study, the business data volume doubles every 1.2 years.
There is currently a rise of IoT, and it will significantly contribute to the volume of data that we collect. We are going to collect data at a much quicker rate.
It will lead to an increase in the demand for Big Data platforms and professionals.
The Data Jobs reveals that Big Data professionals easily earn over $100k annually. That amount is going to increase in the future.
If you want to get into the Big Data space quickly, you can opt for Apache Spark Certification Training Course.
You will know everything that you need to know to get started with Apache Spark with well-structured training.
Things to Know before Installing Apache Spark Framework
It offers the simplest way to get a grasp of API. It is an excellent tool for interactive data analysis.
Spark Session is an advanced feature of Apache Spark via which we can combine HiveContext, SQLContext, and future StreamingContext.
It is an API, which enables you to access structured data through Spark SQL. We use this API for reading and storing both structured and semi-structured data into Spark SQL.
RDD a.k.a.Resilient Distributed Dataset (RDD) is the main Spark’s data structure. It consists of important distributed objects collections.
The developers categorize RDD dataset into logical partitions, and you can compute it on various nodes on the cluster. It can contain Java, Python, or Scala objects.
It is a distributed collected data. We can build the dataset from JVM objects and manipulate it with the help of functional transformations like a filter, map, flat map, and so on.
You can find this API on Java and Scala.
It is an organized dataset in named columns. It is similar to the table that you can see in the relational database. However, you can find a better optimization.
You can construct data frames from a vast array of sources like Hive tables, existing RDDs, structured data files, and so on.
Apache Spark operates in YARN/Mesos. It is a resource navigator and one of the crucial abilities of the second-generation Hadoop.
There is no need for pre-installation, and we can access it without root access. It combines Spark and Hadoop Stack.
Spark in Map Reduce (SIMR)
Follow this guide to smoothly install Apache Spark Framework?
The process of installing Apache Spark Framework can be difficult for you if you are a complete beginner to this framework. However, you do not need to worry about anything if you follow this guide in an ordered way.
1. Make sure you have Java installed in your system
Having Java in your computer is a condition for installing Apache Spark Framework. Run the command below to ensure if you have Java in your computer or not.
the command below to ensure if you have Java in your computer or not.
In case you already have Java in your system, you will see the following screen.
Not seeing the above screen means you need to install Java on your system.
2. Ensure you have Scala in your system
Here is one more prerequisite for installing Apache Spark Framework. Your system needs to have Scala installed. You need Scala language for the implementation of the framework. Verify your Scala installation via command below.
You will see the following screen if your system has Scala installed.
$ tar xvf scala-2.11.6.tgz
b. After the extraction, move the software files to the directory “(/usr/local/scala).” Follow this command below for this step:
$ su –
For the Password purpose:
# cd /home/Hadoop/Downloads/
# mv scala-2.11.6 /usr/local/scala
Create a path for scala with the help of the instruction below:
$ export PATH = $PATH:/usr/local/scala/bin
After the installation, you must verify the Scala once again. To verify Scala, you can head over to the point number 2 and follow the direction given at that point.
5. Time to download Apache Spark Framework in your system
The previous steps are the requirements that you must complete for downloading, installing, and running the Apache Spark Framework in your system. After completing the first four steps, you are now ready to download Apache Spark. Use the command given below for downloading the most recent version of Apache Spark.
After completing the above command, you can head to the download folder where you will find the Spark tar file.
6. Time for the installation
Below are the steps that you need to follow for installing Spark framework.
I. Extract Spark tar with the command below:
$ tar xvf spark-1.3.1-bin-hadoop2.6.tgz
II . Your next task is to use the command below for moving Spark software in the appropriate directory.
$ su -
# cd /home/Hadoop/Download
# mv spark-1.3.1-bin-hadoop2.6 /usr/local/spark
III. You should now configure the environment for the framework to operate smoothly
Insert the line “~/.bashrc “ for adding the location where you have installed the Spark framework to the PATH variable type.
Export PATH = $PATH:/usr/local/spark/bin
$ source ~/ .bashrc
7. Verification of the Apache Spark Installation
Use the command below for Spark shell application version.
You will see the output below if you have correctly installed Apache Spark in your system.
If you see the above screenshot, your job is complete. It is a time to relax for a while now.
After reading this article, you can go through it again to follow the step in a systematic manner to install Apache Spark framework without any trouble. There is nothing fancy that you need to do except to follow this guide for the installation process.
After installing the framework, you can play around with the framework. Like I said before, there are great courses that can help you master the framework.
For the time being, you can find some free resources on the internet to do some necessary processing on Apache Spark. Start using the framework after installing it without procrastination.
If you have any problems while following the tutorial, let me know in the comment section below. I will help you out with your issues.
Besides that, you can also share your experience with Big Data processing with some other frameworks other than Apache Spark.