Apache Spark Installation Apache Spark in 10 Minutes | Distributed Environment | Learn Apache Spark

Must visit:

sudo apt update

sudo apt -y upgrade

[ -f /var/run/reboot-required ] && sudo reboot -f

sudo apt install curl mlocate default-jdk -y

//check java version and installation
java -version

sudo mv spark-3.2.1-bin-hadoop3.2/ /opt/spark

//setting spark envoirment
nano ~/.bashrc

// add these to config
export SPARK_HOME=/opt/spark
export PATH=$PATH:$SPARK_HOME/bin:$SPARK_HOME/sbin

//activate the changes
source ~/.bashrc

root access==sudo su -

Apache Spark is a framework used in cluster computing environments for analyzing big data. This platform became widely popular due to its ease of use and the improved data processing speeds over Hadoop.

Apache Spark is able to distribute a workload across a group of computers in a cluster to more effectively process large sets of data. This open-source engine supports a wide array of programming languages. This includes Java, Scala, Python, and R.

In this tutorial, you will learn how to install Spark on an Ubuntu machine. The guide will show you how to start a master and slave server and how to load Scala and Python shells. It also provides the most important Spark commands.

Tutorial on how to install Spark on an Ubuntu machine.
Prerequisites

An Ubuntu system.
Access to a terminal or command line.
A user with sudo or root permissions.

Spark & its Features
Apache Spark is an open source cluster computing framework for real-time data processing. The main feature of Apache Spark is its in-memory cluster computing that increases the processing speed of an application. Spark provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. It is designed to cover a wide range of workloads such as batch applications, iterative algorithms, interactive queries, and streaming.

Features of Apache Spark:
Spark Features- Spark Architecture-Edureka
Fig: Features of Spark
Speed
Spark runs up to 100 times faster than Hadoop MapReduce for large-scale data processing. It is also able to achieve this speed through controlled partitioning.
Powerful Caching
Simple programming layer provides powerful caching and disk persistence capabilities.
Deployment
It can be deployed through Mesos, Hadoop via YARN, or Spark’s own cluster manager.
Real-Time
It offers Real-time computation & low latency because of in-memory computation.
Polyglot
Spark provides high-level APIs in Java, Scala, Python, and R. Spark code can be written in any of these four languages. It also provides a shell in Scala and Python.
Spark Architecture Overview
Apache Spark has a well-defined layered architecture where all the spark components and layers are loosely coupled. This architecture is further integrated with various extensions and libraries. Apache Spark Architecture is based on two main abstractions:

Resilient Distributed Dataset (RDD)
Directed Acyclic Graph (DAG)
Spark Architecture _ Edureka
Fig: Spark Architecture
But before diving any deeper into the Spark architecture, let me explain few fundamental concepts of Spark like Spark Eco-system and RDD. This will help you in gaining better insights.

Let me first explain what is Spark Eco-System.

Spark Eco-System
As you can see from the below image, the spark ecosystem is composed of various components like Spark SQL, Spark Streaming, MLlib, GraphX, and the Core API component.
apache spark - apache spark - computerphile. 08:27 components of apache spark. you will master essential skills of the apache spark open source framework and the scala programming language including spark streaming spark sql machine learning programming graphx programming and shell scripting spark.

after completing the apache spark and scala training you will be able to:..

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