Spark Introduction Course Overview

The word "Spark" refers to the process of speeding up the Hadoop computational computing software process.

Spark performs its cluster management and is not dependent on Hadoop and uses Hadoop only for storage purpose. Memory cluster computing is one of the main features of Spark and increases the processing speed in any application.

Iterative algorithms, streaming, and interactive queries are handled by Spark quickly. Spark also reduces the burden of managing and maintaining separate tools in addition to supporting workload.

Spark runs on Hadoop, standalone, Mesos or in the cloud. Spark allows accessing diverse data sources including HDFS, Cassandra, HBase, and S3. Spark also runs in standalone cluster mode, on EC2, on Hadoop YARN, or on Apache Mesos.

Spark is a data processing framework that enables Real-time processing data and provides MLib for processing Machine learning algorithms. Spark Datasets represent distributed collections of data created from a variety of sources like JSON and XML files, using Hive tables and external databases. Spark datasets are equivalent to a table in a relational database or a DataFrame in R or Python.

What will you learn at Spark Basics?

During this course, you will learn to:

Learn to use Spark components
Utilize the performance and speed of iterative algorithms
Identify the memory cluster computing methods
Handle wide range of data processing
Understand the concepts of Hadoop, Mesos, Cassandra and HBase or S3

Why get enrolled in this Spark Essentials Course?

Enroll in this course to:

Gain knowledge of Spark components
Understand the fast working method of Spark
 Handle large volume of data processing
Learn the Spark cluster components
Identify the concepts of Hadoop, Mesos, Cassandra and HBase or S3

Spark Foundation Course Offerings?

Live/Virtual Training in the presence of online instructors 
Quick look at Course Details, Contents, and Demo Videos
Quality Training Manuals for easy understanding
Anytime access to Reference materials
Gain your Course Completion Certificate on the Topic
Guaranteed high pay jobs after completing certification

Apache Spark Beginners Course Benefits

Learn about the performance of Spark for iterative algorithms or interactive data mining.
Identify the method by which Spark provides in-memory cluster computing for lightning fast speed
Understand the necessary support of Java, Python, R, and Scala APIs for ease of development by Spark
Learn the method to handle a broad range of data processing use cases
Understand how Spark combines SQL, streaming and complex analytics together in any application.
Gain knowledge on the method by which Spark runs on top of Hadoop, Mesos, or in the cloud.
Use Spark to access diverse data sources such as HDFS, Cassandra, HBase, or S3.

Audience

Any audience interested in learning Spark
Software engineers
Application developers
System Administrators
Data Analysts and Scientists

Prerequisite for learning Spark Fundamentals

Basic understanding of Hadoop and Big Data
Basic knowledge of Linux Operating System  
Basic idea of the Scala, Python, R, or Java programming languages

Spark Fundamentals Course Content

Lesson 1:  Introduction

This chapter introduces you to Spark that provides higher speed, easy use, and in-depth analysis. Also, you will learn about the Spark components like Spark SQL, Spark Streaming, MLib and GraphX

Class 1.1:

Spark Definition

Purpose of Spark

Class 1.2:

Components of Spark
Resilient Distributed Dataset

Lesson 2: Installing Spark

Download and Install Spark
Overview and installation of Java
Overview and installation of Scala
Overview and installation of Python
Using Spark's Scala and Python

Lesson 3: About Resilient Distributed Dataset and DataFrames

Resilient Distributed Dataset (RDD) are very efficient and help in implementing iterative algorithms or the repeated database querying of data.

DataFrames are the data abstraction that supports structured and semi-structured data. Using the domain specific language (DSL) provided by Spark SQL, you can manipulate DataFrames in Scala, Java or Python.

Class 3.1:

Learn to create parallelized collections
Learn to create external datasets

Class 3.2:

Operations on Resilient Distributed Dataset
Use shared variables and key value pairs

Lesson 4: Spark Application Programming

This lesson teaches the procedure to use Spark context, pass functions and initialize Spark with various programming languages.

Class 4.1:

Understand purpose of Spark
Learn to use Spark Context

Class 4.2:

Initialize Spark with the different programming languages
Run Spark examples
Pass functions to Spark

Class 4.3:

Create and run a Spark standalone application
Submit applications to the cluster

Lesson 5: All about Spark Libraries

This session makes you understand the Spark Libraries which are common machine learning algorithms that include regression, clustering, and optimization primitives.

Class 5.1:

Learn Spark Libraries
Use the Spark Libraries

Lesson 6: Spark configuration, monitoring, and tuning

Spark configuration teaches you to configure three main locations provided by the system; they are Spark properties, environment variables, and logging

Class 6.1:

Understand the Spark cluster components
Configure Spark to modify

a) the Spark properties
b) environmental variables, or logging properties

Class 6.2:

Use the web UIs, metrics, and external instrumentation to monitor Spark
Understand performance tuning considerations

FAQs

Why should I enroll in this certificate course?

Enroll in this course to gain knowledge of Spark which is having tremendous growth in the market. Spark deployment is increasing because of its excellent performance and support for iterative algorithms.

What is Spark?

Spark is an open source programming that allows programmers to maintain data structure using the Resilient Distributed Dataset and supports clustering.

What are the main advantages of Spark?

The features of Spark are as follows:

Fast processing using RDD
Flexibility by supporting many languages like Java, Python, Scala or R
In memory computing by storing data in the RAM of servers
Real time processing and
Compatible with Hadoop

What is meant by Scala?

Scala is a modern and powerful language that supports functional programming. Scala source code is compiled to Java bytecode so that the executable output code runs on a Java virtual machine.

How does Spark use Hadoop?

Spark uses Hadoop for storage mainly has Spark own its cluster management computation.