Big Data and Hadoop Tutorial

This is the introductory lesson of Big Data Hadoop tutorial, which is a part of ‘Big Data Hadoop and Spark Developer Certification course’ offered by Simplilearn.

In the next section, we will discuss the objectives of Big Data Hadoop Tutorial.

Objectives

After completion of this Big Data Hadoop Tutorial, you will be able to:

  • Master the concepts of Hadoop framework and its deployment in a cluster environment

  • Understand how the components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, HDFS, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark fits in with the data processing lifecycle

  • Learn to write complex MapReduce programs

  • Describe how to ingest data using Sqoop and Flume

  • Explain the process of distributed data using Spark

  • Learn about spark SQL, Graphx, Mlib

  • List the best practices for data storage

  • Explain how to model structured data as tables with Impala and Hive

Introduction to Big data

To most people, Big Data is a baffling tech term. If you mention Big Data, you could well be subjected to questions such as Is it a tool, or a product? Or Is Big Data only for big businesses? and many more such questions.

So, what is Big Data?

Today, the size or volume, complexity or variety, and the rate of growth or velocity of the data which organizations handle have reached such unbelievable levels that traditional processing and analytical tools fail to process.

Big Data is ever growing and cannot be determined concerning its size. What was considered as Big eight years ago, is no longer considered so.

For example Nokia, the telecom giant migrated to Hadoop to analyze 100 Terabytes of structured data and more than 500 Terabytes of semi-structured data.

The Hadoop Distributed File System data warehouse stored all the multi-structured data and processed data at a petabyte scale.

According to The Big Data Market report the Big Data market is expected to grow from USD 28.65 Billion in 2016 to USD 66.79 Billion by 2021.

The Big Data Hadoop Certification and Training from Simplilearn will prepare you for the Cloudera CCA175 exam. Of all the Hadoop distributions, Cloudera has the largest partner ecosystems.

This Big Data tutorial will give an overview of the course; its objectives, prerequisites, target audience and the value it will offer to you.

In the next section, we will focus on the benefits of this Hadoop Tutorial.

Benefits of Hadoop for Organizations

Hadoop is used to overcome challenges of Distributed Systems such as -

  • High chances of system failure

  • Limited bandwidth

  • High programming complexity

In the next section, we will discuss the prerequisites for taking the Big Data tutorial.

Apache Hadoop Prerequisites

There are no prerequisites for learning Apache Hadoop from this Big Data Hadoop tutorial. However, knowledge of Core Java and SQL is beneficial.

Let’s discuss who will benefit from this Big Data tutorial.

Target Audience of the Apache Hadoop Tutorial

The Apache Hadoop Tutorial offered by Simplilearn is ideal for:

  • Software Developers and Architects

  • Analytics Professionals

  • Senior IT professionals

  • Testing and Mainframe Professionals

  • Data Management Professionals

  • Business Intelligence Professionals

  • Project Managers

  • Aspiring Data Scientists

  • Graduates looking to build a career in Big Data Analytics

Let us take a look at the lessons covered in this Hadoop Tutorial.

Leszsons Covered in this Apache Hadoop Tutorial

There are total sixteen lessons covered in this Apache Hadoop Tutorial. The lessons are listed in the table below.

Lesson No

Chapter Name

What You’ll Learn

Lesson 1

Big Data and Hadoop Ecosystem

In this chapter, you will be able to:

  • Understand the concept of Big Data and its challenges

  • Explain what Hadoop is and how it addresses Big Data challenges

  • Describe the Hadoop ecosystem

Lesson 2

HDFS and YARN

In this chapter, you will be able to:

  • Explain Hadoop Distributed File System (HDFS)

  • Explain HDFS architecture and components

  • Describe YARN and its features

  • Explain YARN architecture

Lesson 3

MapReduce and Sqoop

In this chapter, you will be able to:

  • Explain MapReduce with examples

  • Explain Sqoop with examples

Lesson 4

Basics of Hive and Impala

In this chapter, you will be able to:

  • Identify the features of Hive and Impala

  • Understand the methods to interact with Hive and Impala

Lesson 5

Working with Hive and Impala

In this chapter, you will be able to:

  • Explain metastore

  • Define databases and tables

  • Describe data types in Hive

  • Explain data validation

  • Explain HCatalog and its uses

Lesson 6

Types of Data Formats

In this chapter, you will be able to:

  • Characterize different types of file formats

  • Explain data serialization

Lesson 7

Advanced Hive Concept and Data File Partitioning

In this chapter, you will be able to:

  • Improve query performance with concepts of data file partitioning

  • Define Hive Query Language (HiveQL)

  • Define ways in which HiveQL can be extended

Lesson 8

Apache Flume and HBase

In this chapter, you will be able to:

  • Explain  the meaning, extensibility, and components of Apache Flume

  • Explain the meaning, architecture, and components of HBase

Lesson 9

Pig

In this chapter, you will be able to:

  • Explain the basics of Pig

  • Explain Pig Architecture and Operations

Lesson 10

Basics of Apache Spark

In this chapter, you will be able to:

  • Describe the limitations of MapReduce in Hadoop

  • Compare the batch and real-time analytics

  • Explain Spark, it’s architecture, and its advantages

  • Understand Resilient Distributed Dataset Operations

  • Compare Spark with MapReduce

  • Understand functional programming in Spark

Lesson 11

RDDs in Spark

In this chapter, you will be able to:

  • Create RDDs from files and collections

  • Create RDDs based on whole records

  • List the data types supported by RDD

  • Apply single-RDD and multi-RDD transformations

Lesson 12

Implementation of Spark Applications

In this chapter, you will be able to:

  • Describe SparkContext and Spark Application Cluster options

  • List the steps to run Spark on Yarn

  • List the steps to execute Spark application

  • Explain dynamic resource allocation

  • Understand the process of configuring a Spark application

Lesson 13

Spark Parallel Processing

In this chapter, you will be able to:

  • Explain Spark Cluster

  • Explain Spark Partitions

Lesson 14

Spark RDD Optimization Techniques

In this chapter, you will be able to:

  • Explain the concept of RDD Lineage

  • Describe the features and storage levels of RDD Persistence

Lesson 15

Spark Algorithm

In this chapter, you will be able to:

  • Explain Spark Algorithm

  • Explain Graph-Parallel System

  • Describe Machine Learning

  • Explain the three C’s of Machine Learning

Lesson 16

Spark SQL

In this chapter, you will be able to:

  • Identify the features of Spark SQL

  • Explain Spark Streaming and the working of stateful operations

  • Understand transformation and checkpointing in DStreams

  • Describe the architecture and configuration of Zeppelin

  • Identify the importance of Kafka in Spark SQL

 

How about investing your time in Big Data Hadoop and Spark Developer Certification? Check out our Course Preview now! 

Conclusion

With this, we come to an end about what this Big Data Hadoop tutorial is. In the next chapter, we will discuss the Big Data Hadoop Ecosystem tutorial.

  • Disclaimer
  • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.

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