Big Data and Hadoop for Beginners

Learn Big Data, Hadoop, Hive and Pig from scratch.
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  • Lectures 30
  • Contents Video: 3 hours
    Other: 13 mins
  • Skill Level All Levels
  • Languages English
  • Includes Lifetime access
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    Available on iOS and Android
    Certificate of Completion
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About This Course

Published 1/2016 English

Course Description

The main objective of this course is to help you understand Complex Architectures of Hadoop and its components, guide you in the right direction to start with, and quickly start working with Hadoop and its components.

It covers everything what you need as a Big Data Beginner. Learn about Big Data market, different job roles, technology trends, history of Hadoop, HDFS, Hadoop Ecosystem, Hive and Pig. In this course, we will see how as a beginner one should start with Hadoop. This course comes with a lot of hands-on examples which will help you learn Hadoop quickly.

The course have 5 sections, and focuses on the following topics:

Big Data at a Glance: Learn about Big Data and different job roles required in Big Data market. Know big data salary trends around the globe. Learn about hottest technologies and their trends in the market.

Getting Started with Hadoop: Understand Hadoop and its complex architecture. Learn Hadoop Ecosystem with simple examples. Know different versions of Hadoop (Hadoop 1.x vs Hadoop 2.x), different Hadoop Vendors in the market and Hadoop on Cloud. Understand how Hadoop uses ELT approach. Learn installing Hadoop on your machine. We will see running HDFS commands from command line to manage HDFS.

Getting Started with Hive: Understand what kind of problem Hive solves in Big Data. Learn its architectural design and working mechanism. Know data models in Hive, different file formats supported by Hive, Hive queries etc. We will see running queries in Hive.

Getting Started with Pig: Understand how Pig solves problems in Big Data. Learn its architectural design and working mechanism. Understand how Pig Latin works in Pig. You will understand the differences between SQL and Pig Latin. Demos on running different queries in Pig.

Use Cases: Real life applications of Hadoop is really important to better understand Hadoop and its components, hence we will be learning by designing a sample Data Pipeline in Hadoop to process big data. Also, understand how companies are adopting modern data architecture i.e. Data Lake in their data infrastructure.

What are the requirements?

  • Basics knowledge of SQL and RDBMS is required

What am I going to get from this course?

  • Understand different technology trends, salary trends, Big Data market and different job roles in Big Data
  • Understand what Hadoop is for, and how it works
  • Understand complex architectures of Hadoop and its component
  • Hadoop installation on your machine
  • Understand how MapReduce, Hive and Pig can be used to analyze big data sets
  • High quality documents
  • Demos: Running HDFS commands, Hive queries, Pig queries
  • Sample data sets and scripts (HDFS commands, Hive sample queries, Pig sample queries, Data Pipeline sample queries)
  • Start writing your own codes in Hive and Pig to process huge volumes of data
  • Design your own data pipeline using Pig and Hive
  • Understand modern data architecture: Data Lake

What is the target audience?

  • This course can be opted by anyone (students, developer, manager) who is interested to learn big data. This course assumes everyone as a beginner, and teaches all fundamentals of Big Data, Hadoop and its complex architecture.

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.

Curriculum

Section 1: Welcome to the Course
03:49

a brief introduction about the course, and what you need to get started.

Section 2: Big Data at a Glance
09:23

This high level introduction will help you understand what Big Data is for, how they are being generated, who are using it, and how we can use it.

06:30

This lecture discusses about different job roles required in the Big Data Industry. It will also help you understand what are the skills you need to have for a specific job role in Big Data.

02:55

Understand salaries trend across different job roles in Big Data.

06:30

Understand why Big Data is so disrupting. Learn what are the latest technology trends in the market, and how Big Data is playing an important role.

02:45

Being a beginner, this lecture talks about how you should start with Big Data, and how you should proceed.

Section 3: Getting Started with Hadoop
08:23

In this lecture, we will learn about history of Hadoop, Hadoop Data Storage engine and Hadoop Data Processing engine with a very nice and simple demo to understand how it works.

05:01

In this lecture, we will understand what are the components in Hadoop Ecosystem, and how they work with each other.

14:13

Here we will learn about  architecture of Hadoop, different versions of Hadoop ( i.e. Hadoop 1.x & Hadoop 2.x), and also we will understand what are the enhancements and improvements have been done in Hadoop 2.x with respect to Hadoop 1.x

03:19

Data Processing, Cleaning and Transformation are important parts when it comes to dealing with any amount of data. In this lecture, we will understand how Hadoop uses ELT approach in comparison with traditional ETL approach. 

04:20

There are various Hadoop distributions available by different vendors in the market. We will briefly cover about them, and understand how they are easy to use and move to production. 

13 pages

a very simple and easy guide to install Hadoop on your machine (Mac/Windows/Others)

09:09

We will learn important Hadoop commands to work with HDFS. This will help you when you will be doing some POCs or working on Production.

05:11

In this lecture, we will learn benefits of working with Hadoop on cloud, and how it is easy to install, manage and scale at Cloud.

Section 4: Getting Started with Hive
02:41

We will briefly cover how Hive is used to process large volumes of data, and how it works.

02:28

a deep dive into Hive where we will learn about Hive architecture, and how it works internally.

07:55

Understand Hive Data Models with detailed explanations which you would need to know when you start working with Hive.

04:40

We will briefly cover about different file formats that Hive understand and comparison between them.

03:46

Hive being a data warehouse solution built of top of Hadoop. In this lecture, we will learn about how Hive queries are similar to SQL, and how it is easy to write a query in Hive.

02:57

Understand how we can build custom functions in Hive to process huge volumes of data.

18:50

A very nice demo on Hive to understand how Hive works on top of Hadoop. Different exercises for you to play with Hive.

Section 5: Getting Started with Pig
02:57

A very high level of introduction to Pig built on top of Hadoop to process huge volumes of data.

01:39

Deep dive into Pig Architecture..

02:17

Learn about Data Models in Pig which you would need to know when you are starting to work with Pig.

02:57

We will cover about Pig Latin which is a Data Flow language in Pig which is used to design Data Pipelines to process big data.

05:32

Understand Similarities and Differences between SQL and Pig Latin.
 

03:26

In this lecture, we will learn what UDF is, and how it can be used to design custom functions to process Big data.

12:49

A very nice demo on Pig to understand how Pig is used to process huge volumes of data. A lot exercises for you to play with Pig.

Section 6: Use Cases
07:59

In this lecture, we will cover about real life applications of Pig and Hive. We will understand it by designing a Data Pipeline using them. 

05:24

Understand how various organizations are adopting modern data architecture (i.e. Data Lake) on their productions.

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Instructor Biography

Andalib Ansari, Big Data Consultant

Andalib Ansari is a Big Data consultant based out of Mumbai. He helps companies and people solve business problems using Big Data technologies. Also, one of his passion, to guide and train people on different Big Data tools and technologies.

He is having a very decent exposure of Big Data tools and technologies, and have worked with various clients, top level Mobile Network Operators (MNO), from Latin America and the US to solve different business problems for different use-cases, and designed optimized Data Pipelines using Big Data technologies on the cloud.

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