Big Data and Hadoop for Beginners – with Hands-on!
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 6 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.
Practice: Practice with huge Data Sets. Learn Design and Optimization Techniques by designing Data Models, Data Pipelines by using real life applications’ data sets.
Check out some of our reviews from real students:-
“A nice learning for beginners, the thing which differentiate this course from other similar courses is that it has very “effective and concise” content, so do even a layman can understand easily. The course shows only 3 hours of on-demand video lecture but one should always give time to each lecture ( by means of bookmarks and pause), then you would able to understand all the basics of Big data and Hadoop.”
“I liked the hands-on approach. very helpful.”
“Overall definitely worth the money for what you get, I learnt so much about Big Data.”
“I absolutely recommend taking this course.”
“Presenter explains in simple terms and any lay person or someone like me who has no background about databases and data can understand. Explaining the business use case application us very helpful in understanding how this can be useful for everyday business.”
“Loved it. Saved lots of time searching information on the internet.”
“Very informative, and the course gave me what I was looking for. Thanks!”
“Big Data introduction can be daunting with several new keywords and components that one needs to understand. But, this course very clearly explains to a beginner about the architecture and different tools that can be leveraged in a big data project. It also has indications on the scope of big data in the industry, different roles one can perform in the big data space and also cover various commercial distributions of big data. Overall, a great course for a beginner to get started on the fundamentals of big data. Use Case is a bonus !”
Welcome to the Course
a brief introduction about the course, and what you need to get started.
Big Data at a Glance
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.
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.
Understand salaries trend across different job roles in Big Data.
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.
Being a beginner, this lecture talks about how you should start with Big Data, and how you should proceed.
Getting Started with Hadoop
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.
In this lecture, we will understand what are the components in Hadoop Ecosystem, and how they work with each other.
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
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.
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.
a very simple and easy guide to install Hadoop on your machine (Mac/Windows/Others)
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.
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.
Getting Started with Hive
We will briefly cover how Hive is used to process large volumes of data, and how it works.
a deep dive into Hive where we will learn about Hive architecture, and how it works internally.
Understand Hive Data Models with detailed explanations which you would need to know when you start working with Hive.
We will briefly cover about different file formats that Hive understand and comparison between them.
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.
Understand how we can build custom functions in Hive to process huge volumes of data.
A very nice demo on Hive to understand how Hive works on top of Hadoop. Different exercises for you to play with Hive.
Getting Started with Pig
A very high level of introduction to Pig built on top of Hadoop to process huge volumes of data.
Deep dive into Pig Architecture..
Learn about Data Models in Pig which you would need to know when you are starting to work with Pig.
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.
Understand Similarities and Differences between SQL and Pig Latin.
In this lecture, we will learn what UDF is, and how it can be used to design custom functions to process Big data.
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.
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.
Understand how various organizations are adopting modern data architecture (i.e. Data Lake) on their productions.
In this exercise we will be analyzing Taxi Trips data by designing a Data Warehouse using Hive. There will be Billions of rows in the tables to analyze. By doing this exercise, you will be learning:
- Designing Optimized Data Model in Hive
- Query Optimization Techniques
- ETL process to load data into Dimension and Fact Tables
- Automated Data Pipeline Techniques and much more..
In this section we will learn about designing and developing UDF in Hive