Book Image

Big Data Analytics with Hadoop 3

By : Sridhar Alla
Book Image

Big Data Analytics with Hadoop 3

By: Sridhar Alla

Overview of this book

Apache Hadoop is the most popular platform for big data processing, and can be combined with a host of other big data tools to build powerful analytics solutions. Big Data Analytics with Hadoop 3 shows you how to do just that, by providing insights into the software as well as its benefits with the help of practical examples. Once you have taken a tour of Hadoop 3’s latest features, you will get an overview of HDFS, MapReduce, and YARN, and how they enable faster, more efficient big data processing. You will then move on to learning how to integrate Hadoop with the open source tools, such as Python and R, to analyze and visualize data and perform statistical computing on big data. As you get acquainted with all this, you will explore how to use Hadoop 3 with Apache Spark and Apache Flink for real-time data analytics and stream processing. In addition to this, you will understand how to use Hadoop to build analytics solutions on the cloud and an end-to-end pipeline to perform big data analysis using practical use cases. By the end of this book, you will be well-versed with the analytical capabilities of the Hadoop ecosystem. You will be able to build powerful solutions to perform big data analytics and get insight effortlessly.
Table of Contents (18 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
4
Scientific Computing and Big Data Analysis with Python and Hadoop
Index

Joins


In traditional databases, joins are used to join one transaction table with another lookup table to generate a more complete view. For example, if you have a table of online transactions sorted by customer ID and another table containing the customer city and customer ID, you can use join to generate reports on the transactions sorted by city.

Transactions table: This table has three columns, the CustomerID, the Purchased item, and how much the customer paid for the item:

CustomerID

Purchased Item

Price Paid

1

Headphones

25.00

2

Watch

20.00

3

Keyboard

20.00

1

Mouse

10.00

4

Cable

10.00

3

Headphones

30.00

Customer Info table: This table has two columns the CustomerID and the City the customer lives in:

Customer ID

City

1

Boston

2

New York

3

Philadelphia

4

Boston

 

Joining the transaction table with the customer info table will generate a view as follows:

Customer ID

Purchased Item

Price Paid

City

1

Headphone

25.00

Boston

2

Watch

100.00

New York

3

Keyboard

20.00

Philadelphia

1

Mouse

10.00

Boston

4

Cable

10.00

Boston

3

Headphones

30.00

Philadelphia...