Book Image

Hadoop Blueprints

By : Anurag Shrivastava, Tanmay Deshpande
Book Image

Hadoop Blueprints

By: Anurag Shrivastava, Tanmay Deshpande

Overview of this book

If you have a basic understanding of Hadoop and want to put your knowledge to use to build fantastic Big Data solutions for business, then this book is for you. Build six real-life, end-to-end solutions using the tools in the Hadoop ecosystem, and take your knowledge of Hadoop to the next level. Start off by understanding various business problems which can be solved using Hadoop. You will also get acquainted with the common architectural patterns which are used to build Hadoop-based solutions. Build a 360-degree view of the customer by working with different types of data, and build an efficient fraud detection system for a financial institution. You will also develop a system in Hadoop to improve the effectiveness of marketing campaigns. Build a churn detection system for a telecom company, develop an Internet of Things (IoT) system to monitor the environment in a factory, and build a data lake – all making use of the concepts and techniques mentioned in this book. The book covers other technologies and frameworks like Apache Spark, Hive, Sqoop, and more, and how they can be used in conjunction with Hadoop. You will be able to try out the solutions explained in the book and use the knowledge gained to extend them further in your own problem space.
Table of Contents (14 chapters)
Hadoop Blueprints
About the Authors
About the Reviewers

Test driving Hive and Sqoop

In the previous section, we verified that MySQL, Hive, and Sqoop were available on our Hadoop Sandbox. We will now test drive Hive and Sqoop.

Querying data using Hive

We run Hive queries to select data from tables. Hive has two types of tables:

  • Managed tables

  • External tables

Hive creates managed tables by default. To create external tables, we specify the keyword external during table creation.

In the case of managed tables, the table lifecycle is completely managed by Hive. If you drop a managed table, then the associated data and metadata are also deleted by Hive. The external table reads data from an HDFS file. This file is not deleted when the table is dropped by Hive. Other tools can also access the HDFS file while at the same time we can run Hive queries on the HDFS by defining an external table for the file.

In Chapter 1, Hadoop and Big Data, of this book, we used a dataset containing the historical stock price of IBM to run a MapReduce job that calculated the...