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

Chapter 7. Building a Data Lake

In this chapter, we will cover building a Data Lake with the help of Hadoop. As we have learned in previous chapters, Hadoop offers low storage costs per terabyte of data compared to traditional data warehouse management systems, which makes it an alternative technology or a complementary technology for traditional data warehouse systems. Data Lake and data warehouse are both designed to store data, but a data lake can store a much larger volume of data than a data warehouse.

Data warehouses typically store clean data in pre-defined and structured relational tables. The tables are designed to hold the data in response to specific questions that the stakeholders ask of the data. In this process, the information contained in the data that has no direct value for the question that is being asked is purged when the data is loaded in the data warehouse. Once the information has been purged, there is no way to answer new questions that require the purged information...