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

Data lake building blocks

A data lake is an abstract concept which requires technological tools and systems to implement. Since there is no standard definition of what a data lake must consist of, it is not uncommon to see slightly differing names of the constituent building blocks of data lakes in the definitions proposed by vendors and industry analysts. The building blocks of a data lake will fall into three tiers:

  • Ingestion tier

  • Storage tier 

  • Insights tier

Ingestion tier

In general, a data lake should be able to take data feeds from multiple sources. This data feed can come in real time, micro batches, or in large batch files. A data lake should be able to ingest the data from difference sources. For each type of data source, the data ingestion tools may be identified. The ingestion tier can also provide scheduling tools to import the data at predefined intervals.

Storage tier

Data lakes will have a data storage system, such as HDFS, where the data resides in its raw form. Though this data...