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  • Book Overview & Buying Data Lake for Enterprises
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Data Lake for Enterprises

Data Lake for Enterprises

By : Vivek Mishra, Tomcy John, Pankaj Misra
2.9 (8)
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Data Lake for Enterprises

Data Lake for Enterprises

2.9 (8)
By: Vivek Mishra, Tomcy John, Pankaj Misra

Overview of this book

The term "Data Lake" has recently emerged as a prominent term in the big data industry. Data scientists can make use of it in deriving meaningful insights that can be used by businesses to redefine or transform the way they operate. Lambda architecture is also emerging as one of the very eminent patterns in the big data landscape, as it not only helps to derive useful information from historical data but also correlates real-time data to enable business to take critical decisions. This book tries to bring these two important aspects — data lake and lambda architecture—together. This book is divided into three main sections. The first introduces you to the concept of data lakes, the importance of data lakes in enterprises, and getting you up-to-speed with the Lambda architecture. The second section delves into the principal components of building a data lake using the Lambda architecture. It introduces you to popular big data technologies such as Apache Hadoop, Spark, Sqoop, Flume, and ElasticSearch. The third section is a highly practical demonstration of putting it all together, and shows you how an enterprise data lake can be implemented, along with several real-world use-cases. It also shows you how other peripheral components can be added to the lake to make it more efficient. By the end of this book, you will be able to choose the right big data technologies using the lambda architectural patterns to build your enterprise data lake.
Table of Contents (13 chapters)
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Differences between Data Lake and Data Warehouse

Many a times, Data Lakes are also perceived as Data Warehouses. Both Data Lake and Data Warehouse have different objectives to be achieved in an enterprise. Some of the key difference are shown here:

Data Lake Data Warehouse
Captures all types of data and structures, semi-structured and unstructured in their most natural form from source systems Captures structured information and processes it as it is acquired into a fixed model defined for data warehouse purposes
Possesses enough processing power to process and analyze all kinds of data and have it analyzed for access Processes structured data into a dimensional or reporting model for advanced reporting and analytics
A Data Lake usually contains more relevant information that has good probability of access and can provide operational needs for an enterprise A Data Warehouse usually stores and retains...
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Data Lake for Enterprises
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