Book Image

Data Lake for Enterprises

By : Vivek Mishra, Tomcy John, Pankaj Misra
Book Image

Data Lake for Enterprises

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 (23 chapters)
Title Page
Credits
Foreword
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Part 1 - Overview
Part 2 - Technical Building blocks of Data Lake
Part 3 - Bringing It All Together

Quality of data


There is no doubt that high-quality data (cleansed data) is an irresistible asset to an organization. But in the same way, bad quality or mediocre quality data, if used to make decisions for an enterprise, cannot only be bad for your enterprise but can also tarnish the brand value of your enterprise, which is very hard to get back. The data, in general, becomes not so usable if it is inconsistent, duplicate, ambiguous, and incomplete. Business users wouldn't consider using these data if they do not have a pleasant experience while using these data for various analyzes. That's when we realize the importance of the fourth V, namely veracity.

Quality of data is an assessment of data to ascertain its fit for the purpose in a given context, where it is going to be used. There are various characteristics based on which data quality can be ascertained. Some of which, not in any particular order, are as follows:

  • Correctness/accuracy: This measures the degree to which the collected data describes the real-world entity that's being captured.
  • Completeness: This is measured by counting the attributes captured during the data-capturing process to the expected/defined attributes.
  • Consistency: This is measured by comparing the data captured in multiple systems, converging them, and showing a single picture (single source of truth).
  • Timeliness: This is measured by the ability to provide high-quality data to the right people in the right context at a specified/defined time.
  • Metadata: This is measured by the amount of additional data about captured data. As the term suggests, it is data about data, which is useful for defining or getting more value about the data itself.
  • Data lineage: Keeping track of data across a data life cycle can have immense benefits to the organization. Such traceability of data can provide very interesting business insights to an organization.

There are characteristics/dimensions other than what have been described in the preceding section, which can also determine the quality of data. But this is just detailed in the right amount here so that at least you have this concept clear in the head; these will become clearer as you go through the next chapters in this book.