Book Image

Data Lake Development with Big Data

Book Image

Data Lake Development with Big Data

Overview of this book

A Data Lake is a highly scalable platform for storing huge volumes of multistructured data from disparate sources with centralized data management services. This book explores the potential of Data Lakes and explores architectural approaches to building data lakes that ingest, index, manage, and analyze massive amounts of data using batch and real-time processing frameworks. It guides you on how to go about building a Data Lake that is managed by Hadoop and accessed as required by other Big Data applications. This book will guide readers (using best practices) in developing Data Lake's capabilities. It will focus on architect data governance, security, data quality, data lineage tracking, metadata management, and semantic data tagging. By the end of this book, you will have a good understanding of building a Data Lake for Big Data.
Table of Contents (13 chapters)

Introduction to the Data Management Tier


The key purpose of the Management Tier is to acquire data from the Raw Zone of the Intake Tier and package it so that the data is ready for exploration, discovery, provisioning, and consumption by the end users or applications. The Management tier is a logical intermediary that bridges the gap between the raw data available in the Intake Tier and the discovery efforts performed in the Consumption Tier.

Tip

It is important to recollect that most of the steps in the Management Tier are potentially optional. In many practical implementations of the Data Lake, it is evidenced that the data is directly consumed from the Raw Zone of the Intake Tier. This is true in cases where the raw data is needed for data exploration and building analytical models. Hence, in such cases, all the steps that are part of the Management Tier are deemed optional.

The following figure represents the end-state architecture of the Data Lake as discussed in Chapter 1, The Need for...