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)

Data Discovery and metadata


Data Discovery deals with the identification of related data assets, making them discoverable and guiding the data consumers to relevant datasets.

The efficiency of Data Discovery depends upon the amount and quality of the metadata that is captured as the data moves across the various tiers in the Data Lake. Metadata keeps track of all the data assets that reside on a Data Lake; it helps data consumers to find the relevant data. Metadata identifies and maintains relationships between data, right from the time the data is ingested, enhanced, transformed, and evolved. It guides consumers to related datasets that can be combined and integrated.

Semantic metadata captures the semantics of the data; semantics is the ability to extract contextual meaning from text. Semantic metadata captures the context of the data and annotates the data with it; it can be used for data classification and identifying relationships between data. It improves search efficiency by providing...