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.

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Data Lake Development with Big Data
Nov, 2015 | 164 pages
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Table of Contents (13 chapters)
Data Lake Development with Big Data
Data Lake Development with Big Data
Credits
Credits
About the Authors
About the Authors
Acknowledgement
Acknowledgement
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
The Need for Data Lake
The Need for Data Lake
Before the Data Lake
Need for Data Lake
Defining Data Lake
Key benefits of Data Lake
Challenges in implementing a Data Lake
When to go for a Data Lake implementation
Data Lake architecture
Summary
2
Data Intake
Data Intake
Understanding Intake tier zones
Architectural guidance
Summary
3
Data Integration, Quality, and Enrichment
Data Integration, Quality, and Enrichment
Introduction to the Data Management Tier
Understanding Data Integration
Big Data tools and technologies
Summary
4
Data Discovery and Consumption
Data Discovery and Consumption
Understanding the Data Consumption tier
Data Discovery and metadata
Data Provisioning and metadata
Architectural guidance
Summary
5
Data Governance
Data Governance
Understanding Data Governance
Data Governance components
Architectural guidance
The current and future trends
Summary
Index
Index

Big Data tools and technologies

Data Integration involves a number of sub processes that range from acquiring raw data to enriching the data before the data is used for consumption. There are many tools and technologies available that can sometimes be used independently or together to suit the specific business needs. These range from the packaged tools that natively operate on Big Data, to enabled technologies that let us develop tools that can work on our specific use case.

The following figure depicts the key aspects that are to be considered while choosing the right tools and technologies for Data Integration of structured data and unstructured data:

The key considerations for choosing an Integration tool

Based on the preceding considerations and the associated trade-offs, you can choose a cloud-based Data Integration tool or an on-premise data Integration tool. The primary driver for making this choice is the cost associated for in-house deployment vs. pay-as-you-go benefits of SaaS models...

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