Practical Enterprise Data Lake Insights Handle Data-Driven Challenges in an Enterprise Big Data Lake

Practical Enterprise Data Lake Insights: Handle Data-Driven Challenges in an Enterprise Big Data Lake
Practical Enterprise Data Lake Insights: Handle Data-Driven Challenges in an Enterprise Big Data Lake
By 作者: Saurabh Gupta - Venkata Giri
ISBN-10 书号: 1484235215
ISBN-13 书号: 9781484235218
Edition 版本: 1st ed.
Release Finelybook 出版日期: 2018-07-29
Pages 页数: 327

The Book Description robot was collected from Amazon and arranged by Finelybook

Use this practical guide to successfully handle the challenges encountered when designing an enterprise data lake and learn industry best practices to resolve issues.
When designing an enterprise data lake you often hit a roadblock when you must leave the comfort of the relational world and learn the nuances of handling non-relational data. Starting from sourcing data into the Hadoop ecosystem, you will go through stages that can bring up tough questions such as data processing, data querying, and security. Concepts such as change data capture and data streaming are covered. The book takes an end-to-end solution approach in a data lake environment that includes data security, high availability, data processing, data streaming, and more.
Each chapter includes application of a concept, code snippets, and use case demonstrations to provide you with a practical approach. You will learn the concept, scope, application, and starting point.
What You’ll Learn
Get to know data lake architecture and design principles
Implement data capture and streaming strategies
Implement data processing strategies in Hadoop
Understand the data lake security framework and availability model
Who This Book Is For
Big data architects and solution architects
Cover
Frontmatter
1.Introduction to Enterprise Data Lakes
2.Data lake ingestion strategies
3.Capture Streaming Data with Change-Data-Capture
4.Data Processing Strategies in Data Lakes
5.Data Archiving Strategies in Data Lakes
6.Data Security in Data Lakes
7.Ensure High Availability of Data Lake
8.Managing Data Lake Operations

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