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Description
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
About the Author
Saurabh K. Gupta is a technology leader, published author, and database enthusiast with more than 11 years of industry experience in data architecture, engineering, development, and administration. Working as a Manager, Data & Analytics at GE Transportation, his focus lies with data lake analytics programs that build a digital solution for business stakeholders. In the past, he has worked extensively with Oracle database design and development, PaaS and IaaS cloud service models, consolidation, and in-memory technologies. He has authored two books on advanced PL/SQL for Oracle versions 11g and 12c. He is a frequent speaker at numerous conferences organized by the user community and technical institutions. He tweets at @saurabhkg and blogs at sbhoracle.wordpress.com. Venkata Giri currently works with GE Digital and has been involved with building resilient distributed services at a massive scale. He has worked on big data tech stack, relational databases, high availability, and performance tuning. With over 20 years of experience in data technologies, he has in-depth knowledge of big data ecosystems, complex data ingestion pipelines, data engineering, data processing, and operations. Prior to working at GE, he worked with the data teams at Linkedin and Yahoo.