Practical Hadoop Migration

作 者:
Bhushan Lakhe
日 期:


Re-architect relational applications to NoSQL, integrate relational database management systems with the Hadoop ecosystem, and transform and migrate relational data to and from Hadoop components. This book covers the best-practice design approaches to re-architecting your relational applications and transforming your relational data to optimize concurrency, security, denormalization, and performance.

Winner of IBM’s 2012 Gerstner Award for his implementation of big data and data warehouse initiatives and author ofPractical Hadoop Security, author Bhushan Lakhe walks you through the entire transition process. First, he lays out the criteria for deciding what blend of re-architecting, migration, and integration between RDBMS and HDFS best meets your transition objectives. Then he demonstrates how to design your transition model.

Lakhe proceeds to cover the selection criteria for ETL tools, the implementation steps for migration with SQOOP- and Flume-based data transfers, and transition optimization techniques for tuning partitions, scheduling aggregations, and redesigning ETL. Finally, he assesses the pros and cons of data lakes and Lambda architecture as integrative solutions and illustrates their implementation with real-world case studies.

Hadoop/NoSQL solutions do not offer by default certain relational technology features such as role-based access control, locking for concurrent updates, and various tools for measuring and enhancing performance. Practical Hadoop Migration shows how to use open-source tools to emulate such relational functionalities in Hadoop ecosystem components.

What You'll Learn

  • The requirements and design methodologies of relational data and NoSQL models
  • How to decide whether you should migrate your relational applications to big data technologies or integrate them
  • How to transition your relational applications to Hadoop/NoSQL platforms in terms of logical design and physical implementation
  • RDBMS-to-HDFS integration, data transformation, and optimization techniques
  • The situations in which Lambda architecture and data lake solutions should be considered
  • How to select and implement Hadoop-based components and applications to speed transition, optimize integrated performance, and emulate relational functionalities


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