Practical Hadoop Migration
How to Integrate Your RDBMS with the Hadoop Ecosystem and Re-Architect Relational Applications to NoSQL
(Sprache: Englisch)
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...
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Produktinformationen zu „Practical Hadoop Migration “
Klappentext zu „Practical Hadoop Migration “
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 of Practical 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
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Database developers, database administrators, enterprise architects, Hadoop/NoSQL developers, and IT leaders. Its secondary readership is project and program managers and advanced students of database and management information systems.
- Decide whether you should migrate your relational applications to big data technologies or integrate them
- Transition your relational applications to Hadoop/NoSQL platforms in terms of logical design andphysical implementation
- Discover RDBMS-to-HDFS integration, data transformation, and optimization techniques
- Consider when to use Lambda architecture and data lake solutions
- Select and implement Hadoop-based components and applications to speed transition, optimize integrated performance, and emulate relational functionalities
Database developers, database administrators, enterprise architects, Hadoop/NoSQL developers, and IT leaders. Its secondary readership is project and program managers and advanced students of database and management information systems.
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Inhaltsverzeichnis zu „Practical Hadoop Migration “
Chapter 1. RDBMS Meets Hadoop: Integrating, Re-Architecting, and Transitioning- Conceptual Differences between Relational Design and Hadoop
- Bringing Relational Design and Hadoop Together: Advantages and Challenges
- Deciding Whether to Integrate, Re-Architect, or Transition
PART I. Relational Database Management Systems: A Review of Design Principles, Models, and Best Practices
Chapter 2. Understanding RDBMS Design Principles
- Design Methodologies
- Design Components
- Normal Forms
Chapter 3. Designing and Using RDBMS Design Models
- Developing Physical Data Models
- SSDAM Steps to a Relational Database
Chapter 4. Designing Relational Applications
- Specifying Functional Requirements
- Building a High-Level Entity-Relationship Model
- Designing a Detailed Logical Model
- Designing and Implementing as Physical Model
- Best Practices for Relational Design
Chapter 5. RDBMS Design Tools and Implementation
- Database Design Tools
- Relational Database Systems for Implementing Physical Models
PART II. Hadoop: A Review of the Hadoop Ecosystem and NoSQL Design Principles for Re-Architecting
Chapter 6. The Hadoop Ecosystem
- Apache Hadoop Components and Their Deployment
- Hadoop-Based Vendor Applications and Their Deployment
Chapter 7. Re-Architecting for NoSQL Design Principles, Models, and Best Practices
- Design Approaches to Transitioning Relational Applications to NoSQL Environments
Designing the Transition Model
- Data Transformations for Concurrency, Security, Denormalization, and Performance
- Implementing the Final Model
- Best Practices for Transition Design
Chapter 8. Lambda Architecture for Real-Time Hadoop Applications
- Lambda Architecture Objectives
- The Lambda Layers
- The Pros and Cons of Using Lambda as a NoSQL Solution
PART III. Integrating Relational Database Management Systems with the Hadoop Distributed File System
Chapter 9. Criteria for RDBMS-HDFS Migration versus Integration Decisions
- Assessing Transition
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Objectives
- Migration, ETL, and Warehouse Considerations
- Criteria for Transition Decisions
Chapter 10. Implementing SQOOP- and Flume-Based Data Transfers
- Deciding on an ETL Tool
- Using SQOOP for Data Transfer
- Using Flume for Data Transfer
Chapter 11. RDBMS-HDFS Integration Techniques
- Overview of Integration Techniques
- Choosing the Appropriate Technique
- Techniques for Performing Analytics on Integrated Data
Chapter 12. Data Lake Integration Design Principles
- The Data Lake Concept
- Designing Data Cleansing, Ingestion, and Storage for a Data Lake
- Designing Master Data Management, Indexing, Governance, Security, and Access
- Designing Analytics for a Data Lake
Part IV. Transitioning from Relational to NoSQL Design Models for Your Enterprise Data Warehouse
Chapter 13. Implementing the Transition
- Overview of Migration Techniques
- Choosing the Optimal Storage Format
- Tools for Implementing Concurrency and Security
- Finalizing the Target Model and Using ETL to Load Data
Chapter 14. Transition Optimization Techniques
- Identifying and Monitoring Design and Performance Issues
- Design and Performance Optimization Techniques: Tuning Partitioning, Scheduling Aggregations, and Redesigning ETL
PART V. Case Studies of Re-Architecting and Integrating
Chapter 15. Designing and Implementing a Real-Time System Using Lambda Architecture: Case Study
- Designing Real-Time Querying with HDFS Data Storage
- Applying the Layering Framework to Hadoop-Based Components
- Implementing a Lambda-Architected System
Chapter 16. Data Lake Design and Implementation: Case Study
- Designing the Architecture and Component Details for a Data Lake
- Data Lake Implementation Steps
- Migration, ETL, and Warehouse Considerations
- Criteria for Transition Decisions
Chapter 10. Implementing SQOOP- and Flume-Based Data Transfers
- Deciding on an ETL Tool
- Using SQOOP for Data Transfer
- Using Flume for Data Transfer
Chapter 11. RDBMS-HDFS Integration Techniques
- Overview of Integration Techniques
- Choosing the Appropriate Technique
- Techniques for Performing Analytics on Integrated Data
Chapter 12. Data Lake Integration Design Principles
- The Data Lake Concept
- Designing Data Cleansing, Ingestion, and Storage for a Data Lake
- Designing Master Data Management, Indexing, Governance, Security, and Access
- Designing Analytics for a Data Lake
Part IV. Transitioning from Relational to NoSQL Design Models for Your Enterprise Data Warehouse
Chapter 13. Implementing the Transition
- Overview of Migration Techniques
- Choosing the Optimal Storage Format
- Tools for Implementing Concurrency and Security
- Finalizing the Target Model and Using ETL to Load Data
Chapter 14. Transition Optimization Techniques
- Identifying and Monitoring Design and Performance Issues
- Design and Performance Optimization Techniques: Tuning Partitioning, Scheduling Aggregations, and Redesigning ETL
PART V. Case Studies of Re-Architecting and Integrating
Chapter 15. Designing and Implementing a Real-Time System Using Lambda Architecture: Case Study
- Designing Real-Time Querying with HDFS Data Storage
- Applying the Layering Framework to Hadoop-Based Components
- Implementing a Lambda-Architected System
Chapter 16. Data Lake Design and Implementation: Case Study
- Designing the Architecture and Component Details for a Data Lake
- Data Lake Implementation Steps
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Autoren-Porträt von Bhushan Lakhe
Bhushan Lakhe is Senior Vice President of Information and Data Architecture at Ipsos, a global market research company headquartered in Paris. He has more than 25 years experience in software development life cycle management, enterprise architecture design and framework implementation, service management, data warehousing, and Hadoop ecosystem (HDFS, HBase, Hive, Pig, SQOOP, MongoDB) implementation, having worked successively at Tata Consultancy Services, Fujitsu-ICIM, ICL, IBM, Unisys Corporation, and as a database architecture consultant to such clients as Leo Burnett, ABN AMRO Bank, Abbott Laboratories, Motorola, JPMorgan Chase, and British Petroleum. He received IBM's 2012 Gerstner Award for his implementation of major big data and data warehouse projects. Lakhe is a Cloudera Certified Administrator for Apache Hadoop CDH4 and a Microsoft Certified Technology Specialist, SQL Server Implementation and Maintenance. He is the author of Practical Hadoop Security. He is active in the Chicago Hadoop community and as a speaker at technical meetups and industry conferences. Lakhe graduated from the Birla Institute of Technology and Science, Pilani.
Bibliographische Angaben
- Autor: Bhushan Lakhe
- 2016, 1st ed., XXIV, 305 Seiten, 61 farbige Abbildungen, Maße: 15,5 x 23,5 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 1484212886
- ISBN-13: 9781484212882
- Erscheinungsdatum: 11.08.2016
Sprache:
Englisch
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