Practical Hadoop Migration

Chapter 1: RDBMS Meets Hadoop: Integrating, Re-Architecting, and Transitioning
Conceptual Differences Between Relational and HDFS NoSQL Databases
Relational Design and Hadoop in Conjunction:
Advantages and Challenges
Type of Data
Data Volume
Business Need
Deciding to Integrate, Re-Architect, or Transition
Type of Data
Type of Application
Business Objectives
How to Integrate, Re-Architect, or Transition
Integration
Re-Architecting Using Lambda Architecture
Transition to Hadoop/NoSQL
Part I: Relational Database Management Systems:
A Review of Design Principles, Models and Best Practices
■ Chapter 2: Understanding RDBMS Design Principles 
Overview of Design Methodologies 
Top-down
Bottom-up
SSADM
Exploring Design Methodologies
Top-down
Bottom-up
SSADM
Components of Database Design
Normal Forms
Keys in Relational Design
Optionality and Cardinality
Supertypes and Subtypes
■ Chapter 3: Using SSADM for Relational Design
Feasibility Study
Project Initiation Plan
Requirements and User Catalogue
Current Environment Description
Proposed Environment Description
Problem Definition
Feasibility Study Report
Requirements Analysis
Investigation of Current Environment
Business System Options
Requirements Specification
Data Flow Model
Logical Data Model
Function Definitions
Effect Correspondence Diagrams (ECDs)
Entity Life Histories (ELHs)
Logical System Specification
Technical Systems Options
Logical Design
Physical Design
Logical to Physical Transformation
Space Estimation Growth Provisioning
Optimizing Physical Design
■ Chapter 4: RDBMS Design and Implementation Tools
Database Design Tools
CASE tools
Diagramming Tools
Administration and Monitoring Applications
Database Administration or Management Applications
Monitoring Applications
Part II: Hadoop: A Review of the Hadoop Ecosystem,
NoSQL Design Principles and Best Practices
■ Chapter 5: The Hadoop Ecosystem
Query Tools
Spark SQL
Presto
Analytic Tools
Apache Kylin
In-Memory Processing Tools
Flink
Search and Messaging Tools
■ Chapter 6: Re-Architecting for NoSQL: Design Principles,
Models and Best Practices
Design Principles for Re-Architecting Relational Applications to
NoSQL Environments
Selecting an Appropriate NoSQL Database
Concurrency and Security for NoSQL
Designing the Transition Model
Denormalization of Relational (OLTP) Data
Denormalization of Relational (OLAP) Data
Implementing the Final Model
Columnar Database as a NoSQL Target
Document Database as a NoSQL Target
Best Practices for NoSQL Re-Architecture
Part III: Integrating Relational Database Management
Systems with the Hadoop Distributed File System
■ Chapter 7: Data Lake Integration Design Principles
Data Lake vs. Data Warehouse
Data Warehouse
Data Lake
Concept of a Data Lake
Data Reservoirs
Exploratory Lakes
Analytical Lakes
Factors for a Successful Implementation
■ Chapter 8: Implementing SQOOP and Flume-based
Data Transfers
Deciding on an ETL Tool
Sqoop vs. Flume
Processing Streaming Data
Using SQOOP for Data Transfer
Using Flume for Data Transfer
Flume Architecture
Understanding and Using Flume Components
Implementing Log Consolidation Using Flume
Part IV: Transitioning from Relational to NoSQL
Design Models
■ Chapter 9: Lambda Architecture for Real-time Hadoop
Applications
Defining and Using the Lambda Layers
Batch Layer
Serving Layer
Speed Layer
Pros and Cons of Using Lambda
Benefits of Lambda
Issues with Lambda
The Kappa Architecture
Future Architectures1
A Bit of History 
Butterfly Architecture
■ Chapter 10: Implementing and Optimizing the Transition
Hardware Configuration
Cluster Configuration
Operating System Configuration
Hadoop Configuration
HDFS Configuration
Choosing an Optimal File Format 
Indexing Considerations for Performance 
Choosing a NoSQL Solution and Optimizing Your Data Model
Part V: Case Study for Designing and Implementing a
Hadoop-based Solution
■ Chapter 11: Case Study: Implementing Lambda Architecture
The Business Problem and Solution 
Solution Design 
Hardware
Software
Database Design Implementing Batch Layer 
Implementing the Serving Layer
Implementing the Speed Layer 
Storage Structures (for Master Data and Views) 
Other Performance Considerations
Reference Architectures
Changes to Implementation for Latest Architectures

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