TDWI Data Virtualization: Solving Complex Data Integration Challenges (NEW) Training in Nigeria

The data integration landscape has changed radically the past few years. What was once a relatively manageable problem of blending and unifying data from enterprise transaction systems has grown to encompass external data, Web data, clickstream data, end-user data, big data, cloud data, and more. New expectations for information-driven business agility further compound the complexities of modern data integration. The ETL-based data warehouse is no longer enough. Data virtualization is a core component of next-generation data integration architectures, techniques, and technology.

Get ready to expand your data integration capabilities, deliver business-speed information, and make the most of recent advances in data integration technology. Through a combination of lecture, exercises, and case study review you will learn how data virtualization works and how to position it in your data integration architecture and processes.

• BI, MDM, and data warehousing program and project managers
• Data integration architects, designers, and developers
• Data and technology architects

• Data virtualization definitions and terminology
• Business case and technical rationale for data virtualization
• Key concepts and foundational principles of virtualization--views, services, etc.
• Data virtualization lifecycle, capabilities, and processes
• How to extend the data warehouse with virtualization
• How virtualization enables federation and enterprise data integration
• How virtualization is applied for big data and cloud data challenges
• How companies use virtualization to solve business problems and drive business agility

Data Virtualization Module 1. Data Virtualization Concepts and Principles

Data Virtualization Basics

• Data Virtualization Defined
• Virtualization vs. Materialization
• Virtualization vs. Synchronization
• Virtualization vs. Federation
• History and Evolution

Why Data Virtualization?

• Business Agility
• The Data Virtualization Business Case
• The Data Virtualization Technical Case

The Data Virtualization Foundation

• Views
• Query Optimization
• Data Services
• A “Bird’s-Eye” View

Virtualize or Materialize?

• Decision Factors
• Business Considerations Discussion

Module 2. Data Integration Architecture

Integration Architecture Concepts

• Integration Architecture Defined
• Data Sources, Middleware, and Data Consumers
• You Have It (Whether Defined or Not)

Reference Architectures

• Forrester’s Data Architecture Reference Model
• Forrester’s IaaS Architecture
• Gartner’s Data Services Layer Architecture
• IBM’s BI Reference Architecture

Integration Architecture Examples

• Example 1 – Ministry Social Services Logical Architecture
• Example 2 – Energy Industry Logical Architecture
• Example 3 – Energy Industry Technical Architecture
• Example 4 – Financial Services Logical Architecture

Virtualize or Materialize?

• Data Source Considerations Discussion

Module 3. Data Virtualization in Integration Architecture

Virtualization in Data Integration Projects

• Data Virtualization Use Cases

Data Warehousing Use Cases

• Data Warehouse Augmentation
• Data Warehouse Federation
• Hub and Virtual Spoke
• Complement ETL
• Data Warehouse Prototyping
• Data Warehouse Migration

Data Federation Use Cases

• Federated Views
• Data Services
• Data Mashups
• Caches
• Virtual Data Marts
• Virtual Operational Data Store (ODS)

MDM and EIM Use Cases

• Master Data Hub Extension
• Master Data Services
• Virtual Data Layer
• Enterprise Data Services

More Data Virtualization Applications

• Virtualization and Big Data
• Virtualization and Cloud Data

Virtualize or Materialize?

• Data Consumer Considerations Discussion

Module 4. Data Virtualization Platforms

Platform Requirements

• Data and Information Services
• Development Environment
• Management Functions

Platform Capabilities

• Access
• Delivery
• Transformation
• Abstraction
• Federation
• Query Optimization
• Caching
• Security
• Quality
• Governance

Platform Variations

• Stand-Alone Data Virtualization
• Extension of BI or Data Warehousing Platform
• Embedded and Appliances
• Some Vendors

Module 5. Implementing Data Virtualization

Analysis

• Goals and Purpose
• Scoping
• Data Source Discovery
• Source Data Analysis

Design and Modeling

• Data Source Layer
• Data Integration Layer
• Publish and Access Layer

Development

• Connect to Data Sources
• Build the Views
• Test and Validate
• Publish and Connect Applications

Deployment

• Acceptance Testing and Production

Operation

• Runtime Operations
• Management and Governance

Virtualize or Materialize?

• A Decision Tool

Module 6. Getting Started with Data Virtualization

Skills and Competencies

• Capabilities and Expertise

Human Factors

• People and Data Virtualization

Goals and Expectations

• DV Readiness
• Choosing a First DV Project
• Planning a DV Roadmap

Best Practices

• What Works in DV
• Mistakes to Avoid