"Status quo. Latin for the mess we're in."
Jeve Moorman




DATA BRIDGING
 
 
A data bridge is a process that connects two or more stable, predefined data stores for a limited time or on an ongoing basis. Data bridges are used in a variety of applications such as:
  • Converting data from an old system to a new system
  • Interfacing data from one system to another
  • Sourcing a data warehouse, etc.

This course explains the foundational concepts underlying a successful bridging project, describes the activities required for a successful data bridge, and covers the methodology needed to build a data bridge.

Course Outcomes:
Students of Advanced Strategies’ Data Bridging Course returning to work will be able to:

  1. Explain the concept of data bridging and the different classes of data bridges.
  2. Explain the different activities that are needed to bridge data.
  3. Apply a methodology to define, analyze, and design a data bridge in any application.
  4. Select an appropriate bridging paradigm for various source/target scenarios.
  5. Identify the appropriate use of utility software, custom software, and manual processes in building bridges.

Note: This course will not eliminate the hard, dirty work of bridging data, but it will provide a methodology to make it a manageable, predictable, and repeatable process.

Course Outline:
Introduction to Data Bridging
  • What is Data Bridging?
  • When is Data Bridging Used?
  • Classes of Data Bridges
  • How is Data Bridging Done?
  • Who Does Data Bridging?
Data Bridging Activities
  • Sourcing
  • Auditing
  • Unloading
  • Scrubbing
  • Transforming
  • Loading
  • Logging and Validating
Fundamental Methodology Concepts
  • A Path to a Solution
  • When are Data Bridges Built When Developing Systems
  • The Data Bridging Methodology Framework
Defining a Data Bridge
  • Stakeholders
  • Intentions
  • Values
  • Focus
  • Context
Data Bridging Paradigms
  • Where to Start: Source or Target?
  • Target Backward
  • Source Forward
  • Hybrids
  • Cost/Benefits of Various Paradigms
Analyzing a Data Bridge
  • Discovering Candidate Target and Source Data
  • Assessing Needs of Target and Source Data
  • Assessing Data Bridging Options
  • Specifying Data to Be Bridged and Bridging Rules
Designing a Data Bridge
  • Designing the Data Bridging Architecture
  • Engineering the Data Bridge
  • Completing the Design
Realization Strategies
  • Acquired Packages
  • Custom Code
  • Utilities / Tools
  • Manual Processes
Beyond Realization
  • Implementation
  • Support
  • Maintenance
  • Final Case Study

Who Should Attend:
This course is targeted for Data Modelers, Database Administrators, Systems and Business Analysts, and other individuals involved in data bridging projects.

Non-Course Prerequisites:
Prior to the course, students should be able to read and be familiar with logical and physical data models.

Course Duration:
Three Days

Class Availability: Request It Now!

Materials Provided:
Student Workbook, Case Study, and Bridging Checklists

____________________________________________________________________________
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