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Home » Database » Data Modeling for Technical Analysts

Course Skill Level:

Foundational to Intermediate

Course Duration:

2 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Database

  • Course Code:

    DMFTANN21J09

Who should attend & recommended skills:

Experienced technical analysts with basic software systems analysis, design, and implementation experience

Who should attend & recommended skills

  • Experienced technical analysts that will be working to construct logical and physical models
  • Technical analysis: Basic to Intermediate (1-5 years’ experience)
  • Software systems analysis, design, and implementation: Basic (1-2 years’ experience)

About this course

Data models are built first around a business needs conceptual model, then a logical model, and conclude with a physical model.

Skills acquired & topics covered

  • Data models are built first around a business needs conceptual model, then a logical model, and conclude with a physical model.

Course breakdown / modules

  • What are the constructs around the project?
  • &What’ the system should contain in a conceptual data model
  • Overview of implementing a logical data model
  • Overview of implementing a physical model

  • Steps to Database Design
  • Conceptual View
  • Data Modeling
  • One-to-One
  • One-to-Many
  • Many-to-One
  • Many-to-Many
  • Lab

  • Conceptual Data Models – Domain Models
  • Big-picture view
  • Gathering initial project requirements
  • Include Entity Classes
  • Their characteristics and constraints
  • Relationships between them
  • Security and Data Integrity requirements
  • Notation is typically simple
  • Example of a Data Element
  • Conceptual Model
  • Logical Model Optional Relationships
  • Logical Modeling
  • Logical Models
  • Lab

  • Provide a schema how the data will be physically stored within a database
  • How to translate entities into physical tables
  • What attributes to use for columns of the physical tables
  • Which columns of the tables to define as keys
  • What indexes to define on the tables
  • What views to define on the tables
  • How to denormalize the tables
  • How to resolve many-to-many relationships
  • Lab

  • Overview
  • Eliciting requirements
  • Recording requirements
  • Analyzing requirements
  • Lab

  • Knowledge of business process in the project
  • What the process is, what terminology is used?
  • Who is involved in the process? How does it flow/work?
  • In business terms, you need to be able to discuss the process
  • Liaison between the stakeholders/ technical
  • You need to be seen as knowledgeable
  • Lab
  • The process of defining the expectations of the users for an application that is to be built or modified
  • Tasks that are conducted to identify the needs of different stakeholders
  • Therefore requirements analysis means to analyze, document, validate and manage software or system requirements
  • High-quality requirements are documented, actionable, measurable, testable, traceable, helps to identify business opportunities, and are defined to a facilitate system design
  • Lab

  • We are not gathering requirements at this point, we are acquiring information
  • You could watch someone working with the current process
  • Look at company training manuals
  • Think about:
  • Can I describe the key business processes?
  • What are the &pain’ points?
  • Works with this business process?
  • People you could interview?
  • Be ready when you meet, know what you don’t know and ask the questions!

  • What the objectives for the system?
  • What is to be accomplished?
  • How the system fits into the business?
  • How is it used on a day-to-day basis?

  • Define requirements elicitation
  • How does this process work?
  • Who will perform the process?
  • We spend time learning before we start asking questions
  • Learn the &lingo’ or terms with the process
  • Review

  • Stakeholders
  • Project leaders and owners
  • Business Users
  • Developer, data modeler, analyzer
  • Data quality steward / Metadata steward

  • Interviews / Questionnaires
  • User Observation
  • Workshops
  • Brainstorming
  • Use Cases and more

  • Analysis / Feasibility
  • Define System objectives / scope
  • Conceptual Design
  • Data Analysis
  • E-R modeling
  • Normalization
  • Data Model
  • Logical Design
  • Physical Design
  • Implementation/
  • Loading of data
  • Physical issues
  • Testing/Evaluation
  • Operation
  • Maintenance
  • Labs

  • Who uses the SDLC?
  • Everyone uses SDLC (System Development Life Cycle)
  • The approach is referred to as the waterfall technique
  • Perhaps the most used process
  • Lab

  • Two Main Methodologies
  • Waterfall and Agile
  • Define Waterfall
  • Agile Definition

  • Four phases of the Waterfall Model
  • Conception
  • Initiation
  • Analysis
  • Design

  • The Agile SDLC model
  • Scrum is a framework used to apply the Agile Software Development Lifecycle (SDLC) methodology
  • Lab

  • Automated Design (AD) tools (formerly known as CASE tools)
  • are graphical user interface (GUI) applications that aid in the design of a database
  • Vendors / AD Tools
  • ERWIN Data Modeler – Erwin Inc.
  • TOAD – Quest Software
  • Two basic types of modeling:
  • Logical, Physical
  • Labs

  • Entity Relationship Diagrams / Models
  • Function Driven or Data-Flow-Driven
  • Focus on the function specification
  • Data-Driven
  • Promote reusability of data
  • Establish a consistent set of names for data
  • Lab

  • Data Modelling showing how the entities are related to each other
  • The conceptual model, the logical model and physical model
  • Business Rules and definitions
  • What Business rules apply to

  • Model – a preliminary representation of something, serving as the plan from which the final object is to be constructed
  • Two Concepts of Quality, Completeness, Correctness
  • Lab

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