Developing SQL Data Models

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Duration 5 days – 35 hrs.

 

Overview

 

This comprehensive 5-day training course is tailored for data professionals, business analysts, and database developers aiming to build proficiency in developing SQL data models. Participants will explore foundational concepts, engage in hands-on exercises, and apply best practices to design effective and scalable data models using SQL.

 

Objectives

 

  • Comprehend the importance of data modeling in database development.
  • Distinguish between conceptual, logical, and physical data models.
  • Create effective Entity-Relationship Diagrams (ERD) for diverse business scenarios.
  • Apply normalization and denormalization techniques in ERD design.
  • Navigate SQL Server Data Tools (SSDT) for database project creation.
  • Develop tables, relationships, and implement advanced database design concepts.
  • Explore indexing strategies for optimal query performance.
  • Understand the role of constraints and keys in maintaining data integrity.
  • Grasp the principles of dimensional modeling for data warehousing.
  • Design and implement Fact Tables and Dimension Tables.
  • Create and populate Dimension Tables.
  • Develop Fact Tables considering granularity and relationships.
  • Build Star and Snowflake Schema dimensional models.
  • Implement query performance optimization techniques.
  • Identify and resolve bottlenecks in data models.
  • Maintain flexibility and scalability in data models.
  • Address data model versioning and evolution challenges.
  • Document and communicate data models effectively.
  • Apply course concepts in a comprehensive data model project.
  • Demonstrate proficiency in designing effective SQL data models.

Audience

 

  • Database Developers: Professionals responsible for designing and implementing database solutions.
  • Data Engineers: Specialists focused on managing and transforming data within organizations.
  • Business Analysts: Analysts seeking to enhance their skills in data modeling and database design.
  • Data Modelers: Individuals dedicated to creating effective and scalable data models.
  • Database Administrators (DBAs): DBAs involved in managing and maintaining SQL databases.
  • IT Professionals: IT specialists responsible for database development and management.
  • System Architects: Architects interested in understanding the intricacies of data model design.
  • Business Intelligence (BI) Developers: BI professionals wanting to strengthen their database design skills.
  • Technology Managers: Managers overseeing technology teams involved in database development.
  • Software Engineers: Engineers interested in the design and implementation of SQL data models.
  • Students and Graduates: Students pursuing a career in IT, computer science, or data-related fields.
  • Anyone Working with SQL Databases: Individuals from various roles interacting with SQL databases and eager to improve their data modeling skills.

 

Pre- requisites 

  • Familiarity with fundamental database concepts.
  • Basic knowledge of SQL language and querying.
  • Basic computer skills and familiarity with software applications.

Course Content

 

Topic 1: Introduction to Data Modeling

 

  • Overview of data modeling concepts and importance in database development
  • Understanding different types of data models: conceptual, logical, and physical
  • Introduction to entity-relationship modeling (ER modeling) and Entity-Relationship Diagrams (ERDs)
  • Exploring common data modeling tools and techniques

 

Topic 2: Entity-Relationship Modeling

 

  • Entity types, attributes, and relationships
  • Cardinality and participation constraints in ER modeling
  • Identifying and resolving many-to-many relationships
  • Subtypes and supertypes: modeling inheritance and specialization

 

Topic 3: Relational Database Design

 

  • Normalization principles: 1NF, 2NF, 3NF, and beyond
  • Decomposition and dependency analysis for normalization
  • Denormalization strategies for performance optimization
  • Advanced topics in relational database design: candidate keys, surrogate keys, and natural keys

 

Topic 4: Dimensional Modeling

 

  • Introduction to dimensional modeling for data warehousing and business intelligence
  • Understanding facts and dimensions in dimensional modeling
  • Star schema vs. snowflake schema: design considerations and trade-offs
  • Hierarchies and slowly changing dimensions (SCDs)

 

Topic 5: Advanced Data Modeling Techniques

 

  • Modeling complex relationships: many-to-many, self-referencing, and recursive relationships
  • Handling temporal data: effective dating, versioning, and snapshotting
  • Designing for scalability and performance: partitioning, indexing, and materialized views
  • Incorporating data modeling best practices and design patterns

 

Topic 6: Data Modeling Tools and Implementation

 

  • Exploring data modeling tools such as ERwin, Power Designer, and SQL Server Data Tools (SSDT)
  • Translating conceptual and logical models into physical database designs
  • Generating Data Definition Language (DDL) scripts from data models
  • Collaborative data modeling: version control, team collaboration, and documentation

 

Topic 7: Data Model Validation and Optimization

 

  • Validating data models for correctness and completeness
  • Performing data model reviews and walkthroughs
  • Optimizing data models for performance and scalability
  • Iterative data modeling: continuous improvement and evolution of data models
  • Best practices for maintaining and evolving data models over time
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