Course Objective & Scope
Reports that never reconcile and analytics models that have to prepare data from scratch every time are usually symptoms of weak data modeling. This programme covers two prominent topics in TDWI's data management and analytics bodies of knowledge, data modeling and data science, and connects them.
The conceptual-logical-physical model distinction, Kimball star schemas, slowly changing dimensions (SCD), Data Vault 2.0 and modeling in a lakehouse are applied, followed by how these models become feature tables for data science. Participants draft a dimensional model and a lineage/ownership model for one of the organisation's business domains. The programme does not claim an official TDWI partnership or certification preparation; the content references these knowledge areas.
Who Should Attend?
- Data warehouse and BI developers
- Data architects and data modelers
- Data scientists preparing data for analytical models
- Prerequisite: SQL and relational database knowledge
Key Takeaways
- Derive conceptual and logical data models from business requirements
- Design star schemas with fact and dimension tables at the right grain
- Choose SCD types and the Data Vault approach based on the scenario
- Derive feature tables for data science from a dimensional model
- Document data ownership and lineage alongside the model
Curriculum
Data Modeling Layers
- Conceptual, logical and physical models
- Normalisation and denormalisation for analytics
Dimensional Modeling (Kimball)
- Facts/dimensions, grain and conformed dimensions
- SCD Types 1, 2 and 3
Data Vault and Lakehouse Modeling
- Hubs, links and satellites
- Placing models in a medallion architecture
From Model to Data Science
- Feature tables and analytical datasets
- Point-in-time correctness and historical data
Metadata, Ownership and Lineage
- Data dictionary and business terms
- Drafting an ownership and lineage model
Other Courses in This Category
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Turning business questions into analysis plans, data preparation with SQL and Excel/Python, KPI definition, dashboard design in Power BI or Tableau and data storytelling, for teams moving into or strengthening the analyst role.
- Live Course
- 5 modules
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Querying and aggregating relational databases with SELECT, WHERE, JOIN, GROUP BY, HAVING and subqueries: hands-on SQL training for business analysts and specialists who want self-service access to raw data.
- Live Course
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Window functions, recursive CTEs, cohort and time-series queries, semi-structured (JSON) processing and query tuning with EXPLAIN ANALYZE, for analysts and engineers working on large datasets.
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End-to-end data engineering for RAG, LLM and machine learning systems: data contracts, medallion architecture, feature and embedding pipelines, lineage and metadata management.
- Live Course
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Automated data quality testing (dbt, Great Expectations, Soda) and enforceable producer-consumer data contracts to stop silent schema changes and corrupted data from breaking downstream analytics and AI.
- Live Course
- 5 modules
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Discovering data assets, tracking column-level lineage and catching pipeline delays or distribution drift early using OpenLineage, DataHub/OpenMetadata and data observability practices.
- Live Course
- 5 modules
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