DATA ENGINEER ROADMAP · STAGE 02 OF 04
Data modeling and warehousing
Design understandable analytical datasets and choose storage layouts to match how they are queried.
Transform source records into documented, queryable models that preserve business meaning.
CORE SKILLS
Key concepts in data modeling and warehousing
Relational data modeling
Represent entities and relationships with clear keys and constraints before building downstream reports.
Topics include
- Primary and foreign keys
- Normalization trade-offs
- Grain and entity relationships
Analytical modeling
Shape data around business processes so common analytical questions can be answered consistently.
Topics include
- Facts and dimensions
- Star schemas
- Slowly changing dimensions
Warehouses and lakehouses
Understand analytical storage systems and the trade-offs between flexible raw storage and managed tables.
Topics include
- Columnar storage
- Partitioning and file sizing
- Table formats and schema changes
SQL transformations
Build layered models that separate raw source data from cleaned and business-ready datasets.
Topics include
- Staging and marts
- Incremental transformations
- Documentation and lineage
APPLY WHAT YOU LEARN
Practical project
Model a small orders-and-customers dataset into documented analytical tables, define each table’s grain, and answer example business questions.
CHECK YOUR UNDERSTANDING
You are ready to move on when
- Can state what one row represents in each table.
- Can justify joins and transformation rules with examples.