Data Modeling, Warehousing & Lakehouse
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10 lessons3 levels1 capstone project
Design analytical data models and storage architectures using facts/dimensions, star schemas, warehouses, lakes and lakehouses while balancing usability, governance and performance.
BASIC
- Star Schema / Kimball Approach — Data Modeling Tutorial (Medium)
Explains dimensional modeling and why star schemas work for analytics. - Fact vs Dimension — Star vs Snowflake Schema (Medium)
Clarifies dimensional trade-offs and table roles. - Introduction to Databricks Lakehouse (Medium)
Explains how lakehouse architecture unifies lake and warehouse capabilities.
INTERMEDIATE
- Data Modeling 101 for Lakehouse
A deeper treatment of the same skill; use it to move from concept to working patterns. - Data Engineer Roadmap 2026 — Codebasics
Apply Data Modeling, Warehousing & Lakehouse through the adjacent capability: Data Engineering. - Apache Kafka Fundamentals — Confluent
Apply Data Modeling, Warehousing & Lakehouse through the adjacent capability: Data Engineering.
ADVANCED
- Airflow, Databricks, Kubernetes — Build a Data Platform from Scratch
Extend Data Modeling, Warehousing & Lakehouse into senior/production practice through Data Pipelines, Orchestration & Streaming. - End-to-End Databricks Data Engineering Project
Extend Data Modeling, Warehousing & Lakehouse into senior/production practice through Data Engineering. - Quality, Lineage, Privacy and Trust — Data Governance
Extend Data Modeling, Warehousing & Lakehouse into senior/production practice through Data Quality, Governance & Lineage.