🏗️ Data Warehousing Concepts
Understand data warehousing principles including OLAP, dimensional modeling, and modern cloud warehouse platforms.
Level:
Intermediate
Tools:
Snowflake
BigQuery
Redshift
PostgreSQL
dbt
Skills You'll Learn:
Data warehouse design
OLTP vs OLAP
Star and Snowflake schema
ETL/ELT patterns
Partitioning and clustering
Slowly Changing Dimensions
Step 1: OLTP vs OLAP
- 1Understand transactional (OLTP) database characteristics and use cases
- 2Learn analytical (OLAP) database characteristics and how they differ from OLTP
- 3Compare row-oriented vs columnar storage formats and their performance trade-offs
- 4Identify when to use OLTP vs OLAP systems in a data architecture
- 5Explore how modern cloud warehouses blend OLTP and OLAP capabilities
Step 2: Data Warehouse Architecture
- 1Learn the core layers of a data warehouse: staging, integration, and presentation
- 2Understand the role of the ETL/ELT process in populating a warehouse
- 3Compare Kimball (bottom-up) vs Inmon (top-down) architecture approaches
- 4Explore the data lakehouse pattern and how it extends traditional warehousing
- 5Design a simple three-layer warehouse architecture for a sample business domain
Step 3: Star vs Snowflake Schema
- 1Define fact tables and understand different fact table types (transactional, snapshot, accumulating)
- 2Define dimension tables and learn about conformed dimensions
- 3Build a star schema with one fact table and multiple dimensions
- 4Convert a star schema to a snowflake schema by normalizing dimensions
- 5Evaluate the trade-offs between star and snowflake schemas for query performance and storage
Step 4: Slowly Changing Dimensions (SCD)
- 1Understand why dimensions change over time and why tracking history matters
- 2Implement SCD Type 1 (overwrite) for dimensions where history is not needed
- 3Implement SCD Type 2 (add new row) with effective dates and current flags
- 4Learn SCD Type 3 (add new column) and when it is appropriate
- 5Practice implementing SCD Type 2 using dbt snapshots
Step 5: Partitioning and Clustering
- 1Understand table partitioning and how it reduces query scan size
- 2Choose effective partition keys based on common query patterns
- 3Learn clustering (sort keys) and how they complement partitioning
- 4Practice partitioning a large table in BigQuery or Snowflake
- 5Monitor and optimize partition pruning and clustering effectiveness
Step 6: Modern Cloud Warehouses
- 1Explore Snowflake architecture: virtual warehouses, storage separation, and Time Travel
- 2Explore BigQuery architecture: serverless compute, slots, and nested/repeated fields
- 3Explore Redshift architecture: node types, distribution keys, and sort keys
- 4Compare pricing models across Snowflake, BigQuery, and Redshift
- 5Set up a free-tier cloud warehouse account and run analytical queries on a sample dataset
Recommended Resources
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