Most data warehouses start simple and become unmaintainable as they grow.
When your data lives in Postgres (operational), MySQL (operational), Snowflake (warehouse), and BigQuery (warehouse), you need a warehouse design that scales with your business.
The 4 data warehouse design principles that matter
1. Data Modeling
Effective data modeling uses star schema with facts and dimensions that make analytics intuitive. It balances normalized versus denormalized approaches based on query patterns and performance needs. Most importantly, it handles history through slowly changing dimensions that preserve historical context.
2. Data Integration
Data integration chooses between ETL and ELT based on where transformations should happen for best performance. It uses incremental loads for efficiency, only processing new or changed data. Most critically, it includes error handling for reliability, ensuring pipelines can recover from failures.
3. Data Quality
Data quality starts with schema validation to ensure structure matches expectations. It includes data validation to verify content meets quality standards. Most importantly, it implements quality monitoring that runs continuously, catching issues before they impact analytics.
4. Performance
Performance optimization uses partitioning by date or key to reduce query scan sizes. It implements clustering based on common query patterns to improve query speed. Finally, it uses materialized views to pre-compute expensive calculations, trading storage for query performance.
What to build first (week 1)
Start with a simple data warehouse design that connects source systems like Postgres, MySQL, and other operational databases. Create a staging layer that stores raw data exactly as it comes from sources. Build a transformation layer that cleans, transforms, and joins data from multiple sources. Create a presentation layer with analytics-ready tables optimized for queries. Finally, deploy your warehouse in Snowflake or BigQuery.
Once you have this foundation, add incremental loads that only process new or changed data for efficiency. Implement data quality checks that validate data as it flows through the warehouse. Add performance optimization through partitioning and clustering to improve query speed. Finally, add documentation that explains the warehouse structure and how to use it.
Why most data warehouses fail
Most data warehouses fail because design is ad-hoc with no clear structure, making them hard to understand and maintain. Integration is manual without automation, requiring constant manual work that becomes unsustainable. Quality is ignored with no validation, leading to unreliable data. Most critically, performance is poor with no optimization, making queries slow and frustrating.
When you design a data warehouse right, you can scale with your business by handling growth without redesign. You can maintain it easily because the structure is clear and well-documented. You can trust the data because quality checks ensure reliability. Most importantly, you can query fast because performance optimization makes analytics responsive.
The hidden cost of poor warehouse design
When warehouse design is poor, it’s hard to maintain because the structure is unclear, making changes risky and time-consuming. It’s slow to query with poor performance that frustrates users and limits analytics. It’s unreliable without quality checks, leading to bad decisions based on bad data. Most critically, it doesn’t scale and can’t handle growth, requiring expensive redesigns.
Good warehouse design means it’s easy to maintain because the structure is clear and well-documented. It’s fast to query with good performance that makes analytics responsive. It’s reliable because quality checks ensure data accuracy. Most importantly, it scales to handle growth without requiring redesign, supporting your business as it grows.