Data Warehouses
A data warehouse is a structured, query-optimized store that holds cleaned, conformed data organized for analysis and reporting.
A structured store for analysis
A data warehouse is a central repository of integrated, cleaned data organized to answer analytical questions quickly. Unlike a transactional database tuned for many small reads and writes, a warehouse is tuned for large aggregate queries that scan many rows to compute summaries. Data arrives already conformed to a defined schema.
Schema design
Warehouses often use dimensional modeling. A central fact table holds measurements (for example, one row per event with numeric values), and it references dimension tables that describe context (time, instrument, configuration). This star schema keeps facts compact while allowing rich filtering and grouping by dimensions.
- Fact table: numeric measurements at a defined grain.
- Dimension table: descriptive context for slicing facts.
- Grain: the meaning of one fact row, fixed and explicit.
- Conformed dimensions: shared descriptions used across fact tables.
Columnar execution
Analytical warehouses store data by column rather than by row, so a query touching three columns of a hundred reads only what it needs. Column storage also compresses well because values in a column are similar. See columnar storage.
Warehouse versus lake
A lake keeps everything raw and flexible; a warehouse keeps curated data fast and consistent. They are complementary. Many programs load raw data into a lake, then transform curated subsets into a warehouse for reliable analysis and dashboards, an ELT pattern.
In practice
For a research program, a warehouse suits structured, well-understood data: run metadata, derived scalar results, and comparison tables across simulation cases. It is where questions with stable, agreed definitions get answered quickly and consistently.