Data Warehouse
Centralized analytical storage.
A Data Warehouse (DWH) is the foundation of institutional decision-making. It functions by aggregating data from various sources—such as CRM, ERP, and internal databases—into a single, unified environment. Unlike operational databases that handle high-speed 'Online Transactional Processing' (OLTP), a DWH is optimized for 'Online Analytical Processing' (OLAP), using multi-dimensional modeling (Star and Snowflake schemas) to enable fast, complex queries over historical timeframes.
graph LR
Center["Data Warehouse"]:::main
Pre_sql["sql"]:::pre --> Center
click Pre_sql "/terms/sql"
Pre_data_modeling["data-modeling"]:::pre --> Center
click Pre_data_modeling "/terms/data-modeling"
Center --> Child_etl_extract_transform_load["etl-extract-transform-load"]:::child
click Child_etl_extract_transform_load "/terms/etl-extract-transform-load"
Rel_big_data["big-data"]:::related -.-> Center
click Rel_big_data "/terms/big-data"
Rel_data_lake["data-lake"]:::related -.-> Center
click Rel_data_lake "/terms/data-lake"
Rel_siem["siem"]:::related -.-> Center
click Rel_siem "/terms/siem"
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🧠 Knowledge Check
🧒 Explain Like I'm 5
🏢 A specialized computer system built specifically to help business leaders look at their history and find successful patterns.
🤓 Expert Deep Dive
## Star vs. Snowflake Schema
- Star Schema: The simplest modeling style. A central 'Fact' table (e.g., Sales) is connected to 'Dimension' tables (e.g., Dates, Stores, Products) by as few joins as possible. Highly performant.
- Snowflake Schema: A more normalized version where Dimension tables are further split. This reduces data redundancy but increases query complexity (more joins).