Data Warehouse
A centralized repository optimized for analytical queries across large volumes of historical data from multiple sources.
In Depth
A data warehouse is a system designed for reporting and data analysis, serving as a central repository of integrated data from one or more disparate sources. Unlike operational databases (OLTP) optimized for fast transactional operations, data warehouses (OLAP) are optimized for complex analytical queries across large datasets. They typically use a star or snowflake schema design with fact tables and dimension tables. Modern cloud data warehouses like Snowflake, BigQuery, Amazon Redshift, and Databricks offer elastic scaling, columnar storage, and separation of storage and compute. Data warehouses enable organizations to analyze historical trends, generate reports, and support data-driven decision-making.
How AI for Database Helps
AI for Database connects to data warehouses like Snowflake and BigQuery, letting you query terabytes of data with simple questions.
Related Terms
ETL
Extract, Transform, Load—a data integration process that moves data from source systems into a data warehouse.
OLAP
Online Analytical Processing—a computing approach optimized for complex analytical queries across large datasets.
Business Intelligence
Technologies, practices, and strategies for collecting, integrating, analyzing, and presenting business data to support decision-making.
Ready to try AI for Database?
Query your database in plain English. No SQL required. Start free today.