What is a Data Warehouse? - Definition & Meaning
Learn what a data warehouse is, the difference between OLAP and OLTP, and how BigQuery and Snowflake enable centralized data analysis.
Definition
A data warehouse is a centralized storage system that consolidates large volumes of structured data from multiple sources for analysis and reporting. It is optimized for complex analytical queries rather than transaction processing.
Technical explanation
Data warehouses follow the OLAP model (Online Analytical Processing), optimized for reading and aggregating large datasets, unlike OLTP systems (Online Transaction Processing) that are optimized for fast read/write operations. Star schema and snowflake schema are common data modeling patterns with fact tables (measurements) and dimension tables (context). Google BigQuery is a serverless columnar data warehouse that can analyze petabytes of data using standard SQL without infrastructure management. Snowflake separates compute and storage, allowing independent scaling and pay-per-use billing. Columnar storage compresses data efficiently and accelerates analytical queries by reading only relevant columns. Materialized views precompute frequently used aggregations. Data warehouses receive data through ETL or ELT pipelines that transform raw data into an analysis-ready format. Data lakehouse architectures (Delta Lake, Apache Iceberg) combine the flexibility of data lakes with the performance of data warehouses.
How MG Software applies this
MG Software helps clients set up data warehouses for business intelligence. We configure BigQuery or Snowflake as a central analytics hub, build ETL pipelines to load data from various sources, and create dashboards for data-driven decision making. This gives our clients insight into their business performance.
Practical examples
- A retail company consolidating sales data from their webshop, physical stores, and marketplace into BigQuery to run cross-channel analyses and discover trends.
- A SaaS company using Snowflake to analyze user behavior, churn indicators, and revenue metrics for product decisions.
- A logistics company building a data warehouse to combine delivery data, vehicle telemetry, and weather data for route optimization.
Related terms
Frequently asked questions
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