What does a typical data warehouse contain?
Every warehouse follows the same structural pattern regardless of industry. A central fact table holds the numeric measurements from a business event: quantities, amounts, durations and counts. Dimension tables surround it with the descriptive context: product names, customer segments, dates and locations.
A retail warehouse, for instance, might have a sales fact table recording each transaction line and dimension tables for product, store, customer and calendar. Snowflake's guide to data warehouses describes a data warehouse as 'a centralized repository that stores current and historical data from multiple sources across an organization, designed to support business intelligence (BI) and analytics.'
That definition applies to every example on this page. The data arrives through an ETL or ELT pipeline that extracts records from source systems, transforms them into a consistent format and loads them into the warehouse on a daily or hourly schedule.
How do retailers use a data warehouse?
Retail warehouses are among the most common and easiest to understand. The fact table captures each sale at the transaction line level: item, quantity, price, discount and timestamp. Dimensions include product hierarchy (category, subcategory, brand, SKU), store (region, format, square footage), customer (segment, loyalty tier, acquisition channel) and calendar (day, week, fiscal period).
With this structure, an analyst can answer questions such as which product categories grow fastest in a given region or how loyalty members spend compared to non-members. A second fact table often tracks inventory snapshots, recording stock levels per product per store per day. Joining sales and inventory facts on shared product and store dimensions reveals where stockouts cost revenue or where excess inventory ties up capital.
Two additional retail patterns are demand forecasting and promotion evaluation. A demand model combines years of weekly sales data with seasonal signals to predict inventory needs per store and week. Promotion analysis compares sales lift during a campaign against baseline periods to measure return on marketing spend.
These are the queries that justify the warehouse. They scan millions of rows across years of history, which an operational point-of-sale database was never designed to handle.
How does a warehouse work in healthcare?
A healthcare warehouse integrates data from electronic health record systems, laboratory information systems, pharmacy dispensing, claims processing and scheduling. The fact tables record clinical events: patient encounters, lab results, medication administrations and billing line items. Dimensions include patient demographics, provider details, diagnosis codes (ICD-10), procedure codes (CPT), facility and calendar.
Common analyses include tracking readmission rates by diagnosis, comparing treatment outcomes across facilities and identifying patients at higher risk of chronic conditions. Population health management depends on these queries. A hospital network might load five years of encounter data and compare 30-day readmission rates by discharge day, unit and diagnosis to guide changes in discharge planning.
Claims analytics adds another dimension. A warehouse that holds billing data alongside clinical records can track reimbursement rates by payer, identify coding errors and flag outlier charges for audit.
Operational reporting from the source EHR system cannot do this kind of cross-facility, multi-year analysis because each facility may run a different system with different data formats. The warehouse solves that by integrating everything first.
What does a financial data warehouse look like?
Banks and insurance companies build warehouses around transaction data: deposits, withdrawals, transfers, trades, claims and payments. The grain is usually one transaction per row, with dimensions for account, customer, branch, product type, currency and date.
Regulatory reporting is a primary driver. Financial institutions must produce standardized reports for regulators on capital adequacy, liquidity and risk exposure. Pulling these reports from dozens of operational systems each quarter is slow and error-prone. Pulling them from a single integrated warehouse is routine.
Fraud detection is another major use case. A classification model trained on historical transaction data can flag unusual patterns, but only if the warehouse holds enough labeled examples of both normal and fraudulent activity. Credit risk scoring follows the same logic: regression models predict default probability using years of loan performance data stored in the warehouse.
How do manufacturers apply data warehousing?
Manufacturing warehouses hold production records, equipment sensor logs, quality inspection results, supplier deliveries and shipping data. A production fact table might record each unit produced with its line, shift, machine speed, defect count and downtime minutes. Dimensions include product specification, equipment, operator, shift and plant.
Predictive maintenance is the headline use case. By loading months of sensor readings (temperature, vibration, pressure) alongside maintenance logs, an engineer can spot the patterns that precede equipment failure. Anomaly detection algorithms flag machines that drift outside normal operating ranges before a breakdown occurs.
Supply chain optimization is another common application. A warehouse that joins supplier lead times with production schedules and customer orders can identify bottlenecks weeks before they cause delays. Quality analytics works the same way: tracking defect rates by line, shift and raw material batch pinpoints the root cause of quality problems.
Which platforms run production data warehouses?
Any of the four cloud platforms below can run the examples on this page. Amazon Redshift is described in AWS documentation as 'a fully managed, petabyte-scale data warehouse service in the cloud.' Its Serverless option bills RPU-hours per second with a 60-second minimum and no upfront commitment, and charges stop when the warehouse is idle.
Google calls BigQuery 'a fully managed, AI-ready data platform' with a serverless architecture, and it separates compute and storage so each layer can allocate resources without affecting the other.
Snowflake offers four editions (Standard, Enterprise, Business Critical and Virtual Private Snowflake), with compute billed in credits per second after a 60-second minimum and storage charged separately.
Microsoft describes Azure Synapse Analytics as a service that brings together 'enterprise data warehousing and Big Data analytics,' with dedicated SQL pools for predictable workloads and serverless pools for ad hoc queries. Its dedicated SQL pool 'stores data in relational tables with columnar storage,' which reduces storage costs and speeds up queries, and Microsoft now tells readers who are new to data warehousing to start with Microsoft Fabric Data Warehouse instead.
All four use columnar storage and SQL, but scaling is not automatic everywhere: Azure Synapse dedicated SQL pools are scaled by changing their data warehouse units and provisioned Redshift clusters are resized, while BigQuery and Redshift Serverless allocate capacity for you. The choice between them usually comes down to existing cloud contracts, team expertise and workload shape rather than raw capability.
What separates a strong warehouse example from a weak one?
A strong example ties the warehouse back to a specific business question. Saying 'we put all our data in Redshift' is not a warehouse example. Saying 'we load daily point-of-sale data into a star schema so regional managers can compare same-store sales week over week' is one.
The business question drives the grain, the grain drives the schema and the schema drives the ETL pipeline. Weak examples start with the technology and work backward. They pick a platform, load data without a clear grain definition and end up with a repository that nobody queries.
The strongest warehouses in any industry share three traits: a declared grain that matches a real business event, conformed dimensions that let different fact tables join cleanly and an ETL process that runs reliably on a known schedule.