Data Warehousing

A data warehouse brings data from several source systems into a store built for analysis. It gives you a stable place to compare history, calculate measures, and answer questions that cross operational boundaries.

The central mental model is a historical measurement system.

operational systems -> ingest -> clean and conform -> warehouse -> analysis
       change data                preserve meaning       read across history

An order system records a sale. A support system records a case. A marketing platform records a campaign response. Each system serves its own process. A warehouse reshapes selected data so analysts can study those processes together.

The warehouse does not replace the source systems. It serves a different workload.

Why a warehouse exists

Operational systems handle current business activity. They accept orders, update account balances, and reserve inventory. These systems favor short, predictable transactions and current state.

Analytical work asks different questions:

How did revenue change by product and region over three years?
Which customer segments create the most support demand?
Did a promotion change unit volume or only shift timing?
How do actual results compare with targets?

These questions scan, join, group, and summarize many records. They also need consistent definitions across systems and time.

A warehouse separates that analytical workload from operational processing. It integrates selected data, records historical context, and presents structures designed for filtering and aggregation.

The end-to-end system

A warehouse is more than a database. It is a system of sources, movement, transformation, storage, metadata, controls, and consumers.

sources -> landing or staging -> transformation -> dimensional tables
                                                   |
                                                   v
                                      semantic models and reports

Source systems own operational records. They may use different identifiers, names, time zones, and update rules.

Ingestion copies source data into the analytical environment. A batch may run on a schedule. Change data capture may move smaller changes more frequently.

Staging holds data for loading and transformation. It can reduce repeated reads from operational systems and make a failed load easier to restart. Staging data is not the business-facing product.

Transformation combines sources, converts types, applies business rules, resolves identifiers, and checks quality. The result should use consistent meanings.

Data Warehousing

Why a warehouse exists

The end-to-end system

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