What is a data warehouse?
A data warehouse is a central system that stores data from many sources in a form built for reporting and analysis. Marketers use one to bring together ad spend, web analytics, and customer records so they can answer questions across all of them.
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What a data warehouse is for
A warehouse holds historical data in organized tables that you can query with SQL. It's designed to answer analytical questions quickly, even across large volumes of data.
Examples include BigQuery, Snowflake, and Amazon Redshift.
Why marketers use one
Each ad platform and analytics tool reports only its own piece. A warehouse lets you join them, so you can tie spend to leads, revenue, and customer value on your own terms.
You also keep your history if a platform limits how long it stores data.
What it takes to run one
You need pipelines to load data, rules for how tables are structured, and someone who can write SQL. Plan access controls early, since customer-level data needs protection.
Common questions
What's the difference between a database and a data warehouse?
A database usually runs an application and handles many small reads and writes. A warehouse is tuned for large analytical queries across historical data.
Do small businesses need a data warehouse?
Not always. If your reporting fits comfortably in your analytics and ad tools, you may not need one yet. It helps when you need to combine many sources.
What's the difference between a data warehouse and a data lake?
A warehouse stores structured, organized data. A data lake holds raw data in many formats and is shaped later.
How does data get into a warehouse?
Through data pipelines that extract data from sources and load it, using ETL or ELT processes.
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