Why warehouses exist
Transactional databases (Postgres, MySQL) are optimised for high-frequency single-row reads and writes, the kind that web apps need. They’re not great at scanning a billion rows to aggregate revenue by channel.
Warehouses flip the optimisation: columnar storage, parallel query execution, separate compute and storage, near-infinite scaling. A query that takes 30 seconds on a transactional database can return in 2 seconds on a warehouse, and the warehouse can run dozens of them concurrently.
For analytics, attribution, BI, and increasingly AI agents, the warehouse is where the data lives.
What goes in the warehouse
Typically everything that matters for analytics:
- Web event data, page views, clicks, conversions from tracking
- Order data, purchases, refunds, products, prices from the e-commerce platform
- Customer data, profiles, subscriptions, lifecycle events
- Marketing platform data, ad spend, impressions, clicks from Meta, Google, TikTok APIs
- CRM data, leads, opportunities, account activity
- Finance data, costs, margins, P&L data from accounting systems
The pipelines that move data from source systems into the warehouse are usually built with Fivetran, Airbyte, Hightouch reverse-ETL, or custom Python. The warehouse becomes the canonical, queryable record.
Warehouse vs lake vs lakehouse
Lots of overlapping terminology in 2026:
- Warehouse, structured, schema-on-write, optimised for SQL analytics
- Data lake, raw files (JSON, Parquet, CSV) in object storage, schema-on-read
- Lakehouse, hybrid: lake storage with warehouse-like query semantics (Databricks pioneered, others followed)
For most marketing analytics use cases, a cloud warehouse (BigQuery, Snowflake) is the right shape. Lakes shine for unstructured / high-volume data. Lakehouses for ML and large-scale data engineering.
Where the warehouse fits in the modern stack
Source systems → Pipelines → Warehouse → Semantic layer → Consumers
(Shopify, Meta, (Dashboards,
GA4, CRM, etc.) AI agents, CRM)Each layer has a specific job. The warehouse stores. The semantic layer interprets. The consumers display or act on.
Warehouse performance and cost
Warehouses charge by storage and compute. Two practical concerns:
- Cost runaway, un-optimised queries (full table scans on terabyte tables) can rack up huge bills. Materialise heavy aggregations.
- Latency mismatch, a warehouse query takes seconds. An AI agent making 10 queries in series feels slow. Cache aggressively at the semantic-layer level.
Common mistakes
- Querying the warehouse directly from production apps. Wrong tool. Warehouses are not for real-time reads. Use a transactional database for that.
- Skipping the semantic layer. Every consumer re-implementing metrics produces inconsistency.
- Loading data without modelling. Raw events are not analytics. The transformations (dbt models, etc.) are what produce useful warehouse tables.
FAQ about Data Warehouse
What is a data warehouse?
A data warehouse is a centralised, query-optimised store for all of a business’s structured data. Common warehouses include BigQuery, Snowflake, Redshift, and Databricks. It is the foundation that BI dashboards, attribution models, and AI agents read from.
Data warehouse vs database vs data lake, what is the difference?
Databases are optimised for high-frequency single-row reads (good for apps). Warehouses are optimised for analytical queries (large scans, aggregations). Data lakes store raw files for schema-on-read. Lakehouses hybridise lake storage with warehouse-like query semantics.
Which data warehouse should I choose?
For most marketing analytics stacks, BigQuery (if your data lives in Google) or Snowflake (vendor-neutral) are the dominant choices. Both scale near-infinitely and have rich ecosystem support for the rest of the modern data stack.