Data lake
In short
A data lake is a storage system that keeps raw data in its native format until someone needs it. It can hold structured tables, semi-structured files such as JSON, and unstructured content such as documents or logs, usually in low-cost cloud object storage.
Unlike a data warehouse, which stores cleaned and modeled data shaped for reporting, a data lake stores first and structures later. Teams apply a schema when they read the data, not when they write it.
Data lake vs. data warehouse
- Data lake: Raw, varied data; flexible; good for exploration, machine learning, and keeping history. Needs more work before business users can query it.
- Data warehouse: Modeled, structured data; fast and reliable for dashboards and standard reporting. Less flexible for new data types.
Many teams use both
A common pattern lands data in the lake, transforms the useful parts, and loads them into the warehouse for reporting.
How GTM teams use a data lake
A go-to-market data lake can collect CRM exports, product usage events, marketing platform data, enrichment results, and intent signals in one place. Data teams can then join them to build account scores, attribution models, and customer health views.
Most SDRs and marketers never query the lake directly. They see its output through the CRM, a BI tool, or a reverse ETL sync that pushes modeled fields back into their tools.
Benefits
- Flexibility: Store new sources without redesigning a schema first.
- Cost: Object storage is typically cheaper than warehouse storage for large volumes.
- History: Keep raw data so you can reprocess it when your definitions change.
Risks and common mistakes
- Data swamp: Without documentation, ownership, and naming standards, the lake becomes a dump nobody trusts.
- No governance: Personal data stored without access controls or retention rules creates compliance risk.
- Building before a use case: Start with a question, such as improving account scoring, and ingest what it needs.
- Skipping quality checks: Raw does not mean unchecked. Validate data as it moves downstream.
Is a data lake right for you?
Small teams with a handful of tools often do fine with the CRM and a warehouse. A lake becomes useful when you have many sources, large volumes, or data science needs that a warehouse model does not fit.
Data lake FAQs
What is a data lake?
A data lake is a central repository that stores large amounts of raw data in its original format, whether structured, semi-structured, or unstructured. Teams apply structure when they read the data, which makes it flexible for many uses.
What is the difference between a data lake and a data warehouse?
A data lake stores raw, varied data and applies structure on read. A data warehouse stores cleaned, modeled data built for reporting and fast queries. Many companies use both, moving refined data from the lake to the warehouse.
How do sales and marketing teams use a data lake?
Data teams combine CRM, product usage, marketing, enrichment, and intent data in the lake to build account scores, attribution, and health models. Revenue teams typically see the results in the CRM or BI tools, not the lake itself.
What is a data swamp?
A data swamp is a data lake that has become disorganized and untrustworthy because data was added without documentation, ownership, or quality checks. Users cannot find or rely on what is inside, so the lake loses its value.
