"Lakehouse or warehouse?" is one of the most common questions we get from clients planning a new data platform. Both can serve analytics well the right choice depends on the shape of your data, your team's skills, and how much flexibility you need for machine learning and unstructured data down the line.
01 What each one is actually good at
A data warehouse is optimised for structured, well-modelled data and fast, predictable SQL queries it's the right fit for finance, reporting, and BI workloads where schemas are stable. A lakehouse combines the low-cost, flexible storage of a data lake with the transactional guarantees and performance of a warehouse, making it a better fit when you're mixing structured tables with logs, documents, images, or ML features.
02 Cost
Lakehouse storage on cloud object storage is typically cheaper per terabyte, and compute is decoupled from storage you pay for query power only when you use it. Traditional warehouses often bundle storage and compute, which can be simpler to budget but more expensive at scale.
03 Flexibility
If your roadmap includes AI and ML use cases, a lakehouse gives data scientists direct access to raw and semi-structured data without a separate export pipeline. If your needs are purely reporting and dashboards with stable schemas, a warehouse's guardrails can mean less to manage.
04 Our recommendation
For most organisations we work with in Africa's financial services, health, and public sectors, a lakehouse architecture wins on flexibility and cost as data volume and use cases grow but the right call always starts with your current reporting needs, not a hypothetical future one.
Salcon Team
May 2025
