Data architecture · 2 minutes
Data Architecture Fit Assessment
Find out whether a data warehouse, a data lake or both fits your data, your users and your starting point. Built for data leads, architects, IT directors and analytics managers. It takes about two minutes. No sign-up needed.
Answer all 8 questions to see your result.
How the assessment works
Each answer adds points to a Warehouse score, a Lake score or both. Algoscale converts each score to a percentage of its maximum. A score of 50% or more counts as high. Two high scores mean Lake + Warehouse. One high score points to Warehouse-led or Lake-led. Two low scores mean Start Small.
Four flags can add extra guidance: evaluate a lakehouse, keep your warehouse, governance first and favor managed platforms. Your result shows exactly how each answer moved the score. Nothing is hidden and nothing is generated by AI.
What each result means
Warehouse-led
Governed, repeatable analytics is the priority. A well-designed warehouse gives business users consistent numbers without needing to understand raw source systems.
Lake-led
Varied data and ML or exploratory workloads come first. A lake lets you retain data before every future use case is defined.
Lake + Warehouse
You need both flexible data processing and governed analytics. This is often the most practical enterprise pattern.
Start Small
Your needs don't yet justify a large platform. Begin with a focused warehouse for your most important reporting and grow from there.
Limitations
This is a starting point, not an architecture design. Real decisions also depend on data volumes, existing contracts, skills and budget. For the full comparison, read data lake vs data warehouse.