Data Lake vs Data WarehouseBuilding an AI-Ready Data Platform

Follow data from scattered source systems to trusted dashboards and AI, and switch between a warehouse, a lake and a lakehouse to see what changes.

Walk through eight steps, from scattered spreadsheets to governed data that dashboards and AI assistants can both rely on.

Data lake vs data warehouse vs lakehouse

A data warehouse stores cleaned, structured tables for fast reporting. A data lake stores raw data of any kind cheaply and applies structure when it is read. A lakehouse puts warehouse features on lake storage. Most companies we work with end up with layers of each: raw history, cleaned data and business models.

Attribute	Data warehouse	Data lake	Lakehouse
Best for	Structured business data and fast, trusted reporting	Large or varied data: events, logs, documents, images	Teams that want both, on one copy of the data
How data is stored	Tables in a relational database, schema defined on write	Files in object storage, schema applied on read	Open table formats (Iceberg, Delta Lake) on object storage
Storage cost	Higher per terabyte	Lowest	Low
Query experience	Plain SQL, fast for analytics	Needs a query engine such as Dremio or Spark	SQL through Snowflake, Databricks or another engine
Main risk	Cost and rigidity as data volume and variety grow	Becoming a swamp without modeling and governance	More moving parts to design and operate
AI readiness	Strong for structured data; documents live elsewhere	Holds the documents AI needs, but needs curation first	Tables, documents and vector search in one governed place
Typical stack	PostgreSQL or Snowflake, dbt, Lightdash or Power BI	S3, Kafka, Spark, Dremio, Superset	Iceberg or Delta Lake, Snowflake or Databricks, dbt
Our example	Multi-brand franchisor reporting portal on dbt and PostgreSQL	Restaurant franchisor data lake on Kafka, Spark and Dremio	Snowflake and Databricks consulting

Is your data AI-ready?

Whichever architecture you choose, AI assistants and agents give accurate answers only when the data beneath them is consistent, documented and permissioned. If you can check every item below, you are ready to point AI at your data.

Every source system lands in one place, with history kept
Each customer, product and location has one ID across systems
Key metrics are defined once, in code, and tested on every load
A catalog says what each table means and who owns it
Access rules decide who can see which rows, and apply to AI too
Documents are stored with the records and permissions they belong to

Not sure which architecture fits?

Tell us where your data lives today and what you want from reporting and AI. We will recommend a path.

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