What is a data lakehouse?
A data lakehouse is an architecture that combines a data lake's cheap, open storage of raw and unstructured data with a data warehouse's structured tables, schemas and SQL querying. It aims to give one platform for both machine learning and business analytics instead of two separate systems.
A lakehouse layers warehouse-style features (transactions, schema enforcement, indexing, SQL performance) directly on top of low-cost object storage that holds files in open formats. The goal is to avoid keeping two copies of your data in two systems: a lake for data science and a warehouse for reporting.
Typical lakehouse traits:
- Open table formats so many engines can read the same files.
- ACID transactions for reliable updates on lake storage.
- Schema and governance on top of otherwise raw data.
- One store serving BI dashboards and ML training alike.
A lakehouse is still infrastructure you design, load and maintain, and business users generally reach it through SQL or a BI tool on top.
iDBQuery complements this rather than replacing it. If your organisation runs a lakehouse, iDBQuery connects to it as a standard SQL warehouse and lets anyone ask questions in plain language, getting a cited answer without writing SQL. And if you do not have a lakehouse, iDBQuery can build one live model directly across your existing databases, spreadsheets and files, so you can start answering questions before any lakehouse project exists.
Updated 2026-06-22