Can I connect a data lake to iDBQuery?
Yes. iDBQuery connects to a data lake either through its query engine or warehouse layer as a standard SQL source, or by ingesting exported files such as CSV and Parquet, building one live queryable model, so you can ask questions across lake data in plain language without moving it into a new system.
A data lake is a store of files, often CSV, JSON, and Parquet, in object storage such as S3, ADLS, or GCS, usually with a SQL query layer on top (a lakehouse engine, an external-table service, or a warehouse). iDBQuery meets a lake in the truthful, practical way rather than claiming to be a lake engine itself:
- Connect the lake's SQL layer as a standard warehouse/database source. If your lake is queryable through a lakehouse or external-table service, iDBQuery connects to that endpoint like any SQL warehouse and reads the tables live.
- Ingest exported files directly. iDBQuery reads Parquet, CSV, and JSON, so you can point it at exported files, including files in S3-style object storage, and it builds a queryable model from them.
Either way you get one live model and can ask in plain language things like revenue by region across lake tables, or how a metric trends over the history stored in the lake, with every figure cited to the source rows. The real advantage is joining lake data to systems that are not in the lake, an operational database, a finance spreadsheet, a SaaS export, so a question can span the lake and everything around it. iDBQuery queries in place where possible and does not force you to build a separate warehouse project or pipeline to get answers.
Updated 2026-06-22