Asking the data warehouse a question in plain language
Someone without SQL types a business question — revenue by state last quarter, repeat-purchase rate by acquisition cohort — and the system writes a query against the warehouse, runs it and returns a table or chart. The generated SQL is shown alongside the answer so it can be checked by somebody who reads SQL.
- Effort
- Weeks of work
- Skill level
- Comfortable with software
- Organisation size
- Mid-market
- Value
- Time saved
Tools named for this
- A text-to-SQL layer sitting on the warehouse
- A curated semantic layer that names the joins and defines each metric once
- A read-only role with row filters, so a generated query can never exceed the asker's own access
What to check before you ship it in India
- Plain language removes the friction that used to keep customer-level tables away from casual questions. Consent under section 6(1) is limited to the personal data necessary for the specified purpose, so the boundary has to be enforced by the database role the query runs as, never by what the asker thought to type.
- Small rewordings of the same question can change the query that gets generated; diagnostic benchmarks were built specifically because these systems are vulnerable to that kind of perturbation.
Sources
Every claim on this page traces to one of these, on the date it was read.
- The Digital Personal Data Protection Act, 2023 (No. 22 of 2023) — most obligations commence 13 May 2027 under the DPDP Rules 2025 — s.6(1) · Ministry of Electronics and Information Technology · a rule · read 2026-09-01
- Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs (BIRD) · arXiv (Li et al.) · how it is done · read 2026-09-01
- Dr.Spider: A Diagnostic Evaluation Benchmark towards Text-to-SQL Robustness · arXiv (Chang et al., AWS AI Labs); ICLR 2023 · how it is done · read 2026-09-01
- Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task