Lean MVP Development Via SQL Prototypes: Fast ETL Pipelines, Temporary DWHs, and Accelerated Validation of Product Hypotheses

Abstract

This paper discusses the use of lightweight SQL prototyping for rapid ETL pipeline construction and MVPs in terms of enabling temporary data warehouses and accelerating product hypothesis validation. It lays out, formalizes, and tests aspects of a Lean Analytical Circuit Building approach based on declarative SQL language such that verifiable metrics may be available to a team without waiting for long procurement and infrastructure approval cycles. Relevance comes with high uncertainty at the beginning of both a startup and product-project development, when classic corporate data warehousing takes from six months up to two years to deploy, thus injecting great schedule and budget risk on top of this, reducing the speed of validated learning through loss of the value of data due to lack of operational feedback. It is new in the mixture of three-tier architecture (Staging, Transform, Data Mart), usage of any current DBMS or cloud engines (in temporary cluster mode, DuckDB, ClickHouse, BigQuery Sandbox), content analysis regarding availability of SQL skills and economic risk assessment together with a systematic comparative and instrumental analysis of performances of prototypes. The main finding is that the cycle from event arrival to target table update can fit within fifteen minutes which means fulfillment of a requirement that needs fast reaction for changes in user behavior and marketing campaigns while keeping the flexibility on the structure level among SQL prototypes preserving transparency and reproducibility plus automatic policies deleting obsolete data and serverless sandbox mode controlling costs. A smooth transition from the temporary solution to stable platforms, according to the Infrastructure as Code principle, minimizes operational risks and ensures continuity of metrics. The article will be helpful to startups and product teams, data engineers, and business analysts seeking to combine the speed of Lean methodology with data reliability.