Some of the big whales of BI such as Tableau and PowerBI took a step further and allowed users to define relationships between datasets, dimensions, and metrics in the UI that added some guardrails. However, in a modern context, this is too much of an ask from the users: data is massive, complex, and most data users don't know enough about it to be able to write SQL themselves.
Most BI tools adopted a hands-off approach to data quality by expecting the users to bring the properly cleaned and modeled data into the tool, optimizing for seamless SQL-to-chart flow. However, we can't forget about the business intelligence (BI) layer that serves as the front end to data and, therefore, plays a huge role in ensuring that the data users are making their decisions by looking at the right data in the right way. Too often, discussions about data quality are centered on issues in ETL pipelines: missing values, breaking changes, infra downtime, and so on. To explain why Lightdash matters, it's helpful to contemplate what's so special about Looker (besides it being acquired by Google for $2.6B) and how it managed to become the most data-quality-aware BI tool on the market. It may not be as mature as other open-source products like Metabase, Querybook, or Superset, but it is different in a few essential ways. Lightdash is an open-source alternative to Looker that natively integrates with dbt.
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