I'll drop my two cents here: having multiple layer validations reduce the effort needed to find the root cause of a data incident, but it has a drawback: they are harder to maintain.Every layer has a set of rules to be enforced and there will be asse...
Hi @Coders, I'd also consider some profiling checks for column stats and distribution just to be sure everything is consistent after the migration.Afterwards, you should consider the best-practice of implementing some data quality validations on the ...
Hi @laksh!You could take a look at Rudol Data Quality, it has native Databricks integration and covers both basic an advanced data quality checks. Basic checks can be configured by non-technical roles using a no-code interface, but there's also the o...
Hi Kash!I know it might be too late, but if you managed to create this by yourself and you are struggling to scale the solution you could take a look at Rudol Data Quality, it covers up pretty much everything you mentioned with a focus on enabling no...
Hi there!You could also take a look at Rudol, it has native Databricks support and covers Data Quality validations and Data Governance enabling non-technical roles such as Business Analysts or Data Stewards to be part of data quality as well with no-...
