As a modern data solution, it's widy discussed about having multi layered architecture(like Medallion architecture in databricks) in data projects. This is adds work but also helps in streamlining the data flow and process quality data. 1. Data correction from source based on first 2 layers data analysis will help in minimizing the sata inconsistency at gold layer. 2. Having better understanding about final outcome will help in deciding the data load and process strategy. I would prefer to solve the issues with above mentioned 2 activities and based on complexity we can decide further.

First, you should determine if anyone cares about it or is using that data. Don't waste your time on things that aren't important.

- Set up automated validation rules and cleansing mechanisms as part of the ETL pipeline, to check missing values, duplicates and data formats. - Organize the data warehouse into layers, such as staging, transformation, and serving layers. This approach allows you to isolate issues at specific stages, making it easier to scale and troubleshoot inconsistencies. - Implement a metadata management tool, to maintain data lineage, track transformations, and understand the origins of inconsistencies. - For large datasets, partition and index your tables based on frequently queried columns. Partitioning optimizes storage and retrieval, which helps maintain consistency and performance as the data warehouse scales.

Well first of all, I would review all AI generated questions by a human expert if they - make sense at all - are easy to understand - are not ambigous or contradictive