Sampling, also known as stare and compare,is the most notable form of data validation Source-to-target mapping: a set of data manipulation rules.They control how the structure and contents of data in the source system are adapted to the requirements of the target system Test cases have two sets of SQL queries: One query takes data from the sources. The second query extracts data from the target SQL editors like squirrel, toad are used to implement the tests The test results from the two queries are stored in two Excel spreadsheets Physically compare all the result sets in the original spreadsheet with the target spreadsheet by eye A minus query uses the minus operator in SQL to find the difference between two datasets

Here’s how I ensure success: 1.Robust Design - Start with a solid schema design that enforces constraints and relationships to keep data clean from outset. 2. Implement automated checks and validations within ETL pipelines to detect and address data issues proactively. 3. Establish clear governance policies to ensure consistency, accountability, and compliance across all teams and processes. 4. Resilient Systems -Use version control, backups, and robust transaction management to safeguard data during scaling activities or migrations. 5. Adopt scalable platforms like Snowflake to handle growing workloads while maintaining performance and integrity. These practices ensure your DWH is ready for growth without compromising on data quality.

The question is: "how will you scale your data warehouse?"; you will change: > your database hardware like sharding or partition strategy. > your data modeling from star to data vault or add new entities or column. > your derived analytical components like data mart or exploration data warehouse. and so on. The data integrity of the entity depend how one defines its relations, fields and processes in all related assets interact with it: data pipeline, data storage, processes, etc.