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

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.

Design a Robust Data Architecture : Centralize Data Sources, Implement Schema Standards & Adopt Modular Design Automate Data Validation and Quality Checks: Adoption of Data Profiling tools, enablement of Automated Audits & Anomaly Detection Enforce Governance Policies via ownership definition, standardization of data policies and comprehensive metadata management Building Scalability into Data Pipelines through parallel processing, batch and streaming modes & proper version control Implement Strong Security Measures for enterprise wide data consumption and dissemination Enablement of strong data stewardship across all Business Units

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.

To ensure data integrity while scaling a data warehouse, I focus on implementing strong data validation rules and automated checks to catch errors early. Regular audits and monitoring help identify inconsistencies, while establishing clear data governance policies ensures accountability. Additionally, leveraging robust ETL processes can help maintain quality during data migration and integration.

To ensure data integrity while scaling your data warehouse, adopt modern practices like schema enforcement, ACID-compliant storage layers (e.g., Delta Lake), and incremental processing to prevent inconsistencies. Leverage data quality tools (e.g., Great Expectations, Delta Live Tables) and observability platforms for real-time monitoring and validation. Implement robust data governance with access control and lineage tracking using tools like Unity Catalog. Automate testing, use idempotent pipelines, and optimize partitioning to handle failures and performance bottlenecks effectively.

To ensure data integrity while scaling your data warehouse, implement robust data governance with clear policies, ownership, and stewardship roles. Use schema evolution management to handle changes without compromising consistency. Validate data through ETL pipelines with checks for duplicates, anomalies, and consistency. Employ database constraints like foreign keys and unique indexes. Regularly audit data with quality checks and reconciliation processes. Utilize backup and recovery plans to safeguard against loss or corruption. Leverage automation and monitoring tools for real-time anomaly detection. Finally, proper security controls should be ensured to prevent unauthorized access.

To keep data integrity intact as you scale, prioritize strong governance, integrate smart validation into ETL processes and build a scalable architecture that considers potential risks. A skilled team is key to balancing technical demands with business goals for sustainable growth.