Maintain a record or log of all changes made to a resource while the system is executing normally Focus on multiple dimensions of data integrity: Entity Integrity Referential Integrity Domain Integrity Overcome common threats to data integrity: Data Corruption Human Error Unauthorised Access Replication Issues Ensure that the data reflects real-world values as closely as possible Minimize data discrepancies and focus on fully recording every required data point Enhance data's physical integrity by implementing hardware infrastructure with battery protected write cache Remember that Logical Integrity can be affected by poor software design, software bugs Malware, hacking, other cyber threats can lead to loss of data integrity

Most of my peers here have already touched on data validations, data quality and error handling, which is key to maintaining data integrity. My two cents would be to consolidate and automate data entry at source. If systems don’t allow manual key ins, whether it be for customer names or countries, and instead provide predefined data sets across the corporation, it not only reduces data errors, but also bridges gaps between departments to result in effective reporting and other downstream processes.

1. Leverage Azure Data Quality Tools: Use Microsoft Purview and Fabric Data Quality to enforce data integrity rules and monitor data health. 2. Implement Data Validation in ADF: Use ADF’s data flow transformations like "Assert" and "Derived Column" to validate and cleanse incoming data. 3. Utilize Delta Lake Features: Enforce schema evolution and constraints in Azure Databricks to maintain consistency. 4. Monitor with Azure Monitor & Log Analytics: Set up alerts and logs for anomalies in data pipelines. 5. Enable Row-Level Security in Synapse: Protect data integrity by restricting unauthorized access to sensitive records.

To maintain data integrity in a data warehousing project while ensuring speed and accuracy, follow these key practices: Data Validation & Cleansing: Remove duplicates, handle missing values, and use profiling tools like IDQ. Referential Integrity: Enforce primary/foreign keys, apply data governance rules, and reconcile records. Optimized ETL: Use incremental loads, error handling, and robust tools like Informatica PowerCenter/IICS. Performance Tuning: Leverage indexing, partitioning, pushdown optimization, and CDC. Auditing & Monitoring: Implement audit tables, logging frameworks, and real-time dashboards.

In my experience, first thing to do is to fully understand your business requirements to follow the right path in data cleansing and maintain the quality of your data. Once you understood the benefits of integrating this data, you will be able to set the right quality rules and find anomalies easily in the data, thus maintaining higher reliability and trust on received data.

Data Integrity is key for any data driven organisation. With proliferation of various apps,systems and user interfaces, maintaining integrity and consistency is make informed and consistent decision. Things that worked in my experience are > Data Validation and auto handling of errored data. > Robust Metadata management > Standardisation of engineering and data modelling to reap benefit of consistent and coherent data in ever changing front end application and data source systems. > Auditing and reconciliation >

I build Ai agents who keep track of all changes and can produce Reports. Data get automatically validated, integrity is given at all times as well as 24/7 monitoring

Maintaining data integrity in a fast-moving data warehouse environment requires a structured approach to governance and automation. At WhereScape, we focus on: - Automated Data Lineage & Documentation – Tracking every transformation ensures full transparency, making it easier to audit and troubleshoot data discrepancies. - Built-in Data Validation & Quality Checks – Automated validation at ingestion and transformation stages helps detect and correct anomalies before they impact reporting. - Metadata-Driven Development – Defining transformations and dependencies in metadata reduces manual errors and ensures consistency across environments. Balancing speed and accuracy is critical for reliable analytics.