In August 2012, Knight Capital, a major trading firm in the United States, deployed faulty trading software to its production system. The system used this incorrect configuration data and it triggered

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A comprehensive guide to data quality covering the types of data errors developers commonly introduce (required field errors, format errors, range errors, logical consistency errors, duplicates, relational errors, and structural errors), the six pillars of good data (completeness, uniqueness, validity, timeliness, accuracy, consistency), and the five validation layers every application should implement (frontend, backend, database, service/business logic, and data ingestion). Includes practical code examples in HTML/JavaScript and Laravel PHP, plus testing strategies covering unit, integration, and functional tests to protect data integrity throughout the pipeline.

The Data Quality Handbook: Data Errors, the Developer's Role, and Validation Layers Explained.

Testing Strategies to Protect Data Quality