Can you explain data errors in data warehousing, and describe the difference between OLTP and OLAP systems?
💡 Model Answer
Data errors in a warehouse arise from ETL failures, schema mismatches, duplicate records, missing values, and data type inconsistencies. They can be classified as data quality issues (accuracy, completeness, consistency) or integration errors (mis‑aligned source schemas). Common causes include manual data entry, incorrect mapping rules, or network interruptions during load. To mitigate, implement validation rules, deduplication logic, and automated data quality checks.
OLTP (Online Transaction Processing) systems are optimized for fast, short transactions, use normalized schemas, and support high write concurrency. OLAP (Online Analytical Processing) systems are designed for complex queries over large datasets, use denormalized or star/snowflake schemas, and emphasize read performance and aggregation. For example, an OLTP database might store each sales transaction in a normalized table, while an OLAP cube aggregates sales by product, region, and time for reporting.
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