?Implement real-time validation at the data source to detect errors early. ??Use schema enforcement to prevent format inconsistencies. ??Leverage anomaly detection models to flag outliers dynamically. ??Integrate automated data reconciliation between source and destination. ??Employ distributed processing frameworks to handle high-speed data ingestion. ??Set up alerts and dashboards for proactive monitoring. ??Use deduplication techniques to eliminate redundant records. ??Ensure collaboration between data engineers and analysts for continuous quality improvements.

To ensure data quality in real-time ETL processes, validate data at the source for format, completeness, and structure. Set up real-time monitoring with alerts for issues. Clean and standardize data during processing, sending bad records to a separate location. Only load new or updated data to reduce errors. Measure data quality, collaborate with data owners, and use machine learning for complex issues. Track data lineage and perform periodic checks. Build fault-tolerant systems with retries or backups. These strategies ensure high-quality data in real-time ETL processes.