Proven Best Practices for Managing Data Quality in Hadoop Systems

By 2025, global data volumes are projected to surpass 175 zettabytes, with the Hadoop market expected to reach $851 billion by 2030. Poor data quality costs businesses an average of $15 million annually in operational inefficiencies and lost opportunities. To navigate these challenges and fully harness Hadoop's potential, organizations must adopt best practices for managing data quality, including establishing clear data governance, validating data during ingestion, leveraging version control, continuously monitoring data quality, optimizing storage through partitioning, and using machine learning for proactive management.