Common Mistakes in Data Engineering | IABAC
Common data engineering mistakes include poor data quality management, inefficient pipeline design, lack of scalability, ignoring data security, and inadequate monitoring. Avoid these by implementing robust testing, clear data governance, scalable architecture, and continuous performance monitoring.
https://iabac.org/data-sci...
Common data engineering mistakes include poor data quality management, inefficient pipeline design, lack of scalability, ignoring data security, and inadequate monitoring. Avoid these by implementing robust testing, clear data governance, scalable architecture, and continuous performance monitoring.
https://iabac.org/data-sci...
07:40 AM - Apr 03, 2025 (UTC)
