What do you do if your data engineering project is falling behind schedule?
Data engineering projects often involve complex and interdependent tasks, such as extracting, transforming, loading, and analyzing data from various sources. Sometimes, these tasks can take longer than expected, or encounter unexpected challenges, such as data quality issues, technical glitches, or changing requirements. When your data engineering project is falling behind schedule, what can you do to get back on track and deliver value to your stakeholders? Here are some tips to help you manage your time and resources effectively.