The text discusses the risks associated with code changes in data pipelines, emphasizing the need for advanced tools to help data engineers understand the impact of these changes on interconnected data systems. Unlike software engineers, data engineers lack mature tools to predict such impacts, which can lead to significant negative outcomes, like financial losses or flawed decision-making. Data lineage emerges as a crucial solution, offering a graphical representation of data connections within databases, which aids engineers in making informed design decisions. However, developing data lineage systems is challenging, with varying quality among vendors. Data lineage is particularly significant in ensuring data governance and regulatory compliance, like adhering to GDPR, by automating the detection of upstream and downstream effects of data changes. The complexity of column-level data lineage, due to diverse SQL dialects, highlights the ongoing need for refinement in these tools. As interconnected data systems grow, the role of data lineage is becoming indispensable, offering time, effort, and cost savings while improving compliance and decision-making processes.