Data governance has historically been the least glamorous part of data engineering. Engineers thrive on building things, designing scalable pipelines, curating high-quality datasets, and enabling machine learning models that deliver real business impact due to business demands. Governance, on the other hand, is often seen as red tape, including permissions, audit logs, compliance checks, and documentation. It doesn’t feel exciting, and it rarely gets prioritized until it’s too late.
That’s why, in many organizations, governance becomes an afterthought. Teams launch pipelines into production, datasets grow, and dashboards multiply. Business users rely on the insights daily, and ML models start to influence critical decisions. But then comes the compliance request, “Who accessed customer emails last quarter?”, “Can we guarantee PII is masked in this dashboard?”, “Where did this KPI originate?” Suddenly, the lack of a centralized governance framework is exposed. Access controls are fragmented across Hive Metastore, cloud IAM, and ML registries. Lineage is incomplete, forcing engineers into manual log-diving. Masking rules are inconsistent, often implemented with brittle regex that only works for part of the data. The governance story is fragile and reactive, not proactive.
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