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Not All Data Errors Throw Exceptions: How Business Logic Violations Quietly Ruin Trust
In today’s world of big data, distributed systems, and real-time analytics, data quality has quietly become the backbone of trustworthy decision-making.
And yet, most teams still treat it like an afterthought.
If you’ve ever had a dashboard show inflated revenue, missing product names, or duplicate customer records — even though the pipeline didn't fail — you’ve run into what I call the silent killers of data engineering:
➡️ Business logic violations
➡️ Lack of referential integrity
➡️ Late-arriving data
➡️ Random, unpredictable input sources
In this post, we’ll explore why data quality has become harder to manage in modern data engineering — and how to build smarter, business-aware pipelines that don’t just move data, but trust it.
🧩 Data Quality Is No Longer Just a Technical Problem
Most engineers start with quality checks like:
Is the field null?
Did the schema change?
Did ingestion fail?
