For a long time, companies relied on traditional data systems to move and clean information. These systems followed fixed rules, ran on schedules, and were good enough when data was simple and change was rare.
But that’s no longer the world we live in.
Today, data is messy, fast changing, and comes from dozens of sources such as APIs, files, forms, third party vendors, and internal systems. The structure of that data can change without notice. Columns are renamed. Formats shift. New fields appear or disappear overnight.
And when that happens, traditional systems don’t adapt. They fail. Sometimes they crash loudly. Other times they quietly produce wrong results and no one notices until a dashboard looks wrong or a model makes a bad prediction.
These pipelines were built to m
Real-Time Anomaly Detection and Auto-Correction in Data Workflows
- By Farhan Kaskar
- Published on
This isn’t just automation. It’s autonomy with context, control, and accountability built in.
