Data Observability
Data observability monitors pipeline health (freshness, volume, schema changes). Data quality validates the data itself (accuracy, completeness, consistency). Most teams need both.
Data Quality
Schema changes, stale tables, and anomalies break Snowflake and Databricks pipelines silently. Here's how data quality monitoring works on both platforms, where built-in tools fall short, and how to set it up.
Data Engineering
The 2026 State of Data Engineering Survey found that data teams spend 34% of their time on data quality and 26% firefighting. That's 60% of the week reacting to problems. The numbers reveal a vicious cycle: pressure to move fast creates the fires that slow teams down.
Data Quality
You sign up for a data quality tool. You land on an empty dashboard. There's a button that says "Add Connection." You click it, paste your credentials, wait for discovery to finish, and then... nothing obvious to do next. You poke around. Maybe you find a
ai
Most "AI-powered" data quality tools just add a chat interface to a metadata catalog. That's tactical AI. Strategic AI doesn't wait for your questions. It investigates root causes, recognizes patterns across incidents, and tells you what to fix before anyone notices something is broken.
Data Quality
Every data quality vendor checks the same feature boxes. What actually separates good tools from mediocre ones is time to value, noise control, pricing transparency, and workflow integration. Here's what to evaluate and the questions to ask before you buy.