Most data teams discover issues when users complain or dashboards break.
When your data flows through Datadog (monitoring), Snowflake (warehouse), BigQuery (warehouse), and Databricks (processing), you can build data observability that catches issues before they impact users.
The 5 data observability pillars that matter
1. Data Freshness
Data freshness monitoring checks whether data is arriving on schedule without delays in pipelines. It identifies when data becomes stale, alerting you before users notice missing updates.
2. Data Volume
Volume monitoring tracks whether row counts are normal, alerting on unexpected drops that could indicate data loss or spikes that might indicate duplication. It ensures data isn’t missing when it should be present.
3. Data Quality
Quality monitoring verifies that values are within expected ranges and there are no unexpected nulls that could break downstream processes. It detects duplicates that could skew analysis results.
4. Data Schema
Schema monitoring detects when schema changes occur, identifying new columns or type mismatches that could break downstream systems. This prevents issues before they impact users.
5. Data Lineage
Data lineage tracks where data came from originally, how it was transformed through various processes, and what systems depend on it. This visibility is essential for understanding impact when issues occur.
What to build first (week 1)
Start with a simple data observability system that includes freshness monitoring to alert when data becomes stale. Add volume monitoring that alerts on unexpected changes in row counts. Implement quality monitoring that alerts when data quality issues are detected. Add schema monitoring that alerts when schema changes occur. Finally, implement lineage tracking to understand how data flows through your systems.
Once you have these fundamentals, add anomaly detection to identify unusual patterns that might indicate problems. Implement impact analysis to understand what breaks if a particular dataset fails. Add root cause analysis to understand why issues occurred. Finally, add automated remediation to fix common issues automatically without manual intervention.
Why most data observability fails
Most data observability fails because monitoring is manual, with checks run once and then forgotten as teams move on. Alerts are noisy with too many false positives that cause alert fatigue. Context is missing when alerts don’t explain what changed or why an issue occurred. Most critically, actionability is low when observability shows issues but doesn’t indicate how to fix them.
When you build data observability right, you can catch issues before they impact users by monitoring continuously. You can reduce false positives with better context that explains why alerts fired. You can understand what changed and why, making debugging faster. Most importantly, you can fix issues faster because you have the information needed to resolve problems quickly.
The hidden cost of poor data observability
When data observability is poor, issues are discovered late after users complain, leaving you scrambling to fix problems. Debugging is slow because there’s no context about what changed or why issues occurred. Trust erodes as your team loses confidence in data systems. Most critically, incidents are frequent because there’s no prevention, only reaction.
Good data observability means issues are discovered early before users complain, giving you time to fix problems proactively. Debugging is fast because you have good context about what changed and why. Trust is high because your team knows data systems are monitored and reliable. Most importantly, incidents are rare because prevention works, catching issues before they become problems.