The ability to understand the health and state of data in your systems by monitoring data quality, freshness, volume, schema changes, and lineage in real-time.
The first time I realized we needed data observability was after an exec meeting where the CFO presented revenue numbers that turned out to be off by 12%. A silent schema change upstream had broken our ETL, and the dashboard kept rendering — just with a subset of transactions. No alert. No dbt test catching it. No one noticed for four days.
That story is why I now treat observability as table stakes, not nice-to-have. The traditional data-quality playbook (write tests, schedule them, look at a pass/fail grid) is necessary but not sufficient. It catches what you anticipated. Observability catches what you didn't — the row-count drops, the freshness lags, the distribution shifts, the column that used to be non-null and suddenly isn't.
Observability buys you three things that pure rule-based tests don't: (1) anomaly detection on metrics you never wrote a test for, (2) lineage-aware impact analysis so when a fact table breaks you immediately know the 17 dashboards and 3 ML models that depend on it, and (3) incident runbooks with enough context that the on-call doesn't have to trace through SQL at 2 AM.
At a retail-analytics team I worked with, we rolled out Monte Carlo on top of Snowflake. The first month it flagged a 40% row-count drop on the daily sales fact table at 6 AM — before the 9 AM exec dashboard refresh. Root cause: an upstream Kafka consumer had crashed silently. With manual SLA checks alone, we'd have learned about it from the CMO Slack at 10 AM. Observability pulled the detection window from hours-after-impact to hours-before-impact. That ROI paid for the tooling in one incident.
Data observability is an organization's ability to fully understand the health of data across their data systems in real time. Inspired by software observability (metrics + logs + traces), it applies continuous monitoring, anomaly detection, and root cause analysis to data pipelines, tables, and datasets rather than to services and containers.
analytics.fct_orders been updated in the last 60 minutes during business hours?"fct_orders have row counts within 3 standard deviations of the trailing 30-day mean?"| Aspect | Data Quality | Data Observability |
|---|---|---|
| Focus | Data content (accuracy, completeness, validity) | System health and behavior |
| Timing | Scheduled batch tests | Real-time + continuous monitoring |
| Scope | Individual tables and columns | End-to-end pipelines across systems |
| Primary technique | Rule-based assertions | ML-driven anomaly detection + rules |
| Failure mode caught | "I wrote a test for this" cases | Unknown-unknowns + silent breakage |
| Response | Test pass/fail, halt pipeline | Alert + lineage impact + triage context |
A modern observability setup in 2026 looks like this:
INFORMATION_SCHEMA, ACCOUNT_USAGE (in Snowflake), query history, and information_schema views to capture row counts, freshness, schema, and access patterns.For small teams with <20 production tables, a lightweight approach — dbt tests + a daily freshness SELECT + a Slack webhook — often covers 80% of what a full platform would give you, at 5% of the cost. Commercial observability platforms start paying off around the 50-table / 10-pipeline mark or whenever your first "silent bad data" incident costs more than the annual subscription.
A few things I've learned the hard way about data observability rollouts:
ACCOUNT_USAGE.QUERY_HISTORY so you don't get surprised.Data observability is the continuous, real-time monitoring of your data systems — table freshness, row volume, schema, value distribution, and lineage — so you detect bad data before your users do. It borrows its philosophy from software observability (APM, distributed tracing) but applies it to warehouses, lakes, and pipelines. Unlike rule-based data quality testing, observability uses anomaly detection to catch drift and silent breakage you didn't think to write a test for, and pairs alerts with lineage so the on-call can triage fast.
Data quality answers "is this data correct against the rules I defined?" Data observability answers "is this data behaving like it normally does?" Quality is deterministic, rule-based, and run on a schedule — it catches anticipated problems. Observability is statistical, baseline-driven, and continuous — it catches unanticipated problems (schema drift, freshness lag, distribution shifts, null-rate spikes). You need both: quality for things you care about enough to write a test for, observability for everything else. In mature data teams, observability sits on top of a dbt-tests foundation rather than replacing it.
Freshness (is the data recent?), Volume (is the row count in an expected range?), Schema (has the structure changed?), Distribution (are values within expected ranges and cardinalities?), and Lineage (where did this data come from and what depends on it?). Monte Carlo popularized this framing and most observability vendors have adopted something similar. In practice, freshness and volume catch the majority of silent-break incidents; schema and distribution catch the subtle data-correctness issues; lineage is what turns any of those alerts into a useful incident response rather than a bare notification.
For commercial platforms: Monte Carlo is the market leader with the strongest lineage and incident UX; Bigeye is strong on automated metric coverage and Snowflake integration; Acceldata fits large enterprises with multi-platform estates. For open-source: Elementary is excellent if you're dbt-native; Soda Core + Soda Cloud is a good hybrid of testing and observability; Great Expectations is strong on assertions. If you're Snowflake-only and want zero new vendors, you can build a credible observability layer from Snowflake's Data Metric Functions (DMFs), ACCOUNT_USAGE views, and a dashboard. Start with the cheapest option that covers your top 10-20 tables and expand as ROI proves out.
Data downtime is the period where data is missing, late, or wrong enough to break business decisions. The standard metrics are MTTD (mean time to detect) — the gap between when bad data entered and when your team knew — and MTTR (mean time to resolve). To measure them, log every incident (manual or auto-detected) with timestamps for "issue started," "issue detected," and "issue resolved." Observability tooling makes this automatic. As a sanity check: teams with no observability often have MTTDs measured in days; teams with mature observability have MTTDs in minutes. That delta is where the ROI lives.
Yes — Snowflake is one of the best-supported warehouses for observability because of its rich metadata APIs (INFORMATION_SCHEMA and ACCOUNT_USAGE), query history, and native Data Metric Functions. Monte Carlo, Bigeye, Acceldata, and Elementary all have production-grade Snowflake integrations. A typical setup: observability platform connects via a dedicated monitoring role, runs metadata queries on a dedicated warehouse (so you can cost-attribute), and captures table freshness + row counts + schema + query patterns. Watch the warehouse credits — some observability tooling can spend $1K+/month on metadata queries alone if you don't cap the warehouse size and auto-suspend aggressively.
Start with your critical path: list the 10-20 tables that feed exec dashboards, customer-facing features, or ML models. For each, add three lightweight checks that cover 80% of incidents: (1) freshness — did the table update in the expected window? (2) row count — is today's load within 3σ of the trailing 30 days? (3) key uniqueness — are primary keys still unique? You can implement this with dbt tests + Elementary, or with Snowflake DMFs + a Slack webhook, for essentially zero additional cost. Graduate to a commercial platform once you're past 50 tables or once a silent-data incident has cost more than the platform's annual subscription.