PILLAR 01-04
Data Quality Monitoring That Catches Broken Data First
Automated data quality monitoring across every table in your warehouse, so a stale sync or a bad load never reaches a dashboard unnoticed.
14-day trial, no credit card, read-only connection
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Downstream impact · consumers at risk
What is data quality monitoring?
Data quality monitoring is the continuous, automated checking of your data for freshness, volume, schema, and anomaly issues. Dataobservability watches every table you connect, learns its normal behavior, and alerts your team in Slack or PagerDuty the moment something breaks, with the downstream impact already mapped.
Last updated August 2026
What you get
Built for data quality monitoring
Automated checks, not hand-written rules
ML-tuned monitors learn each table's normal range for volume, nulls, and distribution, so you catch the breakages you never wrote a test for.
Freshness and volume SLAs
Set freshness SLAs per table and get alerted when a load is late or a row count swings beyond its forecast band.
Schema change detection
A renamed or dropped column is caught instantly and traced to everything downstream that depends on it.
Low compute, low noise
Metadata-first checks keep warehouse cost tiny, and alert grouping keeps your channels clean.
How it works
From connected to caught
Connect your warehouse
Read-only metadata access to Snowflake, BigQuery, Databricks, or Redshift in a few minutes.
Auto-generate monitors
We create freshness, volume, schema, and anomaly monitors on every table, dbt-native if you use dbt.
Tune and route
Set SLAs and severities, route alerts to Slack or PagerDuty, and group related alerts into one incident.
Catch and resolve
When data breaks you get an alert with root-cause hints and the downstream blast radius, then track it to resolution.
What data quality monitoring actually watches
Five signals cover most of what breaks. Freshness: did this table load when it was supposed to, measured against warehouse write metadata rather than a timestamp column the source controls. Volume: did the load deliver a row count inside the range this table normally delivers on this day of the week. Schema: has a column been added, dropped, renamed, or retyped since the last run. Distribution: has the shape of the values moved, a null rate climbing from 2 percent to 30, a currency field whose average jumped by a factor of 100, a category that vanished. Lineage: what depends on this table, so a break becomes an impact statement instead of a puzzle. The first four are detection and the fifth is triage, which is why monitoring without lineage tends to produce alerts nobody can prioritize.
Monitoring versus testing, and why you need both
A test is an assertion you wrote: this column is unique, this value is one of four options, this total reconciles. It runs on every build, it fails the job, and it catches exactly the failures somebody predicted. Monitoring learns what each table normally does and flags deviation, which catches the failures nobody predicted: the vendor who dropped a field in a minor release, the sync that paused over a holiday weekend, the upstream team that changed cents to dollars. Testing is precise and does not scale past the tables an engineer worried about. Monitoring is broad and cannot infer your business rules. Teams that run only tests get silent failures on the 200 tables nobody wrote tests for. Teams that run only monitors miss the revenue definition bug. Run tests on rules and monitors on everything.
The compute bill nobody budgets for
Every quality check is a query, and every query costs credits. A platform that runs SELECT COUNT(*) plus full-column profiling across every table on every schedule can add a real percentage to a Snowflake or BigQuery bill, and it shows up in a finance review months later attached to a service account nobody recognizes. Metadata-first monitoring avoids most of that: freshness, volume, and schema are all derivable from the information schema, query history, and dbt artifacts without reading a single row. Only distribution checks need to touch data, and those can sample. When you trial any monitoring tool, tag the warehouse role it uses and pull the credit consumption for that week. That number belongs in the comparison next to the license fee, because for scan-heavy tools it can be the larger of the two.
Why most monitoring projects die of noise
The failure mode is predictable. A tool generates monitors on 400 tables, thresholds are untuned, and the first week produces 300 alerts. Somebody mutes the channel. Six weeks later a real incident fires into a channel nobody reads. The fixes are unglamorous: learned baselines instead of static thresholds so normal seasonality does not page anyone, grouping so one upstream break produces one incident rather than 40 downstream alerts, severity routing so hard failures reach PagerDuty and soft anomalies land in Slack, and a monthly review where any monitor under 50 percent true positives gets retuned or deleted. The metric that matters is not how many tables you monitor. It is what fraction of alerts led to a fix.
Questions buyers ask
Data quality monitoring FAQ
What is data quality monitoring?
Data quality monitoring is the continuous, automated checking of production data for problems: tables that load late, row counts that drop, schemas that change, and values that drift from their normal distribution. It runs against your warehouse on a schedule, learns what normal looks like per table, and alerts a team when something deviates, so broken data is caught before it reaches a dashboard.
What is the difference between data quality and data observability?
Data quality is the outcome you want: data that is fresh, complete, and correct. Data observability is how you see whether you have it, by continuously monitoring freshness, volume, schema, distribution, and lineage across every table. Quality tools often focus on validating and fixing records. Observability focuses on detecting breakage in production and showing what it affects downstream.
How do you monitor data quality?
Connect a read-only role to your warehouse, generate monitors for freshness, volume, schema, and distribution on every table, then add hand-written tests for the business rules a machine cannot infer. Route hard failures to PagerDuty and soft anomalies to Slack, group related alerts into one incident, and review true-positive rates monthly to keep noise down.
What are the 6 dimensions of data quality?
Accuracy, completeness, consistency, timeliness, validity, and uniqueness. Monitoring automates timeliness (freshness), completeness (volume and null rates), and consistency (schema and distribution) because those are inferable from metadata. Validity and uniqueness are normally asserted with tests. Accuracy, meaning the data matches the real world, needs a reference source or a human.
What are data quality metrics?
The ones teams actually track are freshness lag (how far behind a table is against its SLA), row-count variance against a forecast band, null rate per column, duplicate rate on keys, schema change frequency, and distribution drift. Operationally you also want time to detection and time to resolution, since those tell you whether monitoring is doing its job.
How much does data quality monitoring cost?
Self-serve tools that publish a price start low: Dataobservability is 99 dollars a month for Starter, 299 for Team, 799 for Scale. Datadog prices its quality monitoring per monitored table. Enterprise platforms are quote-only and land in five to six figures a year. Budget warehouse compute too, since scan-heavy checks can cost more in credits than in license. Checked July 2026.
More of the platform
Catch broken data before your stakeholders do
Connect your warehouse and get data quality monitoring live from one read-only connection. Transparent pricing, no credit card.