Data observability, questions answered

Data observability is the practice of continuously monitoring the health of your data and pipelines across five pillars: freshness, volume, schema, distribution (anomalies), and lineage. A data observability platform automatically detects when data arrives late, when row counts swing unexpectedly, when a schema changes, or when values drift, then alerts the data team and maps the downstream impact so problems get caught before stakeholders see broken numbers.

Dataobservability is transparently priced and self-serve: Starter is 99 dollars per month, Team is 299 dollars per month, and Scale is 799 dollars per month, billed yearly, with Enterprise priced on request. There is no six-figure contract and no contact-sales gate just to see a price. You can start with a 14-day free trial and no credit card.

Most teams are live in about 15 minutes. You connect your warehouse with read-only metadata access, and if you use dbt we auto-generate monitors from your manifest. All five pillars are active on day one, and lineage comes free from the same connection.

No. Dataobservability is metadata-first: most monitors read warehouse metadata and information-schema statistics rather than scanning full tables, so the compute footprint stays tiny. Heavier checks are sampled and scheduled, and you control the cadence per table.

Alerts are tuned to be acted on, not ignored. ML-based thresholds learn each tables normal behavior, related alerts are grouped into a single incident, and you set severities and routing per monitor. The result is fewer, higher-signal alerts in Slack or PagerDuty.

We connect with read-only access and are metadata-first, meaning we read metadata and statistics rather than copying your rows. Enterprise plans offer an in-VPC deployment so data never leaves your environment, plus SOC 2 controls, SSO, and an audit log.

dbt tests and assertion frameworks check rules you write at run time, which is valuable but only catches the failures you anticipated, only when a model runs. Data observability adds always-on, ML-tuned monitoring across freshness, volume, schema, and anomalies on every table, plus end-to-end lineage and incident tracking, so you also catch the breakages you did not think to write a test for.

Dataobservability connects to Snowflake, BigQuery, Databricks, and Redshift, is dbt-native, and reads orchestration signals from tools like Airflow and Fivetran. Alerts route to Slack and PagerDuty, and lineage spans your warehouse through to BI tools like Looker.

No. Monitors and lineage are generated from your warehouse and dbt project, which you own. There is no proprietary modeling layer to rebuild and no long-term contract on self-serve plans. You can export your configuration and cancel anytime.