Puredata watches every table, column, and pipeline your numbers depend on — and pages you the moment freshness slips, a null rate spikes, or a metric drifts off its baseline. The broken row gets caught here, not in the board deck three weeks later.
$ puredata incidents show 4812
INCIDENT 4812 severity=high status=open opened 6m ago
table analytics.fct_orders (Snowflake / PROD)
✗ freshness last load 3h 41m ago SLA 1h BREACHED
✗ volume 18,204 rows expected ~92k -80%
✗ null_rate customer_id 14.7% baseline 0.2% +73x
✓ schema 17 columns unchanged
[root cause] upstream dbt model stg_orders failed at 02:14
[blast radius] 9 downstream models · 3 dashboards · 1 ML feature
[owner] @data-eng — paged via PagerDuty + #data-alerts
→ snooze 1h ack open dbt run log notify consumers
✓ auto-resolves when freshness < 1h and volume in bandSits on top of the warehouse and pipelines you already run
Freshness, volume, schema, distribution, and lineage — monitored continuously across every warehouse table and pipeline, so a silent failure upstream never reaches the people who trust the number downstream.
Point Puredata at a table and it learns the normal shape of every column — seasonality, weekday dips, slow-growth trends — then alerts only when reality leaves the band. No static thresholds to set, no rules to rewrite as the data evolves. Models retrain nightly, so a healthy Monday spike never wakes you up.
Declare an SLA in one line; Puredata tracks when each table last loaded and how many rows arrived. Late, empty, or doubled — you hear it before your stakeholders do.
Null rates, uniqueness, ranges, accepted values, and cardinality watched per column. Catch a fivefold null spike the hour it lands, not at quarter close.
A live map from raw source to dashboard. Every incident names exactly what breaks downstream: which models, reports, and ML features go stale if you let it ride.
A dropped column or retyped field upstream fires a diff and a heads-up to every consumer before it silently breaks their query.
What clean data is actually worth
Real shapes of data going bad — the kind that pass every green dashboard and surface only when a number is already in front of the wrong person.
An Airflow DAG failed silently at 2 a.m. and the orders table just… stopped updating. Dashboards kept serving yesterday's totals as today's. Puredata breached the freshness SLA at 41 minutes and paged on-call before standup.
An upstream join started fanning out and revenue per order drifted 2.1x off baseline — no error, no failed test, just wrong. The anomaly model flagged the distribution shift the same morning, with the offending model named in the alert.
A producer renamed user_id to account_id in a migration. Twelve downstream queries would have returned nulls by Monday. Puredata diffed the schema on deploy and notified every consumer the minute it changed.
Puredata runs where your team already does the work — in the warehouse, in dbt, in version control, and in the channel where you get paged. Set it up in an afternoon and let it stay quiet until something is genuinely wrong.
Connect with a scoped, read-only role. Puredata queries metadata and samples — never your raw PII — and runs entirely on your warehouse compute, so your rows never leave your account.
Define monitors in YAML beside your dbt models, review them in pull requests, and ship them through CI. Your data tests live next to the data they protect.
Route incidents to Slack, PagerDuty, or Opsgenie with full context — the failing check, the root-cause model, and everything downstream that goes stale — all in the first message.
Every check, incident, and lineage edge is exposed over a typed REST API and webhooks, so you can wire data health into your own runbooks and status pages.
“A pipeline silently dropped 60% of our orders table for two days and our exec dashboard never blinked. Puredata would have caught it in eleven minutes. We bought it the week after that fire.”
“We deleted four hundred hand-written dbt tests and let Puredata's anomaly detection cover them. Fewer false alarms, and it catches drift those static tests never could.”
“When a number looks wrong now, the first question isn't 'is the data broken' — Puredata already told us, and the lineage map already shows everything it touches.”
Per-row pricing punishes you for monitoring more of your data. Puredata charges for the tables you watch, so you can cover the whole warehouse without ever watching a meter.
For small teams putting their first tables under watch.
For data teams running production pipelines.
For regulated, multi-warehouse data platforms.
No. Puredata connects with a read-only role and works from table metadata, row counts, and lightweight column statistics. Profiling runs as queries on your own warehouse compute, so your raw rows and PII never leave your account.
dbt tests are static assertions you write and maintain by hand. Puredata adds learned anomaly detection on top — it models the normal behavior of every column and catches freshness slips, volume drops, and distribution drift that no fixed test anticipated. Most teams keep their critical dbt tests and let Puredata cover the long tail.
About an afternoon. Connect your warehouse with a scoped role, point Puredata at a schema, and it auto-generates baseline monitors within ninety seconds. From there you add custom SQL checks and route alerts to Slack or PagerDuty as you go.
Snowflake, BigQuery, Databricks, Redshift, and Postgres for warehouses, with native lineage from dbt and orchestration signals from Airflow and Dagster. Looker and Tableau assets appear as downstream nodes on the lineage map.
The models learn seasonality and weekday patterns, so a normal Monday spike or month-end batch won't trip an alert. Every incident ships with severity, root cause, and downstream impact, and you can tune sensitivity per table or mute known-noisy columns in one click.
Yes. Enterprise runs Puredata fully inside your VPC or as a private deployment, with data residency by region, SSO, SCIM, and audit logs. Your telemetry never co-mingles with another customer's.
Connect a warehouse, put your first tables under watch, and get your first incident alert today. No sales call required to start.