For teams
Data Reliability Engineering With Real Observability
Run data reliability engineering like SRE for data: set SLAs, monitor every pillar, track incidents, and measure data downtime over time.
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Alerted #data-eng 0.8s ago.
Downstream impact · consumers at risk
In one paragraph
Data reliability engineering applies SRE practice to data: agree targets for freshness and quality, detect breaches automatically, and track incidents so data downtime goes down. DataObservability supplies the instrumentation. It checks every monitored table hourly across five pillars, opens one incident per break and closes it when the check passes, routes to Slack or PagerDuty, and keeps 90 or 365 days of history for trend reporting. Plans are published, from 59 dollars a month billed yearly, with a 14-day trial and no card.
Side by side
Data reliability engineering tooling compared
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| Approach | What you can measure | Where it stops | Published price |
|---|---|---|---|
| Incident spreadsheet plus orchestrator alerts | Job failures and whatever someone remembered to log | No detection of silent failures and no dependable open and close times | Engineer time |
| Checks as code (Soda, Great Expectations Core) | Pass rates on the rules you wrote | You build the scheduling, storage, alerting and incident record yourself | Soda Team lists at 750 dollars a month. GX Core is free |
| Incident management tool on its own | Response times once somebody raises the incident | It routes alerts. It does not detect a stale or shrunken table | Per-seat subscription |
| Enterprise observability contract (Monte Carlo, Bigeye, Sifflet, Qualytics) | Broad coverage and incident workflows | A quote-led purchase and an annual commitment before you have a baseline | AWS listings of 45,000 to 60,000 dollars a year |
| DataObservability | Five pillars per table, incidents with open and close times, 90 or 365 days of history | Postmortem documents and on-call schedules stay in the tools you already use | 59, 179 or 479 dollars a month billed yearly |
Summarized in good faith from public documentation, August 2026. Verify current behavior and terms with the vendor.
Why it fits
Teams formalizing data reliability with SLAs, on-call, and incident review.
Data SLAs you can measure
Set and monitor freshness and quality SLAs per table and report on them.
Incident tracking and MTTR
Every break becomes a tracked incident, so you can measure mean time to resolution.
Reduce data downtime
Lineage and anomaly detection shrink detection and resolution time.
Data reliability engineering starts with a number you can defend
SRE works because uptime is measured. Data teams adopting the practice usually stall at the first step, since nobody can say how many hours last quarter a critical table was stale or wrong. Incidents were found by stakeholders, logged inconsistently and closed when someone remembered. A reliability practice needs detection that does not wait for a human to notice, and an incident record with a real start and end. DataObservability checks every monitored table hourly across freshness, volume, schema, anomalies and lineage, opens one incident when a check fails and closes it when the check passes again. Those two timestamps per incident are the raw material for time to detect, time to resolve and data downtime per table, without a spreadsheet.
Setting freshness and quality targets that match how tables behave
A target of fresh by 7am sounds right until the table that loads twice a week breaches it every other day. Useful service levels come from observed behavior: when this table normally lands, how much its row count moves, how often its schema changes. Baselines learned per table give you that picture before you commit to a promise. Starter keeps 7 days of metric history, Team 90 and Scale 365, and the longer window is what lets you separate a seasonal dip from a real regression and show a trend to leadership. Start with the ten tables whose failure reaches a customer or the finance close, agree targets for those with the people who consume them, and widen from there.
An on-call rotation for data that engineers will agree to carry
Reliability programs die when the pager is noisy. Three things keep a data rotation livable. Alerts group, so a failed upstream load that breaks six downstream checks is one incident. Alerts carry context, with the upstream tables in the Slack or email message so the responder starts at the likely cause. And alerts route by severity: Slack and email on every plan, PagerDuty on Team and above for the tables that justify waking someone. Incidents that resolve close on their own, which keeps the open list truthful for the morning review. On Scale, the audit log records every change made in the workspace, which helps when an incident review asks who changed a setting and when.
What it costs to instrument a reliability program
The contracts usually quoted for this job sit between 45,000 and 60,000 dollars a year at list: Bigeye lists 45,000 on AWS Marketplace, Sifflet 48,000, Monte Carlo 50,000 and Qualytics 60,000. Those platforms cover more surface than a team starting a reliability practice tends to need in year one. Instrumenting 250 tables with hourly checks, PagerDuty routing and 90 days of history is the Team plan at 179 dollars a month billed yearly, 2,148 dollars for the year. Scale covers 1,500 tables across 5 warehouses with a year of history for 5,748 dollars. The 14-day trial needs no card and is long enough to see your real incident rate before you set a single target.
Frequently asked questions
What tools do you need for data reliability engineering?
You need three things: automated detection on the tables that matter, an incident record with reliable start and end times, and alert routing into the on-call tool you already use. DataObservability covers detection and the incident record, and routes to Slack, email and PagerDuty. Postmortems and on-call schedules can stay in your existing incident tooling.
How do you measure data downtime?
Data downtime is the total time a table was stale, incomplete or wrong. Measure it per table as the sum of incident durations, from the moment a check first failed to the moment it passed again. That requires automated checks on a fixed cadence, since incidents reported by people start late. With hourly checks and incidents that close themselves, the durations are recorded for you.
How much does data reliability tooling cost?
DataObservability costs 59 dollars a month for 50 tables, 179 for 250 and 479 for 1,500 when billed yearly. Enterprise data observability contracts list at 45,000 to 60,000 dollars a year on AWS Marketplace (Bigeye, Sifflet, Monte Carlo, Qualytics). Checks-as-code frameworks are free or low cost to license and cost engineering time to run.
Do we need an enterprise data observability contract to start a data reliability practice?
No. A practice starts with a baseline of how often critical tables break and for how long, and a self-serve tool gives you that within the first weeks. If you later need coverage of legacy or on-premise systems, or governance features, an enterprise platform may be the right second step, and by then you will have your own incident data to size it.
Can data incidents page on-call through PagerDuty?
Yes, on the Team, Scale and Enterprise plans. Each incident opens once per break, lists the upstream tables of the affected table, and closes when the check passes, so PagerDuty reflects current state. Slack and email alerts are included on every plan for tables that do not need a page.
How much history do we need to report on data SLAs?
At least a quarter if you report to leadership, since monthly and quarter-end patterns only show up over that span. Team keeps 90 days of metric history and Scale keeps 365, which supports year over year comparison. Starter keeps 7 days, enough to operate on but too short for trend reporting.
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