Data SLO Calculator

Apply SRE error budget principles to your data pipelines. Track freshness, correctness, and coverage.

Configuration

Data Source Type

Scheduled batch jobs (dbt, Airflow, Fivetran)

SLO Dimension

Records pass validation and quality checks

Correction Window

Total Events

Target SLO (%)

Current State

Failures so far

Hours Elapsed

Load Example Scenario

Error Budget

5,000

allowed bad records

Burn Rate

0.9x

vs expected pace

Remaining Budget

2,250

45.0% left

Healthy: 2,250 errors remaining

Budget consumption is on track. Continue monitoring.

Budget Burn Visualization

14h elapsed

Actual error consumption vs. ideal linear burn. Dashed orange line shows the projected path.

Export Monitor Configuration

Copy-paste configurations for your observability platform

{
  "name": "[Data SLO] my-data-pipeline - correctness budget burn",
  "type": "metric alert",
  "query": "sum(last_1d):sum:data.correctness.failures{service:my-data-pipeline} > 4000",
  "message": "{{#is_alert}}Data correctness SLO at risk! {{value}} failures detected (budget: 5000){{/is_alert}}",
  "tags": [
    "team:data",
    "slo:99.5",
    "dimension:correctness"
  ],
  "options": {
    "thresholds": {
      "critical": 4500,
      "warning": 3750
    }
  }
}

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Integration Note

Requires standard Datadog Agent with metric submission enabled.