Data SLAs / SLOs
Data Service Level Agreements and Objectives are commitments to data availability, quality, and freshness, specifying targets, monitoring mechanisms, and remediation when violated.
A Data SLA (Service Level Agreement) is a formal contract specifying guarantees: "This data will be 99.9% available, updated within 4 hours, and maintain 99% data quality." An SLO (Service Level Objective) is an internal target that's typically stricter than the SLA to provide buffer. SLAs include specifics: which dimensions count toward the metric (exclude planned maintenance?), how violations are measured, and what happens when targets are missed (credits, escalation, remediation commitment).
Data SLAs emerged because analytics became business-critical and stakeholders needed guarantees. Executives running daily dashboards need to know data is current and reliable. Data SLAs make this expectation explicit: when data is stale or quality drops, it's a violation requiring remediation, not an inconvenience. SLAs also incentivize investment: when metrics are tracked and failures are escalated, organizations prioritize improvements.
Data SLAs typically measure three dimensions: availability (is the data accessible?), freshness (how current is it?), and quality (how many rows/fields are accurate?). Different data assets might have different SLAs: operational data might require 99.99% freshness (within minutes); historical data might accept 24-hour freshness. SLAs are measured automatically and tracked in dashboards. Violations trigger incident workflows: investigation, root cause analysis, and remediation planning.
Key Characteristics
- ▶Formal guarantees on availability, freshness, and quality
- ▶Includes measurement methodology and violation definitions
- ▶Monitored automatically and tracked in dashboards
- ▶Violations trigger investigation and remediation
- ▶Typically stricter internal SLOs than external SLAs
- ▶Published and communicated to stakeholders
Why It Matters
- ▶Reliability: Stakeholders know what to expect
- ▶Accountability: Violations are objective and drive remediation
- ▶Investment: Metrics visibility justifies platform investments
- ▶Trust: Consistent SLA attainment builds confidence
- ▶Operations: SLA dashboards guide resource allocation
Example
Data SLA for revenue metrics: Availability >= 99.5%, Freshness <= 4 hours (max lag from source), Quality >= 99% (accurate values). SLO: Availability >= 99.9%, Freshness <= 2 hours. If actual freshness exceeds 4 hours, incident is opened and investigation begins.
Coginiti Perspective
Coginiti Actions support data SLA enforcement through cron scheduling with timezone awareness and misfire policies (RUN_IMMEDIATELY or SKIP). These policies define how the system handles missed schedules, directly affecting freshness SLAs. CoginitiScript #+test blocks can assert SLO conditions (row counts, value ranges, freshness thresholds) within pipeline execution, and the onFailure option controls whether a violated SLO stops the pipeline (test.Stop) or logs the violation and continues (test.Continue).
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