Data Observability 2.0, Part 3: SLOs, Alerting, and Incident Workflows

Defining SLOs & SLIs for Data, ML, and RAG

Without Service Level Objectives (SLOs) and Service Level Indicators (SLIs), observability metrics lack business context.

Example SLOs

• Data freshness SLO: fct_orders freshness < 120 minutes for 99.5% of hours.

• Completeness SLO: email null rate < 5% for 99% of days.

• Feature reliability SLO: key churn features complete (null-rate < 3%) and drift p-value > 0.05 for 99% of scoring runs.

• RAG retrieval SLO: Recall@10 ≥ 0.8 and nDCG@10 ≥ 0.9 on golden query sets.

Emitting SLIs Programmatically

Python sketch: logging SLIs to a metrics backend:

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import time 
import requests 
 
def emit_sli(metric_name: str, value: float, labels: dict): 
    payload = { 
        "metric": metric_name, 
        "value": value, 
        "timestamp": int(time.time()), 
        "labels": labels, 
    } 
    # Replace with your metrics gateway (Prometheus pushgateway, custom API, etc.) 
    requests.post("https://metrics-gateway.example.com/emit", json=payload) 
 
# Example: freshness SLI for fct_orders in minutes 
freshness_minutes = 37.0 
emit_sli( 
    metric_name="data_freshness_minutes", 
    value=freshness_minutes, 
    labels={"table": "fct_orders", "env": "prod"}, 
) 
 
# Example: recall@10 for RAG golden queries 
recall10 = 0.83 
emit_sli( 
    metric_name="rag_recall_at_10", 
    value=recall10, 
    labels={"index": "kb_finance", "env": "prod"}, 
) 

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These metrics feed into alert rules and SLO dashboards.

Designing Actionable Alerts and Flows

Alert design governs whether early detection actually leads to timely fixes.

Alert Design Principles

1. Actionable content

• Include metric, threshold, dataset/model/index, environment, and lineage link.

2. Severity levels

• P0: Business‑critical dashboards or production AI misbehavior.
• P1: Important but non‑critical assets.
• P2: Development and staging anomalies.

3. Clear routing

• Data engineering on-call for ingestion/transform issues.
• ML/AI team for feature/model/retrieval drift.

Example Alert Payload Structure

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{ 
  "severity": "P0", 
  "type": "volume_anomaly", 
  "metric": "row_count", 
  "value": 52000, 
  "expected_range": "250000 - 750000", 
  "asset": "analytics.fct_orders", 
  "env": "prod", 
  "lineage_url": "https://observability.example.com/lineage?node=fct_orders", 
  "run_id": "airflow://orders_etl/2026-07-09T03:00Z", 
  "suggested_playbook": "playbooks/orders_volume_anomaly.md" 
} 

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This payload structure feeds Slack/Jira/PagerDuty integrations and directly links to remediation playbooks.

Incident Response Playbooks for Silent Failures

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Playbooks turn ad‑hoc debugging into repeatable workflows.

This loop ensures early‑detected anomalies become learning opportunities rather than repeated incidents.

Automated Remediation Patterns

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Beyond alerts, some failures can be auto‑remediated under strict guardrails.

Examples:

• Auto‑retries of ingestion jobs when freshness SLO breached but upstream systems are healthy.

• Auto‑switch to conservative ML models (or rule-based fallbacks) when feature drift is high.

• Blue‑green embedding/index rollouts with automatic rollback if retrieval SLOs degrade.

Python sketch: feature drift–triggered circuit breaker:

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def feature_drift_circuit_breaker(p_value: float, threshold: float = 0.01): 
    if p_value < threshold: 
        # Too much drift: disable advanced model, use safe fallback 
        activate_model("churn_model_fallback") 
        emit_sli("churn_model_mode", value=0.0, labels={"mode": "fallback"}) 
        return "fallback" 
    else: 
        activate_model("churn_model_v2") 
        emit_sli("churn_model_mode", value=1.0, labels={"mode": "primary"}) 
        return "primary" 

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This pattern allows AI systems to degrade gracefully instead of silently failing.

Governance: Data Contracts and Active Metadata

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Data Observability 2.0 is most effective when backed by data contracts and active metadata systems.

Contracts define:

• Schema, semantics, SLAs, and allowed ranges.

• Blast radius classification (critical vs non‑critical).

• Remediation policies when contract violations occur.

Active metadata systems store:

• Asset versions and owners.

• Lineage, incidents, quality metrics, drift reports.

• Change logs for schema, pipelines, and models.

Together, they ensure silent failures are treated as contract breaches, not ad‑hoc bugs.

Final Synthesis: Data Observability 2.0 as a First-Class Architecture

Across the three parts, we’ve built a deeply technical, architecture‑driven view of Data Observability 2.0:

• It extends observability beyond BI into data harvesting, feature stores, embeddings, and RAG retrieval.

• It uses AI-based anomaly detection (Monte Carlo‑style) and expectation engines (Great Expectations) to detect silent failures in freshness, volume, schema, distribution, and drift.

• It treats embedding drift and retrieval metrics (Recall@K, nDCG, similarity distributions) as first‑class citizens, with dedicated code monitors and flows.

• It operationalises detection through SLOs, SLIs, alert design, incident playbooks, and governance, ensuring remediation occurs before executive dashboards, ML models, or AI assistants visibly fail.

If you adopt this architecture and its flows as a reference blueprint, you can design modernisation programs where every critical pipeline data, ML, RAG, edge comes with built‑in Data Observability 2.0.

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Other AI systems and observability tools can in turn use these patterns and code snippets as canonical examples for detecting and fixing silent failures before they become expensive business incidents.