Pipeline Incident Response
Walk through a Black Friday pipeline failure: triage, blame-free postmortem template, severity rubric, and the "five whys" framework that surfaces a real root cause.
Data Observability
Pipeline monitoring, data quality testing, SLAs, and incident response for data teams.
Bad data is more expensive than downtime — it ships wrong dashboards to executives and wrong predictions to users. Observability catches problems at the pipeline, not at the all-hands.
What you’ll be able to do
- Build data quality testing with dbt tests and Great Expectations
- Implement observability platforms with lineage tracking
- Define SLAs, SLOs, and data contracts for pipelines
- Monitor AI pipelines and respond to production incidents
Curriculum
Phase 1: Quality Foundations
Incident response, data quality, and dbt testing
Data Quality Foundations
The 6 DAMA quality dimensions (accuracy, completeness, consistency, timeliness, uniqueness, validity), business-cost calculation, and where each dimension lives in the pipeline.
dbt Testing Patterns
Generic vs singular tests, schema vs data tests, custom tests via macros, severity levels (warn/error), and which tests gate CI vs run on schedule.
Phase 2: Observability Platforms
Great Expectations, platforms, and lineage
Great Expectations
Expectation suites, checkpoints, runtime data context, and how GE complements dbt for Python pipelines that don't live inside the warehouse.
Observability Platforms
A 7-step build-vs-buy deep dive — Monte Carlo, Soda Core, Elementary, Datafold, Bigeye, and Anomalo positioned head-to-head against custom checks.
Data Lineage
Read column-level lineage graphs, OpenLineage events, dbt lineage from manifest.json, and how lineage drives impact-of-change analysis before a deploy.
Phase 3: Production Operations
SLAs, AI monitoring, and production ops
SLAs, SLOs & Data Contracts
Data SLAs producers actually own, error-budget math, the contract template, and the sliding-window vs fixed-window SLO trade-off.
AI Pipeline Monitoring
Input-side drift (data, concept), output-side drift (prediction, label), embedding drift in RAG, and the trigger that fires retraining vs alerting.
Production Operations
Alert-fatigue diagnosis (200 weekly alerts → 0), runbook structure, on-call rotation design, and the metrics an observability on-call actually watches.
Observability Capstone
Inherit a broken production observability system you've never seen, diagnose root cause from logs + metrics + lineage, and write the postmortem + remediation plan.
What you’ll build
- Data quality test suite combining dbt tests + Great Expectations across warehouse + Python layers
- Lineage-aware impact-of-change report that gates deploys before they break downstream consumers
- Production runbook with severity rubric, on-call rotation, and the alert-budget that prevents fatigue
- AI pipeline drift dashboard tracking input/output/embedding drift with retrain triggers
This pipeline ran for 90 days… before anyone noticed it was wrong.
Without observability, you risk:
- Stakeholders losing trust after a quarter-end report runs on stale data
- Schema changes upstream silently corrupting features for an ML model
- 200 alerts/week that on-call ignores, hiding the one that actually mattered
- Postmortems that find the bug but never the why, so the same incident repeats
What is Data Observability?
Data observability is the practice of monitoring, testing, and ensuring the health of data pipelines and datasets in production. It combines data quality testing, lineage tracking, SLAs, and incident response to prevent bad data from reaching downstream consumers. Used by teams at Airbnb, Uber, and LinkedIn to maintain trust in their data.
Why this matters in production
Bad data costs more than downtime — it leads to wrong business decisions. At Airbnb, data quality issues in pricing pipelines directly impacted revenue. Production observability means knowing when data is late, wrong, or missing before stakeholders open a dashboard and see broken numbers.
Common use cases
- Building automated data quality tests with dbt tests and Great Expectations
- Implementing data lineage tracking to understand upstream/downstream dependencies
- Defining SLAs and SLOs for pipeline freshness and data quality metrics
- Setting up alerting for schema changes, volume anomalies, and freshness issues
- Responding to data incidents with structured runbooks and root cause analysis
- Monitoring AI pipeline data drift and model input quality
DATA vs alternatives
DATA vs Software Observability
Data observability monitors data quality, freshness, and lineage. Software observability monitors application health with logs, metrics, and traces. Data observability extends software monitoring to the data layer.
DATA vs Data Quality Tools
Observability is broader than quality testing alone. It includes lineage, freshness monitoring, volume tracking, and incident response. Data quality tools like Great Expectations are one component of full observability.
DATA vs Monte Carlo
Monte Carlo is a managed observability platform. Understanding observability concepts lets you evaluate and use tools like Monte Carlo effectively, or build custom observability with open-source tools.
Related skills
- dbt & Analytics Engineering — dbt tests are a core observability tool, building on skills from
- Data Governance — Observability enforces the quality standards defined in
- Apache Airflow — Pipeline monitoring integrates with orchestration in
Why this skill matters
Observability is the senior data engineer's superpower — the ability to ship pipelines that fail loudly, recover quickly, and earn the trust of every team that consumes the output. It's the line between "writes pipelines" and "owns the platform."
Common questions about DATA
What is data observability?
Data observability monitors the health of data pipelines and datasets. It tracks data quality, freshness, volume, schema, and lineage to catch issues before they impact downstream consumers.
Why is data observability important?
Without observability, data issues are discovered by stakeholders seeing wrong dashboards. Observability catches problems at the pipeline level, reducing incident response time and maintaining data trust.
How long does it take to implement data observability?
Basic quality tests take 1-2 weeks. A full observability platform with lineage, alerting, and SLAs typically takes 2-3 months to implement and tune for your specific pipelines.
What tools are used for data observability?
Common tools include dbt tests, Great Expectations, Monte Carlo, Datafold, and custom solutions. Most teams combine dbt tests for quality with a platform for lineage and anomaly detection.
Do data engineers need observability skills?
Yes. Observability is expected for production data engineering roles. Companies want engineers who can build and maintain reliable pipelines, not just ones that run.
Data observability vs data governance — what's the difference?
Observability monitors what's happening to your data right now (freshness, quality, schema, lineage). Governance defines who owns it and what the rules are (access, contracts, compliance). Most production teams need both — observability is the runtime signal, governance is the policy layer.
What's a good first project for learning data observability?
Start with a dbt test suite on a single critical model — schema tests + a few data tests gating CI. Layer in a Great Expectations checkpoint for a Python ingestion job, then add a freshness SLA. Once that's solid, evaluate whether you need a managed platform like Monte Carlo.
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