Monitoring And Observability Basics

Why this matters

As an Analytics Engineer, your pipelines must be reliable. Monitoring and observability help you see what is happening (signals), detect problems early (alerts), and fix issues fast (runbooks). Real tasks you will handle:

• Detect when a daily dbt model is late or failed.
• Alert the right person when a DAG retries too many times.
• Track freshness, row counts, and anomaly spikes in key tables.
• Prove reliability with simple SLOs (e.g., 99% of jobs succeed within 1 hour).

Concept explained simply

Monitoring vs Observability

• Monitoring: Predefined checks and alerts on known conditions (e.g., job failed, freshness > 2 hours).
• Observability: The ability to answer unknown questions from your telemetry (metrics, logs, traces) without shipping new code.

Mental model

Think of a pipeline as a patient and your telemetry as vital signs:

• Metrics: Heart rate and temperature (counts, durations, success rate).
• Logs: Doctor’s notes about events (task started, error details).
• Traces: Full story across services (how long each step took end-to-end).

Simple loop: Signals → Detect → Triage → Recover → Learn. If any step is weak, reliability drops.

Core building blocks

• Signals: Metrics (success/failure, durations, queue time), logs (errors, warnings), traces (span timings), events (deployment, schema change).
• Health checks: Freshness, row count, null/duplicate checks on critical tables.
• Alerting: Thresholds, anomalies, severity levels, routing (who gets notified), and deduplication.
• SLOs and error budgets: Define a reliability target (e.g., 99% on-time jobs). If you exceed the error budget, prioritize reliability work.
• Dashboards: At-a-glance health for pipelines and data quality.
• Runbooks: Step-by-step guides to investigate and fix common failures.
• On-call basics: Clear ownership, escalation, and quiet hours.

Worked examples

Example 1: Failure rate alert for a daily pipeline

Goal: Alert if the pipeline failure rate exceeds 5% in the last 24 hours.

Example 2: Freshness health check for a model

Goal: Alert if fact_orders is more than 90 minutes stale.

-- Pseudo-SQL (adapt to your warehouse)
SELECT CASE WHEN TIMESTAMPDIFF(MINUTE, MAX(ingestion_ts), CURRENT_TIMESTAMP) > 90
THEN 'STALE' ELSE 'FRESH' END AS freshness_state
FROM analytics.fact_orders;

Example 3: Duration anomaly for a long-running task

Goal: Detect when task duration is unusually high.

Example 4: Smart routing to reduce alert fatigue

Goal: Send the right alerts to the right people.

Hands-on exercises

These mirror the exercises below. Try them here, then submit in your environment.

1. Exercise 1: Write a short alerting specification for a pipeline that must finish by 06:00 each day. Include: metric, detection logic, severity, routing, and the first 3 runbook steps.
2. Exercise 2: Design a minimal observability plan for one DAG with 3 tasks (extract, transform, load). Specify: metrics to collect, 2 health checks, 2 alerts, and one simple SLO.

Exercise checklist

• Did you define a concrete metric name and window?
• Is the threshold realistic and not too sensitive?
• Does each alert specify severity, owner, and escalation?
• Is there at least one data quality check (freshness or row count)?
• Is the SLO measurable and easy to report weekly?

Common mistakes and self-check

• Only monitoring failures, not lateness. Self-check: Do you alert on both failure and excessive duration/freshness?
• Too many alerts. Self-check: Can you reduce noise via routing, deduplication, and severity?
• No runbooks. Self-check: Can a new teammate resolve a common failure using your steps?
• Ignoring upstream changes. Self-check: Do you log schema changes and deployments as events?
• Dashboards without ownership. Self-check: Each dashboard has an owner and a review cadence.

Practical projects

• Project 1: Instrument a single daily DAG with success rate, duration, and freshness metrics. Build a 1-page dashboard and one high-severity alert.
• Project 2: Add data quality checks (row count, null rate) to two critical tables and connect alerts to on-call rotation.
• Project 3: Define an SLO (e.g., 99% on-time) for your top pipeline. Track error budget for a month and create a post-incident review template.

Who this is for

• Analytics Engineers and BI Developers who own or contribute to data pipelines.
• Anyone preparing to be on-call for analytics workflows.

Prerequisites

• Basic pipeline orchestration knowledge (DAGs, tasks, retries).
• SQL for writing freshness and quality checks.
• Familiarity with logs and metrics concepts.

Learning path

1. Instrument signals: success/failure counts, durations, freshness.
2. Add 2–3 high-value alerts with clear routing and runbooks.
3. Build a simple operational dashboard.
4. Define one SLO and review weekly.
5. Practice an incident drill using your runbook.

Next steps

• Pick one pipeline and implement at least one alert and one data quality check today.
• Create or update a runbook for your most common failure.
• Set a simple SLO and start tracking it.

Mini challenge

Choose a pipeline that runs daily. In one page, define: 3 metrics, 1 freshness check, 1 anomaly check, 2 alerts with routing, a 5-step runbook, and a 99% on-time SLO. Share with your team for feedback.

Quick Test

Everyone can take the test for free. If you are logged in, your progress will be saved automatically.