Data Engineering Guide

Data Observability Guide

Your CEO opens a dashboard and the numbers are wrong. Again. Data observability prevents this by monitoring freshness, volume, schema, distribution, and lineage across your entire data stack.

Map Your Observability StackData Quality Guide
15 min readFor Data Engineers & Platform Teams

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The Five Pillars of Data Observability

Freshness

Is data arriving on time? Detect late or stale tables before dashboards show yesterday's numbers.

Volume

Are the expected number of rows arriving? Catch drops, spikes, and missing partitions automatically.

Schema

Have columns, types, or constraints changed unexpectedly? Detect breaking changes before downstream failures.

Distribution

Are values within expected ranges? Catch NULL spikes, outliers, and categorical shifts that pass schema checks.

Lineage

Where did the data come from and what depends on it? Trace issues upstream and assess blast radius downstream.

Observability vs Quality: You Need Both

AspectData Quality TestsData Observability
ApproachPredefined rulesML-based anomaly detection
CatchesKnown issuesUnknown issues
Setup EffortWrite tests per modelConnect and auto-learn
Toolsdbt tests, Great Expectations, SodaMonte Carlo, Bigeye, Metaplane

Implementation Roadmap

Phase 1: Freshness & Volume (Week 1-2)

  • Monitor arrival time of top 10 critical tables
  • Set row volume alerts (% change threshold)
  • Alert to Slack/PagerDuty on anomalies

Phase 2: Schema & Distribution (Week 3-4)

  • Enable schema change detection
  • Monitor NULL rates and distribution shifts
  • Connect data catalog for metadata context

Phase 3: Lineage & Incident Response (Month 2)

  • Build end-to-end lineage from sources to dashboards
  • Create incident response runbooks
  • Track MTTD (mean time to detect) and MTTR (mean time to resolve)

Tool Comparison

ToolBest ForPricing
Monte CarloEnterprise, automated detection$$$
BigeyeAutomated metrics, multi-warehouse$$
SodaOpen-source, flexible checksFree (OSS) / Cloud $$
Elementarydbt-native observabilityFree (OSS) / Cloud $
Great ExpectationsOpen-source quality testingFree (OSS)

FAQ

What is data observability?
The ability to understand, diagnose, and resolve data issues using the five pillars: freshness, volume, schema, distribution, and lineage.
What are the five pillars of data observability?
1) Freshness — is data on time? 2) Volume — expected row counts? 3) Schema — any breaking changes? 4) Distribution — values in range? 5) Lineage — source and impact mapping.
Data observability vs data quality?
Quality uses predefined rules for known issues. Observability uses ML to detect unknown anomalies. You need both for comprehensive coverage.

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