Module 09 - Data Observability
Your ML model went live six months ago. Predictions are solid. Stakeholders are happy. Then on a Tuesday morning, a product manager sends a Slack message: "these numbers look off." You spend three hours investigating. The root cause: a source table quietly changed its schema two weeks ago. The model kept running. The predictions kept flowing. Nobody noticed - until a human did.
Data observability is the discipline of knowing when your data is broken, why it broke, and what it affects - before your users tell you. It is the difference between discovering a data incident in 5 minutes with an automated alert and discovering it in 3 hours after a confused stakeholder escalates.
This module covers the full stack of data observability: the five foundational pillars, data lineage for tracing root causes, the data catalog for navigating large data estates, commercial and open-source observability platforms, custom monitoring architectures, and the incident management processes that turn a chaotic data outage into a structured, repeatable response.
Module Map
Lessons in This Module
| # | Lesson | Core Skill | Read Time |
|---|---|---|---|
| 01 | Five Pillars of Data Observability | Instrument freshness, volume, schema, distribution, lineage | 25 min |
| 02 | Data Lineage | Column-level lineage with OpenLineage, sqlglot, impact analysis | 25 min |
| 03 | Data Catalog and Discovery | DataHub ingestion, business glossary, active metadata | 22 min |
| 04 | Monte Carlo and Observability Platforms | Platform landscape, Soda Core, Datafold, build vs. buy | 22 min |
| 05 | Custom Data Monitoring | SQL metrics, statistical baselines, Grafana dashboards | 25 min |
| 06 | Data Incident Management | Triage playbooks, post-mortems, prevention loops | 22 min |
Prerequisites
This module assumes you have completed Modules 01–05 (Data Pipelines, Batch Processing, Stream Processing, Data Warehousing, and Data Lakehouse Architecture). You should be comfortable writing SQL, running Python scripts against a warehouse, and understanding how Airflow orchestrates pipelines.
Key Concepts You Will Master
- Five pillars of data observability (Barr Moses / Monte Carlo, 2020): the framework that defines the measurable dimensions of data health - freshness, volume, schema, distribution, and lineage
- Data downtime: the period during which data is inaccurate, missing, or otherwise unfit for use - the observability equivalent of service uptime
- Data lineage: the end-to-end record of where data came from, how it was transformed, and what it feeds into - from source system to model prediction
- Data catalog: the centralized inventory of all data assets - what exists, who owns it, what it means, and what quality it has
- Monte Carlo and the observability platform landscape: commercial, open-source, and custom approaches to instrumenting all five pillars across your entire data estate
- Active metadata: using catalog metadata to trigger automated actions - not just storing information about data, but acting on it
What You Will Be Able to Do
By the end of this module you will be able to:
- Implement automated monitoring for all five observability pillars using SQL and Python
- Trace a wrong prediction backward through a multi-layer transformation pipeline using column-level lineage
- Build and configure a data catalog that makes your data estate discoverable by any new team member in minutes
- Evaluate commercial observability platforms (Monte Carlo, Bigeye, Soda, Datafold) against a build-your-own architecture
- Design and operate a custom monitoring system for a mid-size data stack at near-zero cost
- Run a structured data incident from detection through post-mortem, and convert every incident into a monitoring improvement