Featured Project~10–13 hrs

Build a Production Data Observability System

Your revenue dashboard is wrong. Find it, trace it, prevent it — and build the system that catches it next time.

This is what senior data engineers build.

4 Parts/~10–13 hrs/3 SLO Tiers

dataguard / observability-pipeline

MEASURE

Quality Dimensions

Composite Scoring

Freshness Tracking

Volume Checks

→TEST

dbt Schema Tests

Custom SQL Tests

GE Expectations

Contract Validation

→TRACK

OpenLineage Events

Column Lineage

Impact Analysis

Data Contracts

→OPERATE

SLO Monitoring

Alert Routing

Incident Runbooks

Grafana Dashboards

fig 1 — observability pipeline

TABLES

200+

Monitored Daily

TESTS

50+

Automated Checks

SLOs

3 Tiers

Service Levels

ALERTS

P1-P4

Severity Routing

What You'll Build

4 layers that make data observable, debuggable, and reliable — portfolio-ready for senior engineering interviews.

You don't need all tools at once — focus on concepts first, tools become optional.

Validation Layer

Quality dimensions, composite scoring, dbt tests, and freshness checks that catch broken data before stakeholders do.

Lineage Layer

OpenLineage-powered graph with column-level granularity — trace any failure back to its exact source table and transformation.

SLO & Prevention Layer

Tiered SLO framework, error budgets, data contracts, and severity-based alerting that prevents incidents before they happen.

Observability Platform

Prometheus metrics + Grafana dashboards + Docker deployment — the full production stack, ready to demo in interviews.

Curriculum

4 parts, each with a clear checkpoint. Build incrementally, test as you go.

Your Data Is Wrong — Find the Problem

3 hours

The revenue dashboard is wrong and no one knows why. Build the validation layer — quality dimensions, composite scoring, dbt tests, and freshness checks — that detects broken data before stakeholders do.

✅ You detected a real data issue before stakeholders noticed

Where Did the Data Break?

3 hours

You found the issue — but you don't know where it started. Deploy OpenLineage, build column-level lineage, and run impact analysis to trace every failure back to its source.

✅ You traced the failure through the full lineage graph

Stop Data Incidents Before They Happen

4 hours

Detecting and tracing isn't enough. Define SLOs, implement error budgets, enforce data contracts, and build severity-based alerting that pages you before the CEO sees a broken number.

✅ You prevented failures with measurable reliability guarantees

Ship an Observability Platform

3 hours

You fixed the incident — but it will happen again without a system. Build the full stack: Prometheus metrics, Grafana dashboards, Docker deployment, and a portfolio-ready observability platform.

🎉 You built a production-grade data observability system

Technical Standards

Production patterns you'll implement across the observability platform.

COVERAGE

200+tables

Every table has quality checks, freshness SLAs, and lineage tracking — no blind spots in your data platform

DETECTION

<5minMTTD

Automated anomaly detection with tiered alerting catches issues before downstream consumers are affected

RELIABILITY

99.5%SLO target

Error budget-based SLOs with burn rate alerts ensure data meets business requirements consistently

Environment Setup

Spin up the observability stack and run your first data quality pipeline.

dataguard-observability

# Clone the project & launch observability stack
$ git clone https://github.com/aide-hub/dataguard-observability.git
$ cd dataguard-observability

# Start PostgreSQL + Prometheus + Grafana
$ docker-compose -f docker-compose.observability.yml up -d

# Run the data quality pipeline
$ python -m dataguard.pipeline run \
$   --checks quality,freshness,volume \
$   --slo-tier production --dashboard grafana

Tech Stack

dbtGreat ExpectationsOpenLineagePrometheusGrafanaPythonPostgreSQLDockerAirflowSoda

Prerequisites

SQL proficiency (CTEs, window functions, aggregations)
Basic Python (classes, functions, pip/virtualenv)
Understanding of data warehousing concepts (dimensions, facts)
Docker basics (containers, compose files)

Related Learning Path

Master data quality dimensions, testing frameworks, lineage tracking, and SLO management before diving into this project.

Data Observability Path

New to data observability? Read the complete guide covering the 5 pillars, SLOs, lineage, and tooling.

What is Data Observability? — Full Guide

Ready to build your observability platform?

Start with Part 1: Quality Foundation