full-stack-observability-hub

What You'll Build

A unified observability platform that monitors every aspect of your data infrastructure - from pipeline failures to data quality issues. When something breaks, your system will detect it within minutes and automatically create tickets with root cause analysis.

Why This Project Matters

• Prevent Disasters: Bad data costs enterprises $12.9M annually on average
• Build Trust: Data teams with good observability have 3x higher stakeholder satisfaction
• Modern Skills: Observability is the #1 requested skill in senior data engineering roles

Tech Stack Explained for Beginners

Technology	What it is	Why you'll use it
OpenLineage	Standard for tracking data lineage	Captures how data flows through your pipelines
Monte Carlo	Data observability platform	Monitors data quality metrics automatically
DataHub	Open-source metadata catalog	Visualizes your entire data ecosystem
Grafana	Metrics visualization platform	Creates beautiful monitoring dashboards
Slack/Jira	Communication and ticketing	Automates incident response workflow

Step-by-Step Build Plan

1. Week 1: Deploy DataHub and configure data source connections
2. Week 2: Implement OpenLineage in sample pipelines
3. Week 3: Set up Monte Carlo monitors for key datasets
4. Week 4: Build Grafana dashboards for SLO tracking
5. Week 5: Create incident response automation
6. Week 6: Implement root cause analysis features

Detailed Requirements

Functional Requirements

• Lineage Tracking:
  • Column-level lineage for 100+ tables
  • Track transformations across tools (Airflow, dbt, Spark)
  • Visual lineage graphs with impact analysis
  • Data ownership and contact mapping
• Quality Monitoring:
  • Freshness checks (data not older than X hours)
  • Volume anomalies (row count changes >20%)
  • Schema change detection
  • Distribution shift alerts
  • Custom business rule validation
• Incident Management:
  • Auto-create Jira tickets for incidents
  • Slack alerts with severity levels
  • Suggested root causes based on lineage
  • Incident timeline reconstruction
  • Post-mortem report generation

Technical Requirements

• Detection Speed:
  • Mean time to detection (MTTD) < 10 minutes
  • Process lineage events in < 1 second
  • Support 1000+ tables/pipelines
  • Handle 100K+ quality checks/day
• Integration Coverage:
  • Connect to 5+ data sources
  • Support major orchestrators (Airflow, Prefect)
  • Work with cloud warehouses (Snowflake, BigQuery)
  • API for custom integrations
• Reliability:
  • 99.9% uptime for monitoring
  • No single point of failure
  • Automated backup of metadata
  • Disaster recovery < 1 hour

Prerequisites & Learning Path

• Required: Python, SQL, and basic understanding of data pipelines
• Helpful: Experience with monitoring tools
• You'll Learn: Data observability, metadata management, SRE practices

Success Metrics

• Detect 95% of data incidents within 10 minutes
• Reduce mean time to resolution (MTTR) by 50%
• Achieve 100% lineage coverage for critical data
• Generate 10+ automated root cause analyses
• Maintain false positive rate < 5%