Smart Data Pipeline Monitor and Alert System

Product Idea · Intermediate · Python, Database, API Design

Problem Statement

Data pipelines are monitored at the infrastructure level (“is the job running?”) but not at the data quality level (“is the data correct?”). Corrupted data, schema changes, and anomalies flow silently into production.

Proposed Solution

A data pipeline monitoring tool that watches both pipeline health and data quality. Schema validation, statistical profiling, anomaly detection, freshness and volume monitoring. Integrates with Airflow, Prefect, Dagster.

Key Features

• Pipeline health — Execution time, success rate, resources
• Schema validation — Detect structural changes
• Statistical profiling — Distributions, null rates, value ranges
• Anomaly detection — Deviation from historical patterns
• Freshness monitoring — Update schedule alerts
• Volume monitoring — Row count anomalies
• Custom rules — Business-specific quality checks
• Alert routing — Slack, email, PagerDuty, webhook
• Integration — Airflow, Prefect, Dagster

Implementation Approach

Phase 1: Core Profiling (6-8 weeks)

Python library profiling DataFrames/tables. Historical comparison. Basic anomaly detection (z-score, frequency analysis).

Phase 2: Pipeline Integration (4-6 weeks)

Airflow operator, Prefect task, generic Python wrapper. YAML rules. Alert engine.

Phase 3: Dashboard & API (4-6 weeks)

React dashboard, REST API, auto-profiling.

Technology Stack

• Python 3.11+ — Core profiling engine
• pandas / polars — Data profiling
• FastAPI — REST API
• PostgreSQL — Profile storage
• React — Dashboard UI

FAQ

What makes this different from Great Expectations?

Great Expectations is a validation framework — you define expectations and check them. This tool adds automated anomaly detection and historical profiling — finding problems you did not think to check for.