Proof of Concept: Building a Data Quality Monitor

Sep 16

Data quality issues are the silent killers of analytics projects. You've built beautiful dashboards, trained sophisticated models, and automated your reporting - but if your underlying data is inconsistent, incomplete, or drifting over time, all that effort crumbles.

As a proof of concept, I’ve put together a Data Quality Monitor that demonstrates how modern teams can catch problems before they cascade through their systems. While still in early stages, this POC showcases the core patterns and capabilities needed for production-grade data quality monitoring.

https://github.com/steveoliai/data-quality-monitor

The Problem: Data Goes Bad in Predictable Ways

After years of wrestling with data quality issues, I’ve seen that many problems fall into predictable patterns:

Schema drift: New columns appear, data types change unexpectedly
Value corruption: NULLs creep in where they shouldn't, outliers multiply
Business rule violations: Invalid status codes, out-of-range values
Subtle degradation: Gradually increasing NULL rates, shifting distributions

The challenge isn't just detecting these issues - it's catching them early, understanding their impact, and getting actionable alerts when things go wrong.

POC Solution: Demonstrating Modern Data Quality Patterns

This proof of concept showcases what's possible with a thoughtfully designed data quality framework. While currently supporting a focused set of data sources (CSV files, PostgreSQL, and BigQuery), the architecture demonstrates patterns that could scale to enterprise needs:

Configuration-driven validation via YAML
Automated drift detection against historical baselines
Multi-format reporting with visual schemas
Smart alerting integration (Slack proof-of-concept)
Performance optimizations for handling larger datasets

Note: This is a proof of concept designed to validate approaches and patterns. For production use, you'd want to extend the data source connectors, add authentication layers, and implement more robust error handling.

Core POC Features Demonstrated

1. Flexible Data Source Integration (Limited Scope)

The POC currently demonstrates connectivity patterns with three common data sources:

source:
  type: postgres
  conn: "postgresql://user:pass@host:5432/db"
  sql: "SELECT * FROM sales_data WHERE date >= '2024-01-01'"

While focused on CSV, PostgreSQL, and BigQuery for this proof of concept, the abstraction layer shows how you'd extend support to other databases, APIs, or cloud storage systems in a production version.

2. Proof-of-Concept Data Quality Checks

The POC demonstrates a comprehensive set of validation patterns that could be extended for production use:

checks:
  - type: not_null
    column: customer_id
    max_null_pct: 0.0
    
  - type: range
    column: order_amount
    min: 0
    max: 100000
    
  - type: in_set
    column: status
    allowed: ["pending", "completed", "cancelled"]
    
  - type: unique
    column: transaction_id
    
  - type: duplicates
    subset: ["customer_id", "order_date"]
    max_dup_pct: 1.0

Each check type is optimized for performance and provides detailed failure information, including sample rows that violate the rules.

3. Intelligent Schema Visualization

The tool automatically generates Mermaid ERD diagrams that visualize your data structure:

erDiagram
  sales_data {
    INT customer_id
    TIMESTAMP order_date
    FLOAT order_amount
    TEXT status
    TEXT product_category
  }

This makes it easy to understand your data schema at a glance and share it with stakeholders.

4. Experimental Drift Detection

One of the most interesting aspects of this POC is the automatic drift detection capability. The system demonstrates how to compare runs against historical baselines:

Row count changes beyond acceptable thresholds
NULL rate increases in critical columns
Cardinality shifts (sudden changes in distinct values)
Check status transitions (previously passing checks now failing)

drift:
  enabled: true
  thresholds:
    row_count_pct: 10.0      # Alert if row count changes >10%
    null_pct_abs: 2.0        # Alert if NULL rate increases >2%
    distinct_pct: 20.0       # Alert if distinct values change >20%

5. Rich, Multi-Format Reporting

Reports are generated in multiple formats for different audiences:

JSON: Machine-readable for automation and APIs
Markdown: Version-control friendly for documentation
HTML: Rich interactive reports with embedded visualizations

Each report includes:

Dataset profiling with statistical summaries
Column-by-column analysis with top values
Failed check details with sample violating rows
Historical drift analysis
Visual schema diagrams

6. Basic Alerting Integration

The POC includes a simple Slack integration to demonstrate how alerts could work:

notifications:
  enabled: true
  type: slack
  slack:
    webhook_url: "https://hooks.slack.com/..."
    mention: "@data-team"

For production deployment, you'd want to add authentication, retry logic, rate limiting, and support for multiple notification channels.

Performance Optimizations Demonstrated

Even as a POC, several performance patterns that would be essential at scale have been implemented:

Caching Strategy

The optimized version implements intelligent caching for expensive operations:

Numeric conversions are cached per column to avoid repeated pd.to_numeric() calls
DateTime parsing is cached similarly
Lookup structures are pre-built for drift comparison

Memory Efficiency

For large datasets, the tool optimizes memory usage:

Sample collection is limited to essential columns only
Top N sampling prevents memory issues with wide tables
Batch file operations reduce I/O overhead

Early Returns

The code implements early returns for common error conditions:

Missing columns are caught immediately
Invalid regex patterns fail fast
Empty datasets are handled gracefully

Proof of Concept Results

Here's what this POC demonstrates for teams evaluating data quality solutions:

Demonstrated Value: The POC shows how structured, configuration-driven data quality monitoring can catch issues that manual spot-checks miss.

Early testing of this POC helps teams:

Validate the approach before investing in a full solution
Understand quality patterns specific to their data
Prototype alerting workflows with their existing tools
Test performance characteristics on their actual datasets

Getting Started with the POC

Install dependencies: pandas, pyyaml, plus connectors for supported data sources
Create a config file defining your data source and quality rules
Run the monitor: python dq_monitor.py --config your_config.yaml
Test with sample data to understand the reporting format
Experiment with Slack notifications using a test webhook

This POC is designed to help you evaluate the approach and understand what a production system might look like. Start with basic checks on a non-critical dataset to get familiar with the patterns.

The Bottom Line

This proof of concept demonstrates that sophisticated data quality monitoring doesn't have to be complex to get started. Even with limited data source support, the core patterns - configuration-driven checks, drift detection, and automated reporting - provide immediate value.

The POC validates the approach and gives teams a foundation to build upon. Whether you extend this codebase or use it to evaluate commercial solutions, you'll have hands-on experience with the key capabilities that matter for data quality at scale.

Ready to experiment with intelligent data quality monitoring? Clone the POC, point it at a test dataset, and see what patterns emerge in your data. The insights you gain from this experiment will inform your strategy for production-grade data quality management.