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Great Expectations Data Quality Implementation Summary

Overview

This document summarizes the implementation of Great Expectations for data quality monitoring in the Fawkes platform (Issue #47).

Implementation Completed

1. Great Expectations Project Structure ✅

Created a complete GX project in services/data-quality/:

  • Configuration: gx/great_expectations.yml, gx/datasources.yml
  • Expectation Suites: 4 database-specific validation suites
  • Checkpoints: 4 checkpoints for automated validation
  • Scripts: Alert handler and checkpoint runner
  • Tests: 14 unit tests (all passing)
  • Documentation: Comprehensive README and guides

2. Database Datasources ✅

Configured connections to:

  • Backstage DB: Developer portal catalog validation
  • Harbor DB: Container registry metadata validation
  • DataHub DB: Data catalog metadata validation
  • DORA Metrics DB: Deployment metrics validation

3. Expectation Suites ✅

Backstage DB (backstage_db_suite.json)

  • Row count validation
  • Schema validation (required columns)
  • Primary key (entity_id) not null and unique
  • Entity references validation

Harbor DB (harbor_db_suite.json)

  • Artifact row counts
  • Required columns validation
  • Primary key uniqueness
  • Size validation (0-10GB range)
  • Referential integrity

DataHub DB (datahub_db_suite.json)

  • Metadata row counts
  • URN format validation
  • Required columns (urn, aspect, version)
  • DataHub standard compliance

DORA Metrics (dora_metrics_suite.json)

  • Metrics data exists
  • Timestamp validation
  • Data freshness checks
  • Time-series completeness

4. Checkpoints ✅

Created 4 checkpoints:

  • backstage_db_checkpoint.yml - Backstage-specific
  • harbor_db_checkpoint.yml - Harbor-specific
  • datahub_db_checkpoint.yml - DataHub-specific
  • all_databases_checkpoint.yml - All databases combined

5. Kubernetes Deployment ✅

Resources Created

  • data-quality-application.yaml - ArgoCD Application
  • configmap.yaml - Configuration settings
  • secret.yaml - Database credentials (dev/local)
  • serviceaccount.yaml - RBAC
  • service.yaml - Service endpoint
  • cronjob.yaml - Scheduled validation (every 6 hours)
  • kustomization.yaml - Kustomize config with ConfigMap generators

Deployment Strategy

  • Uses ArgoCD for GitOps deployment
  • CronJob runs every 6 hours automatically
  • ConfigMaps generated from source files
  • Secrets managed (with External Secrets Operator support)

6. Alerting and Monitoring ✅

Mattermost Integration

  • Alert on validation failures
  • Daily summary reports
  • Configurable alert thresholds

Grafana Dashboard

  • Validation success rate
  • Failed validations tracking
  • Validation results over time
  • Database-specific status
  • Recent failure tracking
  • Data freshness heatmap

Alert Configuration (alerting.yaml)

  • Multiple alert rules (failure, stale data, high failure rate)
  • Daily summary configuration
  • Deduplication and escalation support
  • Multi-channel support (Mattermost, Email, Slack, PagerDuty)

7. Validation and Testing ✅

Unit Tests

  • 14 tests created in tests/test_config.py
  • All tests passing ✅
  • Coverage:
  • Configuration file validation
  • Datasources validation
  • Expectation suites validation
  • Checkpoints validation
  • Alert configuration validation
  • Requirements validation

Validation Script

  • Created scripts/validate-at-e2-004.sh
  • Added Makefile target make validate-at-e2-004
  • Comprehensive 7-phase validation:
  • Prerequisites
  • Configuration
  • Data sources
  • Expectation suites
  • Validation automation
  • Checkpoints
  • ArgoCD application

8. Documentation ✅

Created comprehensive documentation:

  • README.md: Full service documentation
  • Architecture overview
  • Directory structure
  • Expectation suites description
  • Deployment instructions
  • Usage examples
  • Troubleshooting guide
  • alerting.yaml: Alert configuration with examples
  • Grafana dashboard JSON: Pre-configured monitoring dashboard

File Structure

services/data-quality/
├── .gitignore
├── README.md (7.7KB)
├── requirements.txt
├── alerting.yaml (4.6KB)
├── grafana-dashboard.json (5.8KB)
├── gx/
│   ├── great_expectations.yml
│   ├── datasources.yml
│   └── uncommitted/ (gitignored)
├── expectations/
│   ├── backstage_db_suite.json
│   ├── harbor_db_suite.json
│   ├── datahub_db_suite.json
│   └── dora_metrics_suite.json
├── checkpoints/
│   ├── backstage_db_checkpoint.yml
│   ├── harbor_db_checkpoint.yml
│   ├── datahub_db_checkpoint.yml
│   └── all_databases_checkpoint.yml
├── scripts/
│   ├── alert_handler.py
│   └── run_checkpoint.py
└── tests/
    └── test_config.py

platform/apps/data-quality/
├── configmap.yaml
├── secret.yaml
├── serviceaccount.yaml
├── service.yaml
├── cronjob.yaml
└── kustomization.yaml

platform/apps/
└── data-quality-application.yaml

scripts/
└── validate-at-e2-004.sh

Technology Stack

  • Great Expectations: 0.18.12
  • SQLAlchemy: 2.0.25
  • psycopg2-binary: 2.9.9
  • Python: 3.11
  • Kubernetes: CronJob for automation
  • ArgoCD: GitOps deployment

Acceptance Criteria Status

Criteria Status Notes
Great Expectations configured ✅ Complete Full project structure with all configs
Data sources connected ✅ Complete 4 datasources configured
Expectation suites created ✅ Complete 4 comprehensive suites
Validation running automatically ✅ Complete CronJob every 6 hours
Data docs generated ✅ Configured Will generate on first run
Passes AT-E2-004 ⏳ Pending Requires cluster deployment

Testing Results

Unit Tests: ✅ 14/14 Passed

$ pytest services/data-quality/tests/test_config.py -v
================================================= test session starts ==================================================
collected 14 items

tests/test_config.py::TestGreatExpectationsConfig::test_gx_config_exists PASSED                   [  7%]
tests/test_config.py::TestGreatExpectationsConfig::test_gx_config_valid_yaml PASSED               [ 14%]
tests/test_config.py::TestGreatExpectationsConfig::test_datasources_config_exists PASSED          [ 21%]
tests/test_config.py::TestGreatExpectationsConfig::test_datasources_config_valid PASSED           [ 28%]
tests/test_config.py::TestExpectationSuites::test_expectation_suites_exist PASSED                 [ 35%]
tests/test_config.py::TestExpectationSuites::test_expectation_suites_valid_json PASSED            [ 42%]
tests/test_config.py::TestExpectationSuites::test_backstage_suite_expectations PASSED             [ 50%]
tests/test_config.py::TestCheckpoints::test_checkpoints_exist PASSED                              [ 57%]
tests/test_config.py::TestCheckpoints::test_checkpoints_valid_yaml PASSED                         [ 64%]
tests/test_config.py::TestCheckpoints::test_all_databases_checkpoint_complete PASSED              [ 71%]
tests/test_config.py::TestAlertConfiguration::test_alerting_config_exists PASSED                  [ 78%]
tests/test_config.py::TestAlertConfiguration::test_alerting_config_valid PASSED                   [ 85%]
tests/test_config.py::TestRequirements::test_requirements_file_exists PASSED                      [ 92%]
tests/test_config.py::TestRequirements::test_requirements_has_gx PASSED                           [100%]

================================================== 14 passed in 0.07s ==================================================

Next Steps

For Deployment

  1. Deploy to Kubernetes cluster:
kubectl apply -f platform/apps/data-quality-application.yaml
# Or via ArgoCD sync
  1. Verify deployment:
make validate-at-e2-004
  1. Test manual checkpoint:
kubectl exec -it -n fawkes deployment/data-quality -- \
  python3 scripts/run_checkpoint.py backstage_db_checkpoint --json
  1. Configure Mattermost webhook:
kubectl edit secret data-quality-secrets -n fawkes
# Update MATTERMOST_WEBHOOK_URL
  1. View data docs: 
    kubectl port-forward -n fawkes svc/data-quality 8080:8080
open http://localhost:8080

For Production

  1. Use External Secrets Operator for credentials
  2. Configure production Mattermost webhook
  3. Import Grafana dashboard
  4. Set up Prometheus metrics collection
  5. Configure escalation policies

Dependencies

Depends on: Issue #45 (DataHub deployment) Blocks: Issue #48, #49 (dependent data quality features)

Resources

  • Great Expectations docs: https://docs.greatexpectations.io/
  • Issue #47: https://github.com/paruff/fawkes/issues/47
  • AT-E2-004 test specification in docs/implementation-plan/fawkes-handoff-doc.md

Contributors

  • Implementation: GitHub Copilot (Agent)
  • Review: paruff

Status: ✅ Implementation Complete (Pending Deployment) Date: 2024-12-21 Estimated Effort: 5 hours Actual Effort: ~4 hours