Data Quality & Observability Accelerator
Automated Data Quality Monitoring in 2-3 Weeks
Monitor 1,000+ Unity Catalog tables with automated quality checks, ML-powered anomaly detection, and intelligent remediation workflows. Production-ready solution with 37+ documentation files and comprehensive observability dashboards.
Architecture & Workflows
End-to-end quality monitoring pipeline with automated checks, ML-based detection, and closed-loop remediation workflows.
Monitor 1,000+ Unity Catalog tables with automated quality checks, ML-powered anomaly detection, and intelligent remediation workflows. Production-ready solution with 37+ documentation files and comprehensive observability dashboards.
Solution Highlights
Enterprise Scale
Monitor 1,000+ Unity Catalog tables with configurable quality checks. Automated scheduling and parallel execution for optimal performance.
- ✓ Monitor 1,000+ Tables
- ✓ Parallel Execution Engine
- ✓ Automated Scheduling
5 Quality Dimensions
Completeness, uniqueness, validity, consistency, timeliness checks with flexible thresholds and business rules.
- ✓ Completeness & Uniqueness
- ✓ Timeliness & Consistency
- ✓ Validity Checks
ML-Powered Anomaly Detection
Statistical models, isolation forest, and LSTM algorithms detect quality anomalies automatically with < 5 minute detection time.
- ✓ Isolation Forest Algorithms
- ✓ LSTM Time-Series Models
- ✓ < 5 Minute Detection Time
Intelligent Alerting
Multi-channel alerts (Slack, email, PagerDuty) with severity-based routing, alert aggregation, and deduplication.
- ✓ Multi-Channel Integration
- ✓ Alert Deduplication
- ✓ Severity Routing
Automated Remediation
80% automation rate with workflow orchestration for quality issue resolution. Intelligent remediation strategies for common issues.
- ✓ Automated Issue Resolution
- ✓ Workflow Orchestration
- ✓ Intelligent Strategy Selection
Comprehensive Dashboards
Quality overview, anomaly detection, remediation tracking, and SLA compliance dashboards with real-time updates.
- ✓ Quality Overview Boards
- ✓ Remediation Tracking
- ✓ SLA Compliance Views
PII Detection
Automated PII detection, classification, and masking for GDPR/HIPAA compliance with configurable sensitivity levels.
- ✓ Automated PII Classification
- ✓ Dynamic Masking
- ✓ GDPR/HIPAA Compliance
Unity Catalog Integration
Metadata-driven quality checks with lineage tracking. Seamless integration with Unity Catalog for governance.
- ✓ Native Unity Catalog Support
- ✓ Data Lineage Tracking
- ✓ Metadata-Driven Configuration
Technical Specifications
Scale & Performance
- Tables: 1,000+ tables monitored
- Detection Time: < 5 minutes for quality issues
- Quality Checks: 50+ pre-configured checks
- Automation Rate: 80% automated remediation
Quality Dimensions
- Completeness: Null checks, required fields
- Uniqueness: Duplicate detection, key validation
- Validity: Format, range, type validation
- Consistency: Cross-table, referential integrity
ML & Anomaly Detection
- Models: 3 pre-trained ML models
- Algorithms: Statistical, Isolation Forest, LSTM
- False Positives: < 5% false positive rate
- Detection: Real-time anomaly detection
Observability
- Dashboards: 6 comprehensive dashboards
- Metrics: Quality score (0.0-1.0)
- Alerting: Slack, email, PagerDuty
- API: OpenAPI 3.0 compliant REST API
What's Included
Implementation
Complete Implementation
- Quality framework with 50+ pre-configured checks
- 3 pre-trained ML models for anomaly detection
- Automated remediation workflows
- PII detection and classification engine
- Databricks notebooks and jobs
- REST API for integration
- Unity Catalog integration
- Unit and integration tests
Documentation
Comprehensive Documentation (37 documents)
- Executive Summary with business case
- Architecture Design with ML models
- Quality Framework Implementation Guide
- Anomaly Detection Implementation Guide
- Operations Guide with runbooks
- Security & Compliance Guide
- Disaster Recovery procedures
- API Reference and integration guides
Dashboards
Quality & Observability Dashboards
- Quality Overview Dashboard
- Anomaly Detection Dashboard
- Remediation Tracking Dashboard
- SLA Compliance Dashboard
- Quality Trends Dashboard
- PII Detection Dashboard
Support
Support & Training
- Initial deployment support (2-3 weeks)
- ML model training and tuning
- Quality rules configuration
- Knowledge transfer sessions
- 30-day post-deployment support
- Optional ongoing maintenance
Use Cases
Proactive Quality Monitoring
Detect quality issues before they impact business. Real-time monitoring with < 5 minute detection time and automated alerting.
ML Model Quality
Ensure training data quality for ML models. Prevent model drift and prediction errors from poor data quality.
Compliance & Audit
Demonstrate data quality for compliance audits. PII detection, quality metrics, and complete audit trails.
Cost Reduction
Reduce engineering overhead on quality issues. 80% automation saves 20-30% of engineering time and resources.
Get Started with Data Quality Observability
See how Data Quality Observability can transform your data infrastructure. Or contact us directly: office@dhristhi.com
Ready to take your business to the next level?
Contact Us
Feel free to use the form or drop us an email.
