DOCC Platform Guide
Comprehensive guide to using the Data Operation Control Center (DOCC) platform. Learn about core features, data operations, and best practices.
Platform Overview
The Data Operation Control Center (DOCC) is a unified platform that centralizes all your data operations needs. From data ingestion and quality monitoring to advanced analytics and machine learning, DOCC provides a comprehensive solution for modern data teams.
Key Benefits
- Unified Experience: One platform for all data operations - no more tool sprawl
- Multi-Engine Support: Native integration with Apache Spark, Trino, and Apache Flink
- Enterprise Security: OAuth2/OIDC authentication with role-based access control
- Real-time Operations: Live monitoring, streaming analytics, and instant insights
Core Platform Components
Dashboard & Monitoring
The central command center provides real-time visibility into your entire data ecosystem. Monitor system health, track job performance, and get instant alerts on issues.
- Real-time system health monitoring
- Customizable dashboards and KPI tracking
- Proactive alerting and notifications
- Performance analytics and trends
Data Catalog & Discovery
Centralized metadata management enables easy data discovery across your entire data landscape. Find, understand, and trust your data assets.
- Universal data discovery and search
- Automated metadata extraction
- Data lineage visualization
- Business glossary and data classification
Quality Framework
Comprehensive data quality monitoring ensures your data is reliable and trustworthy. Automated quality checks and remediation workflows maintain data integrity.
- Automated quality monitoring
- Custom validation rules
- Quality scorecards and reporting
- Automated remediation workflows
Visual Pipeline Designer
Drag-and-drop interface for building complex data workflows. Create sophisticated pipelines without writing code, with real-time monitoring and execution.
- Visual workflow designer
- Pre-built component library
- Real-time execution monitoring
- Template gallery for common patterns
Data Operations Workflow
DOCC supports the complete data operations lifecycle:
| Phase | Description | Key Features |
|---|---|---|
| Ingestion | Connect and ingest data from multiple sources | 100+ connectors, real-time streaming, batch processing |
| Preparation | Clean, transform, and prepare data for analysis | Visual transformations, data profiling, schema evolution |
| Quality | Monitor and ensure data quality standards | Automated checks, custom rules, quality scorecards |
| Analytics | Analyze data with multiple processing engines | SQL interface, notebooks, ML pipelines |
| Governance | Manage access, compliance, and data policies | RBAC, audit trails, policy enforcement |
Getting Started
Follow these steps to start using DOCC effectively:
1. Initial Setup
Configure your DOCC instance and connect your first data sources.
2. Connect Data Sources
Set up connections to your databases, files, and streaming sources.
3. Create Your First Pipeline
Build a data processing pipeline using the visual designer.
4. Set Up Quality Monitoring
Configure data quality rules and monitoring for your datasets.
Best Practices
Recommended Practices
- Start with a pilot project to familiarize your team with the platform
- Establish data quality standards early in your implementation
- Use the data catalog to document and classify your data assets
- Implement proper access controls and security policies
- Monitor system performance and optimize resource usage
Common Pitfalls to Avoid
- Don't skip the planning phase - understand your data landscape first
- Avoid creating overly complex pipelines without proper testing
- Don't neglect user training and change management
- Avoid insufficient monitoring and alerting setup
Next Steps
Ready to dive deeper? Explore these resources: