169 lines
3.0 KiB
Markdown
169 lines
3.0 KiB
Markdown
# Data Platform Architecture Design
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**Date**: 2025-01-27
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**Purpose**: Design document for unified data platform
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**Status**: Design Document
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---
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## Executive Summary
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This document outlines the design for a unified data platform that provides centralized data storage, analytics, and governance across all workspace projects.
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---
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## Architecture Overview
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### Components
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1. **Data Lake** (MinIO, S3, or Azure Blob)
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2. **Data Catalog** (Apache Atlas, DataHub, or custom)
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3. **Analytics Engine** (Spark, Trino, or BigQuery)
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4. **Data Pipeline** (Airflow, Prefect, or custom)
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5. **Data Governance** (Policies, lineage, quality)
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---
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## Technology Options
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### Data Storage
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#### Option 1: MinIO (Recommended - Self-Hosted)
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- S3-compatible
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- Self-hosted
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- Good performance
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- Cost-effective
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#### Option 2: Cloudflare R2
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- S3-compatible
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- No egress fees
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- Managed service
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- Good performance
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#### Option 3: Azure Blob Storage
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- Azure integration
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- Managed service
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- Enterprise features
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**Recommendation**: MinIO for self-hosted, Cloudflare R2 for cloud.
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---
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## Data Architecture
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### Data Layers
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1. **Raw Layer**: Unprocessed data
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2. **Cleansed Layer**: Cleaned and validated
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3. **Curated Layer**: Business-ready data
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4. **Analytics Layer**: Aggregated and analyzed
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### Data Formats
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- **Parquet**: Columnar storage
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- **JSON**: Semi-structured data
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- **CSV**: Tabular data
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- **Avro**: Schema evolution
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---
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## Implementation Plan
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### Phase 1: Data Storage (Weeks 1-2)
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- [ ] Deploy MinIO or configure cloud storage
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- [ ] Set up buckets/containers
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- [ ] Configure access policies
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- [ ] Set up backup
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### Phase 2: Data Catalog (Weeks 3-4)
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- [ ] Deploy data catalog
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- [ ] Register data sources
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- [ ] Create data dictionary
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- [ ] Set up lineage tracking
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### Phase 3: Data Pipeline (Weeks 5-6)
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- [ ] Set up pipeline orchestration
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- [ ] Create ETL jobs
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- [ ] Schedule data processing
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- [ ] Monitor pipelines
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### Phase 4: Analytics (Weeks 7-8)
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- [ ] Set up analytics engine
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- [ ] Create data models
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- [ ] Build dashboards
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- [ ] Set up reporting
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---
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## Data Governance
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### Policies
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- Data retention policies
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- Access control policies
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- Privacy policies
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- Quality standards
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### Lineage
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- Track data flow
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- Document transformations
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- Map dependencies
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- Audit changes
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### Quality
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- Data validation
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- Quality metrics
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- Anomaly detection
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- Quality reports
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---
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## Integration
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### Projects Integration
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- **dbis_core**: Transaction data
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- **the_order**: User data
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- **Sankofa**: Platform metrics
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- **All projects**: Analytics data
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### API Integration
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- RESTful APIs for data access
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- GraphQL for queries
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- Streaming APIs for real-time
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- Batch APIs for bulk
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---
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## Security
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### Access Control
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- Role-based access
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- Data classification
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- Encryption at rest
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- Encryption in transit
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### Privacy
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- PII handling
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- Data masking
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- Access logging
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- Compliance tracking
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---
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## Monitoring
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### Metrics
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- Data ingestion rate
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- Processing latency
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- Storage usage
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- Query performance
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### Alerts
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- Pipeline failures
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- Quality issues
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- Storage capacity
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- Access anomalies
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---
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**Last Updated**: 2025-01-27
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