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Data Platform Architecture Design

Date: 2025-01-27 Purpose: Design document for unified data platform Status: Design Document

Executive Summary

This document outlines the design for a unified data platform that provides centralized data storage, analytics, and governance across all workspace projects.

Architecture Overview

Components

  1. Data Lake (MinIO, S3, or Azure Blob)
  2. Data Catalog (Apache Atlas, DataHub, or custom)
  3. Analytics Engine (Spark, Trino, or BigQuery)
  4. Data Pipeline (Airflow, Prefect, or custom)
  5. Data Governance (Policies, lineage, quality)

Technology Options

Data Storage

Option 1: MinIO (Recommended - Self-Hosted)

  • S3-compatible
  • Self-hosted
  • Good performance
  • Cost-effective

Option 2: Cloudflare R2

  • S3-compatible
  • No egress fees
  • Managed service
  • Good performance

Option 3: Azure Blob Storage

  • Azure integration
  • Managed service
  • Enterprise features

Recommendation: MinIO for self-hosted, Cloudflare R2 for cloud.

Data Architecture

Data Layers

  1. Raw Layer: Unprocessed data
  2. Cleansed Layer: Cleaned and validated
  3. Curated Layer: Business-ready data
  4. Analytics Layer: Aggregated and analyzed

Data Formats

  • Parquet: Columnar storage
  • JSON: Semi-structured data
  • CSV: Tabular data
  • Avro: Schema evolution

Implementation Plan

Phase 1: Data Storage (Weeks 1-2)

  • Deploy MinIO or configure cloud storage
  • Set up buckets/containers
  • Configure access policies
  • Set up backup

Phase 2: Data Catalog (Weeks 3-4)

  • Deploy data catalog
  • Register data sources
  • Create data dictionary
  • Set up lineage tracking

Phase 3: Data Pipeline (Weeks 5-6)

  • Set up pipeline orchestration
  • Create ETL jobs
  • Schedule data processing
  • Monitor pipelines

Phase 4: Analytics (Weeks 7-8)

  • Set up analytics engine
  • Create data models
  • Build dashboards
  • Set up reporting

Data Governance

Policies

  • Data retention policies
  • Access control policies
  • Privacy policies
  • Quality standards

Lineage

  • Track data flow
  • Document transformations
  • Map dependencies
  • Audit changes

Quality

  • Data validation
  • Quality metrics
  • Anomaly detection
  • Quality reports

Integration

Projects Integration

  • dbis_core: Transaction data
  • the_order: User data
  • Sankofa: Platform metrics
  • All projects: Analytics data

API Integration

  • RESTful APIs for data access
  • GraphQL for queries
  • Streaming APIs for real-time
  • Batch APIs for bulk

Security

Access Control

  • Role-based access
  • Data classification
  • Encryption at rest
  • Encryption in transit

Privacy

  • PII handling
  • Data masking
  • Access logging
  • Compliance tracking

Monitoring

Metrics

  • Data ingestion rate
  • Processing latency
  • Storage usage
  • Query performance

Alerts

  • Pipeline failures
  • Quality issues
  • Storage capacity
  • Access anomalies

Last Updated: 2025-01-27

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