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Data Platform Ecosystem

A comprehensive suite of data platform components designed for enterprise-scale data processing, analytics, and machine learning workflows. These archetypes provide the foundation for building robust, scalable data infrastructure.

Available Components​

Core Platform​

Data Platform Core

Complete data platform foundation with orchestration, storage, and processing capabilities using Apache Airflow and modern data stack.

AdvancedAirflowETLOrchestration

Ingestion Database

PostgreSQL database component optimized for unstructured data ingestion workflows with performance tuning and monitoring.

IntermediatePostgreSQLDatabaseAWS RDS

Data Processing​

Transforms Library

Reusable data transformation functions and utilities for ETL pipelines with comprehensive validation and performance optimization.

IntermediatePythonETLData Quality

PySpark Project

Apache Spark framework for large-scale data processing with Python, optimized for distributed computing and big data analytics.

AdvancedPySparkBig DataDistributed

Ray Components

Distributed computing components using Ray for scalable machine learning workflows and parallel data processing.

AdvancedRayMLDistributed

Architecture Overview​

The data platform ecosystem follows a layered architecture approach:

Technology Stack​

Core Technologies​

  • Apache Airflow: Workflow orchestration and scheduling
  • PostgreSQL: Operational and metadata database
  • Apache Spark: Large-scale data processing
  • Ray: Distributed machine learning and computing
  • Python: Primary development language

Cloud & Infrastructure​

  • AWS S3: Object storage for data lake
  • Kubernetes: Container orchestration
  • Docker: Containerization
  • Terraform: Infrastructure as code

Data Formats & Protocols​

  • Parquet: Columnar storage format
  • Delta Lake: ACID transactions and versioning
  • JSON/CSV: Semi-structured and structured data
  • Apache Kafka: Streaming data ingestion

Use Cases by Component​

Data Platform Core​

  • Enterprise ETL: Large-scale data extraction, transformation, and loading
  • Workflow Orchestration: Complex data pipeline scheduling and monitoring
  • Data Governance: Centralized data quality and lineage management
  • Multi-source Integration: Unified data processing from diverse sources

Processing Components​

  • Batch Processing: Large-scale historical data analysis
  • Stream Processing: Real-time data processing and analytics
  • Machine Learning: Distributed model training and inference
  • Data Science: Interactive analysis and experimentation

Ingestion & Storage​

  • Data Lake Architecture: Scalable raw data storage
  • Data Warehousing: Structured data for analytics
  • Metadata Management: Data catalog and schema evolution
  • Feature Engineering: ML feature preparation and storage

Getting Started​

Prerequisites​

  • Python 3.9+
  • Docker and Kubernetes access
  • Cloud provider account (AWS recommended)
  • Basic understanding of data engineering concepts

Quick Setup​

  1. Choose your starting point:

    # For complete platform
    archetect render git@github.com:p6m-archetypes/dataplatform.archetype.git

    # For specific components
    archetect render git@github.com:p6m-archetypes/pyspark.archetype.git
    archetect render git@github.com:p6m-archetypes/ray-components.archetype.git

  2. Configure environment:

    # Set up configuration
    cp config/environments/dev.yaml.example config/environments/dev.yaml
    # Edit configuration as needed

  3. Deploy locally:

    docker-compose up -d

  4. Access services:

Performance Considerations​

Scalability Patterns​

  • Horizontal Scaling: Add more worker nodes for increased capacity
  • Vertical Scaling: Increase resources per node for memory-intensive workloads
  • Auto-scaling: Dynamic resource allocation based on workload
  • Partitioning: Data partitioning strategies for optimal performance

Optimization Strategies​

  • Columnar Storage: Use Parquet and Delta Lake for analytics workloads
  • Caching: Strategic caching of frequently accessed data
  • Parallel Processing: Leverage Spark and Ray for distributed computing
  • Resource Management: Proper CPU, memory, and GPU allocation

Production Deployment​

Infrastructure Requirements​

  • Compute: Multi-node Kubernetes cluster
  • Storage: High-performance object storage (S3, GCS, Azure Blob)
  • Network: High-bandwidth, low-latency networking
  • Monitoring: Comprehensive observability stack

Security & Compliance​

  • Authentication: RBAC with LDAP/Active Directory integration
  • Encryption: Data encryption at rest and in transit
  • Audit Logging: Comprehensive audit trails
  • Data Privacy: GDPR/CCPA compliance features

Monitoring & Observability​

  • Metrics: Application and infrastructure metrics
  • Logging: Centralized log aggregation
  • Tracing: Distributed tracing for complex workflows
  • Alerting: Proactive alerting and incident response

Best Practices​

Data Engineering​

  • Implement comprehensive data quality checks
  • Use version control for data schemas and transformations
  • Design for fault tolerance and recovery
  • Implement proper data lineage tracking

Development Workflow​

  • Use CI/CD pipelines for deployment automation
  • Implement infrastructure as code
  • Follow data engineering best practices
  • Maintain comprehensive documentation

Operations​

  • Monitor data pipeline health and performance
  • Implement automated backup and recovery
  • Use blue-green deployments for updates
  • Regular performance tuning and optimization

Support & Community​

Documentation​

  • Architecture Guide - Included in generated project documentation
  • Deployment Guide - Kubernetes manifests and guides provided with each archetype
  • API Reference - Generated automatically from code annotations
  • Troubleshooting - Common issues documented in project README files

Community Resources​

  • GitHub Issues for bug reports
  • Discussion forums for questions
  • Slack community for real-time support
  • Contribution guidelines for developers