PDaiPostgres - PostgreSQL Integration for PDai

Overview

PDaiPostgres provides seamless PostgreSQL integration for the PDai analytics ecosystem. This package enables enterprise-grade database connectivity, optimized data processing, and real-time analytics capabilities for production environments.

Features

• High-Performance Connectivity: Optimized connection pooling and query execution
• Data Pipeline Integration: Seamless ETL/ELT workflows with PDai analytics
• Scalable Architecture: Built for enterprise workloads with millions of records
• Security First: SSL/TLS encryption, role-based access control, and audit logging
• Real-Time Processing: Stream processing capabilities for live data analysis
• Smart Caching: Intelligent query result caching for improved performance

Installation

Prerequisites

• R (>= 4.0.0)
• PostgreSQL (>= 12.0)
• PDai base package

From GitHub

# Install devtools if not already installed
if (!require(devtools)) {
  install.packages("devtools")
}

# Install PDaiPostgres from GitHub
devtools::install_github("embeddedlayers/package-PDaiPostgres")

Quick Start

# Load the package
library(PDaiPostgres)

# Configure database connection
conn <- pdai_pg_connect(
  host = "your-postgres-host",
  port = 5432,
  dbname = "your-database",
  user = "your-username",
  password = "your-password",
  ssl = TRUE
)

# Execute analytics pipeline
results <- pdai_pg_pipeline(
  connection = conn,
  query = "SELECT * FROM sales_data",
  analytics = list(
    predict = TRUE,
    visualize = TRUE,
    export = "html"
  )
)

# View results
print(results$summary)

Core Functions

Connection Management

• pdai_pg_connect(): Establish secure database connection
• pdai_pg_pool(): Create connection pool for concurrent operations
• pdai_pg_disconnect(): Safely close connections

Data Operations

• pdai_pg_query(): Execute optimized SQL queries
• pdai_pg_stream(): Stream large datasets efficiently
• pdai_pg_write(): Bulk insert/update operations
• pdai_pg_upsert(): Intelligent upsert operations

Analytics Integration

• pdai_pg_pipeline(): Run PDai analytics on PostgreSQL data
• pdai_pg_aggregate(): Perform in-database aggregations
• pdai_pg_ml(): Execute machine learning models in-database
• pdai_pg_cache(): Manage analytics result caching

Administration

• pdai_pg_monitor(): Monitor query performance
• pdai_pg_optimize(): Automatic query optimization
• pdai_pg_audit(): Audit trail for compliance

Configuration

Create a .pdai_pg_config file in your project root:

default:
  host: localhost
  port: 5432
  ssl: true
  pool_size: 10
  timeout: 30

production:
  host: prod-server.example.com
  port: 5432
  ssl: required
  pool_size: 50
  timeout: 60

Performance Tips

1. Use connection pooling for applications with multiple concurrent users
2. Enable query caching for frequently accessed data
3. Leverage in-database analytics to minimize data transfer
4. Use streaming for large dataset processing
5. Enable compression for network traffic optimization

Security Best Practices

• Always use SSL/TLS connections in production
• Implement role-based access control (RBAC)
• Use environment variables for credentials
• Enable audit logging for compliance
• Regularly update both PDaiPostgres and PostgreSQL

Documentation

Comprehensive documentation available at:

• Package Documentation
• API Reference
• Examples

Support

• Issues: GitHub Issues
• Email: support@embeddedlayers.com
• Community: Discussions

Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Copyright (c) 2024 PeopleDrivenAI LLC (DBA EmbeddedLayers)

Related Projects

• PDai: Core analytics package
• MCP Analytics: Statistical analysis tools for AI assistants

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Enterprise PostgreSQL Integration for AI-Powered Analytics
_{Part of the EmbeddedLayers Analytics Ecosystem}