# Database Architecture

## Overview

This section details the database architecture for the Animal Genetics Research Platform. The platform utilizes a dual database approach to optimize for different data types and access patterns, ensuring high performance, scalability, and data integrity.

## Core Database Stack

The platform employs two complementary database systems:

| Database   | Type             | Purpose                                       | Provider                  |
| ---------- | ---------------- | --------------------------------------------- | ------------------------- |
| PostgreSQL | Relational (SQL) | Genetic/genomic data and farm management      | Amazon RDS for PostgreSQL |
| DynamoDB   | NoSQL            | User profiles, sessions, and LLM chat history | Amazon DynamoDB           |

## Database Architecture

The database architecture is designed to support the diverse needs of the platform:

{% @mermaid/diagram content="graph TD
A\[Applications] --> B\[PostgreSQL]
A --> C\[DynamoDB]
B --> D\[Genetic Data]
B --> E\[Farm Management]
B --> F\[Research Data]
B --> G\[Animal Records]
C --> H\[User Profiles]
C --> I\[Session Data]
C --> J\[LLM Chat History]
C --> K\[User Preferences]

```
style A fill:#bbf,stroke:#333,stroke-width:1px
style B fill:#bfb,stroke:#333,stroke-width:1px
style C fill:#fbb,stroke:#333,stroke-width:1px
style D fill:#fbf,stroke:#333,stroke-width:1px
style E fill:#bff,stroke:#333,stroke-width:1px
style F fill:#ffd,stroke:#333,stroke-width:1px
style G fill:#ddd,stroke:#333,stroke-width:1px
style H fill:#fdb,stroke:#333,stroke-width:1px
style I fill:#bfd,stroke:#333,stroke-width:1px
style J fill:#dbf,stroke:#333,stroke-width:1px
style K fill:#dfb,stroke:#333,stroke-width:1px" %}
```

## PostgreSQL Implementation

Amazon RDS for PostgreSQL is used for structured data with complex relationships:

### Genetic Data Storage

* Implements the [Sheep Genetics Schema](/animal-research-hub/technical-architecture/system-components/sheep-genetics-schema.md)
* Stores genomic data with efficient indexing
* Manages complex pedigree relationships
* Supports advanced genetic analysis queries
* Handles breeding value calculations and storage

### Farm Management Data

* Stores animal records and metadata
* Manages breeding event tracking
* Records performance measurements
* Supports farm analytics and reporting
* Maintains historical data for trend analysis

### Research Data Management

* Stores research project data and metadata
* Manages experimental designs and protocols
* Records analysis results and findings
* Supports collaborative research data sharing
* Maintains publication-related data

### Key PostgreSQL Features

The platform leverages PostgreSQL's capabilities:

* **Advanced Indexing**: Optimized for genetic data queries
* **Partitioning**: For efficient management of large datasets
* **JSON Support**: For semi-structured data within relational context
* **Full-Text Search**: For research content and documentation
* **Transactional Integrity**: For critical breeding and research operations
* **Spatial Data**: For geographic analysis of farm data
* **Triggers and Stored Procedures**: For complex data validation and processing

## DynamoDB Implementation

Amazon DynamoDB is used for high-throughput, low-latency access to user-related data:

### User Profile Storage

* Stores user account information
* Manages role and permission data
* Records user preferences and settings
* Supports personalization features
* Maintains user activity history

### Session Management

* Stores authentication tokens and session data
* Manages concurrent session information
* Records session analytics and metrics
* Supports session recovery and persistence
* Handles security-related session attributes

### LLM Chat History

* Stores conversation history with Emilia AI
* Manages context for ongoing conversations
* Records interaction patterns and preferences
* Supports conversation export and sharing
* Maintains privacy and retention policies

### Key DynamoDB Features

The platform leverages DynamoDB's capabilities:

* **Auto-scaling**: For handling variable workloads
* **Global Tables**: For multi-region availability
* **Point-in-time Recovery**: For data protection
* **TTL (Time to Live)**: For automatic data expiration
* **Streams**: For event-driven architectures
* **Secondary Indexes**: For flexible query patterns
* **Transactions**: For multi-item operations

## Data Integration and Synchronization

The platform implements several strategies for maintaining consistency across databases:

* **Event-driven Updates**: Using message queues for cross-database synchronization
* **Change Data Capture**: For tracking and propagating changes
* **Batch Synchronization**: For periodic reconciliation of data
* **Transactional Outbox Pattern**: For reliable cross-database operations
* **Eventual Consistency Model**: For non-critical data synchronization

## Data Security and Compliance

Database security measures include:

* **Encryption at Rest**: For all stored data
* **Encryption in Transit**: For all data communications
* **Fine-grained Access Control**: For data access management
* **Audit Logging**: For tracking all data access and modifications
* **Backup and Recovery**: For data protection and disaster recovery
* **Compliance Controls**: For meeting regulatory requirements

## Performance Optimization

Database performance is optimized through:

* **Query Optimization**: For efficient data retrieval
* **Connection Pooling**: For efficient resource utilization
* **Read Replicas**: For distributing read workloads
* **Caching Strategies**: For frequently accessed data
* **Data Partitioning**: For improved query performance
* **Index Optimization**: For faster data access
* **Query Monitoring**: For identifying and resolving bottlenecks

## Scalability Approach

The database architecture supports scalability through:

* **Horizontal Scaling**: Adding more database instances
* **Vertical Scaling**: Increasing resources for existing instances
* **Read/Write Splitting**: Separating read and write operations
* **Sharding**: Distributing data across multiple instances
* **Auto-scaling**: Automatically adjusting resources based on demand

## Data Lifecycle Management

The platform implements comprehensive data lifecycle policies:

* **Data Retention**: Policies for how long different data types are kept
* **Archiving**: Moving historical data to cost-effective storage
* **Purging**: Removing unnecessary or expired data
* **Versioning**: Tracking changes to critical data over time
* **Lineage Tracking**: Recording the origin and transformations of data

## Integration with Analytics

The database architecture supports analytics through:

* **Data Warehousing**: Integration with analytics platforms
* **ETL Pipelines**: For transforming data for analysis
* **Real-time Analytics**: For immediate insights
* **Reporting Infrastructure**: For generating business intelligence
* **Machine Learning Integration**: For predictive analytics


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