Data Architecture

Auteur

Affiliations

[Author Name]

Université de Toulon

LIS UMR CNRS 7020

Purpose of this Document

This document defines the data architecture for [System Name], including data models, storage strategies, flows, and governance aspects.

Data Strategy Overview

Data Philosophy: [Approach to data management]
Primary Goals:
  - [Goal 1, e.g., "Support business analytics"]
  - [Goal 2, e.g., "Ensure data integrity"]
  - [Goal 3, e.g., "Enable real-time processing"]

Key Principles:
  - [Principle 1, e.g., "Data is an enterprise asset"]
  - [Principle 2, e.g., "Single source of truth"]
  - [Principle 3, e.g., "Privacy by design"]

Data Model Overview

Domain Models:
  - Entity: [Primary entity name]
    Purpose: [Business function]
    Attributes: [Key fields]
    Relations: [Connections to other entities]
    Volume: [Expected data size]
    Growth: [Projected growth rate]
  
  - Entity: [Secondary entity name]
    # Similar structure as above

Storage Types:
  Operational:
    - Type: [Database type, e.g., "PostgreSQL"]
      Purpose: [Primary use cases]
      Scale: [Size/operations per second]
      Key Tables: [Critical data elements]
      Indices: [Important indices]
  
  Analytical:
    - Type: [Database type, e.g., "Snowflake"]
      Purpose: [Primary use cases]
      Scale: [Size/operations]
      Key Marts: [Important data marts]
      Access Patterns: [Query patterns]

Conceptual Data Model

erDiagram
    CUSTOMER ||--o{ ORDER : places
    ORDER ||--|{ LINE_ITEM : contains
    PRODUCT ||--o{ LINE_ITEM : "ordered in"
    CUSTOMER }|..|{ PAYMENT_METHOD : has
    ORDER ||--|| INVOICE : generates
    INVOICE ||--o{ PAYMENT : receives
    
    CUSTOMER {
        string id PK
        string name
        string email
        date created_at
    }
    
    ORDER {
        string id PK
        string customer_id FK
        date created_at
        string status
        decimal total_amount
    }
    
    LINE_ITEM {
        string id PK
        string order_id FK
        string product_id FK
        int quantity
        decimal unit_price
        decimal total_price
    }
    
    PRODUCT {
        string id PK
        string name
        string description
        decimal price
        int inventory_count
    }

Logical Data Model

@startuml
!define table(x) class x << (T,#FFAAAA) >>
!define primary_key(x) <b>x</b>
!define foreign_key(x) <i>x</i>

table(CUSTOMER) {
    primary_key(id): UUID
    name: VARCHAR(100)
    email: VARCHAR(100)
    created_at: TIMESTAMP
    status: VARCHAR(20)
}

table(ORDER) {
    primary_key(id): UUID
    foreign_key(customer_id): UUID
    created_at: TIMESTAMP
    status: VARCHAR(20)
    total_amount: DECIMAL(10,2)
}

table(LINE_ITEM) {
    primary_key(id): UUID
    foreign_key(order_id): UUID
    foreign_key(product_id): UUID
    quantity: INTEGER
    unit_price: DECIMAL(10,2)
    total_price: DECIMAL(10,2)
}

table(PRODUCT) {
    primary_key(id): UUID
    name: VARCHAR(100)
    description: TEXT
    price: DECIMAL(10,2)
    category: VARCHAR(50)
    inventory_count: INTEGER
}

CUSTOMER "1" -- "0..*" ORDER : places
ORDER "1" -- "1..*" LINE_ITEM : contains
PRODUCT "1" -- "0..*" LINE_ITEM : "ordered in"
@enduml

Physical Data Model

-- Example schema definition for a key table

CREATE TABLE customers (
    id UUID PRIMARY KEY,
    name VARCHAR(100) NOT NULL,
    email VARCHAR(100) NOT NULL UNIQUE,
    created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
    status VARCHAR(20) NOT NULL DEFAULT 'active',
    last_login TIMESTAMP,
    preferences JSONB
);

CREATE INDEX idx_customers_email ON customers(email);
CREATE INDEX idx_customers_status ON customers(status);

Data Flow Architecture

@startuml
database "Operational DB" as odb
database "Analytics DB" as adb
queue "Message Queue" as mq
component "ETL Pipeline" as etl
component "Data API" as api
actor "Users" as users
component "Applications" as apps
component "BI Tools" as bi

users --> apps
apps --> api
api --> odb : CRUD operations
odb --> mq : Change events
mq --> etl : Batch processing
etl --> adb : Data loading
bi --> adb : Analytics queries
api --> adb : Reporting data
@enduml

Data Flow Diagrams

graph LR
    A[Source Systems] --> B[Data Ingestion]
    B --> C[Data Processing]
    C --> D[Data Storage]
    D --> E[Data Access]
    E --> F[Data Consumers]
    
    subgraph "Data Pipeline"
    B
    C
    D
    end

Data Flow Sequence

@startuml
participant "User Interface" as UI
participant "API Layer" as API
participant "Cache" as Cache
participant "Database" as DB
participant "Event Bus" as Events
participant "Analytics" as Analytics

UI -> API: Request Data
API -> Cache: Check Cache
alt data in cache
    Cache --> API: Return Cached Data
else data not in cache
    API -> DB: Query Data
    DB --> API: Return Data
    API -> Cache: Update Cache
end
API --> UI: Return Response
API -> Events: Publish Event
Events -> Analytics: Process Event
@enduml

Data Storage Strategy

Primary Data Stores:
  - Type: [Storage type, e.g., "Relational Database"]
    Technology: [Specific technology, e.g., "PostgreSQL 13"]
    Purpose: [Primary use cases]
    Data Types: [Key data categories stored]
    Scaling: [Horizontal/Vertical approach]
    Backups: [Strategy]
  
  - Type: [Storage type, e.g., "Document Database"]
    Technology: [Specific technology, e.g., "MongoDB 5"]
    Purpose: [Primary use cases]
    Data Types: [Key data categories stored]
    Scaling: [Approach]
    Backups: [Strategy]

Caching Strategy:
  - Level: [Application/Database/API/etc.]
    Technology: [e.g., "Redis 6"]
    Purpose: [Use cases]
    TTL: [Time to live policies]
    Invalidation: [Strategy]

Data Lifecycle:
  - Hot Data: [Storage and retention]
  - Warm Data: [Storage and retention]
  - Cold Data: [Storage and retention]
  - Archival: [Strategy and technology]

Data Governance

Data Classification:
  - PII Data:
      Examples: [List of PII elements]
      Protection: [Controls applied]
      Access: [Who can access]
      Retention: [How long kept]
  
  - Business Critical:
      Examples: [List of examples]
      Protection: [Controls applied]
      Access: [Who can access]
      Retention: [How long kept]
  
  - General Data:
      Protection: [Controls applied]
      Retention: [How long kept]

Data Lifecycle:
  Collection:
    Methods: [How data is collected]
    Consent: [How consent is managed]
    Validation: [Validation approaches]
  
  Processing:
    Controls: [Processing controls]
    Transformations: [Key transformations]
    Quality: [Quality measures]
  
  Storage:
    Encryption: [At-rest encryption approach]
    Segregation: [Data isolation approach]
    Backup: [Backup strategy]
  
  Deletion:
    Process: [How data is deleted]
    Verification: [How deletion is verified]
    Records: [Deletion tracking]

Quality Controls:
  Validation:
    Input: [Input validation approaches]
    Business Rules: [Rule validation]
    Integrity: [Referential integrity]
  
  Monitoring:
    Metrics: [Data quality metrics]
    Alerts: [When alerts are triggered]
    Remediation: [How issues are fixed]
  
  Governance:
    Ownership: [Data ownership model]
    Stewardship: [Stewardship approach]
    Policies: [Key governance policies]

Data Security

Encryption:
  Transit: [Encryption methods for data in transit]
  Rest: [Encryption methods for data at rest]
  Keys: [Key management approach]

Access Control:
  Authentication: [Methods]
  Authorization: [Models - RBAC, ABAC, etc.]
  Rows/Columns: [Data filtering approach]
  Masking: [Data masking strategy]

Audit:
  Changes: [How data changes are tracked]
  Access: [How data access is logged]
  Reports: [Key audit reports]
  Retention: [How long audit data is kept]

Storage Solutions

Data Type	Storage Solution	Justification	Usage Patterns
Transactional	[Solution]	[Rationale]	[Access patterns]
Document	[Solution]	[Rationale]	[Access patterns]
Analytical	[Solution]	[Rationale]	[Access patterns]
Binary/Blob	[Solution]	[Rationale]	[Access patterns]
Time Series	[Solution]	[Rationale]	[Access patterns]
Cache	[Solution]	[Rationale]	[Access patterns]

Persistence Strategies

Data Access Patterns

• Read Patterns
  • [List common read patterns]
  • [List performance optimizations]
• Write Patterns
  • [List common write patterns]
  • [List durability guarantees]

Caching Strategy

• Cache Levels
  • [List each cache level]
  • [Purpose and invalidation approach]
• Cache Consistency
  • [Approach to maintaining consistency]
  • [Trade-offs made]

Backup Strategy

• Backup Types
  • [Full/Incremental/Differential approach]
  • [Schedule and retention]
• Recovery Testing
  • [Testing approach]
  • [Success criteria]

Schema Management

Version Control:
  Tool: [Schema migration tool, e.g., "Flyway"]
  Process: [Migration approach]
  Rollback: [How to handle failures]
  Testing: [How migrations are tested]

Migration Strategy:
  Planning:
    - [Assessment steps]
    - [Impact analysis]
    - [Stakeholder approval]
  
  Testing:
    - [Test environments]
    - [Data integrity checks]
    - [Performance testing]
  
  Deployment:
    - [Deployment process]
    - [Downtime requirements]
    - [Rollback procedures]
  
  Verification:
    - [Post-deployment checks]
    - [Monitoring approach]
    - [Success metrics]

Data Integration

ETL/ELT Processes:
  Tools: [Technologies used]
  Schedules: [Frequency of processing]
  Monitoring: [How processes are monitored]

APIs:
  Data APIs: [API-based data access]
  Formats: [Data exchange formats]
  Standards: [API standards]

Event-driven:
  Event Types: [Key data events]
  Consumers: [Event consumers]
  Guarantees: [Delivery guarantees]

Template Validation Checklist

• Data strategy defined
• Conceptual data model documented
• Logical data model documented
• Data flows mapped
• Storage strategies specified
• Data governance defined
• Security measures specified
• Schema management approach described
• Integration patterns identified
• Team review completed

Related Views

• System Context - System boundaries
• Logical Architecture - Overall system structure
• Integration Architecture - Data integration interfaces