🏗️ Architecture Documentation¶

📋 Overview¶

Comprehensive architecture documentation for the Azure Real-Time Analytics platform, covering system design, component specifications, data flow patterns, and security implementation.

📑 Table of Contents¶

• System Architecture
• Component Design
• Data Flow Architecture
• Security Architecture
• Network Architecture
• Scalability Design

🎯 System Architecture¶

Architectural Principles¶

1. Cloud-Native Design - Built for Azure with native service integration
2. Event-Driven Architecture - Real-time processing with streaming-first approach
3. Microservices Pattern - Loosely coupled, independently deployable components
4. Zero Trust Security - Comprehensive security with assume-breach mentality
5. Infrastructure as Code - Automated deployment and configuration
6. Observability First - Comprehensive monitoring and alerting

High-Level Architecture¶

graph TB
    subgraph "Data Sources"
        IoT[IoT Devices]
        Apps[Applications]
        DB[Databases]
        Files[File Systems]
    end

    subgraph "Ingestion Layer"
        subgraph "Streaming"
            Kafka[Confluent Kafka]
            EH[Event Hubs]
            SA[Stream Analytics]
        end
        subgraph "Batch"
            ADF[Data Factory]
            FTP[File Transfer]
        end
    end

    subgraph "Processing Layer"
        subgraph "Compute"
            DBR[Databricks Runtime]
            Spark[Apache Spark]
            Python[Python/Scala]
        end
        subgraph "Orchestration"
            DatabricksJobs[Databricks Jobs]
            ADFPipelines[ADF Pipelines]
        end
    end

    subgraph "Storage Layer"
        subgraph "Data Lake"
            Bronze[Bronze Layer - Raw]
            Silver[Silver Layer - Cleansed]
            Gold[Gold Layer - Curated]
        end
        UC[Unity Catalog]
        DeltaLake[Delta Lake]
    end

    subgraph "Analytics Layer"
        subgraph "AI/ML"
            OpenAI[Azure OpenAI]
            MLflow[MLflow]
            AutoML[AutoML]
        end
        subgraph "BI"
            PowerBI[Power BI]
            DirectLake[Direct Lake]
        end
    end

    subgraph "Consumption"
        Dashboards[Dashboards]
        APIs[REST APIs]
        Reports[Reports]
        Apps2[Applications]
    end

    IoT --> Kafka
    Apps --> EH
    DB --> ADF
    Files --> FTP

    Kafka --> DBR
    EH --> SA
    SA --> DBR
    ADF --> Bronze
    FTP --> Bronze

    DBR --> Bronze
    Bronze --> Silver
    Silver --> Gold

    Gold --> PowerBI
    Gold --> APIs

    OpenAI --> Silver
    MLflow --> DBR

    PowerBI --> Dashboards
    DirectLake --> Reports
    APIs --> Apps2

Architecture Layers¶

🔧 Component Design¶

Azure Databricks¶

Purpose: Unified analytics and processing platform

Configuration:
  Workspace:
    Tier: Premium
    Region: East US 2
    Storage: ADLS Gen2

  Clusters:
    Stream_Cluster:
      Node_Type: Standard_D16s_v3
      Min_Nodes: 2
      Max_Nodes: 50
      Autoscale: true
      Spot_Instances: 70%
      Runtime: 13.3 LTS

    Batch_Cluster:
      Node_Type: Standard_E8s_v3
      Min_Nodes: 1
      Max_Nodes: 100
      Autoscale: true
      Spot_Instances: 90%
      Runtime: 13.3 LTS

  Features:
    - Unity Catalog
    - MLflow
    - Delta Live Tables
    - Photon Acceleration

Confluent Kafka¶

Purpose: High-throughput streaming platform

Configuration:
  Cluster:
    Type: Dedicated
    Cloud: Azure
    Region: East US 2

  Specifications:
    Kafka_Version: 3.5
    CKU: 10
    Storage: 10 TB

  Topics:
    events:
      Partitions: 20
      Replication: 3
      Retention: 7 days

    metrics:
      Partitions: 10
      Replication: 3
      Retention: 3 days

  Security:
    Authentication: SASL/PLAIN
    Encryption: TLS 1.2
    ACLs: Enabled
    IP_Whitelist: true

Azure Data Lake Storage Gen2¶

Purpose: Scalable data lake storage

Configuration:
  Account:
    Name: rtadatalake
    Performance: Standard
    Replication: ZRS

  Containers:
    bronze:
      Access: Private
      Lifecycle: 90 days hot, 180 days cool

    silver:
      Access: Private
      Lifecycle: 180 days hot, archive after 1 year

    gold:
      Access: Private
      Lifecycle: Always hot

  Features:
    - Hierarchical Namespace
    - Encryption at Rest
    - Soft Delete
    - Versioning

Power BI¶

Purpose: Business intelligence and visualization

Configuration:
  Capacity:
    Type: Premium
    SKU: P2
    Region: East US 2

  Workspace:
    Name: RealTimeAnalytics
    Mode: Premium

  Datasets:
    Connection: Direct Lake
    Refresh: Automatic

  Features:
    - Paginated Reports
    - AI Insights
    - Deployment Pipelines
    - Advanced Security

🔄 Data Flow Architecture¶

Streaming Data Flow¶

sequenceDiagram
    participant Source
    participant Kafka
    participant Databricks
    participant Bronze
    participant Silver
    participant Gold
    participant PowerBI

    Source->>Kafka: Send Events
    Kafka->>Databricks: Consume Stream
    Databricks->>Bronze: Write Raw Data
    Bronze->>Databricks: Read for Processing
    Databricks->>Silver: Write Cleansed Data
    Silver->>Databricks: Read for Aggregation
    Databricks->>Gold: Write Business Data
    Gold->>PowerBI: Direct Lake Query

Data Processing Patterns¶

Bronze Layer Processing¶

# Bronze layer - raw data ingestion
bronze_stream = (
    spark.readStream
    .format("kafka")
    .option("kafka.bootstrap.servers", kafka_servers)
    .option("subscribe", "events")
    .load()
    .selectExpr("CAST(value AS STRING)")
    .writeStream
    .format("delta")
    .outputMode("append")
    .option("checkpointLocation", checkpoint_path)
    .trigger(processingTime="10 seconds")
    .table("bronze.raw_events")
)

Silver Layer Processing¶

# Silver layer - data cleansing and validation
silver_stream = (
    spark.readStream
    .format("delta")
    .table("bronze.raw_events")
    .transform(parse_json)
    .transform(validate_schema)
    .transform(enrich_data)
    .transform(apply_data_quality_rules)
    .writeStream
    .format("delta")
    .outputMode("append")
    .option("checkpointLocation", checkpoint_path)
    .trigger(processingTime="30 seconds")
    .table("silver.validated_events")
)

Gold Layer Processing¶

# Gold layer - business aggregations
gold_stream = (
    spark.readStream
    .format("delta")
    .table("silver.validated_events")
    .groupBy(
        window("event_time", "1 minute"),
        "product_id",
        "region"
    )
    .agg(
        count("*").alias("event_count"),
        sum("amount").alias("total_amount"),
        avg("amount").alias("avg_amount")
    )
    .writeStream
    .format("delta")
    .outputMode("complete")
    .option("checkpointLocation", checkpoint_path)
    .trigger(processingTime="1 minute")
    .table("gold.metrics_1min")
)

Data Quality Framework¶

🔒 Security Architecture¶

Zero Trust Security Model¶

graph TB
    subgraph "Identity Layer"
        AAD[Azure AD]
        MFA[Multi-Factor Auth]
        CA[Conditional Access]
        PIM[Privileged Identity]
    end

    subgraph "Network Layer"
        PE[Private Endpoints]
        NSG[Network Security Groups]
        FW[Azure Firewall]
        VNET[Virtual Network]
    end

    subgraph "Data Layer"
        CMK[Customer Managed Keys]
        TDE[Transparent Data Encryption]
        RLS[Row Level Security]
        DLP[Data Loss Prevention]
    end

    subgraph "Application Layer"
        RBAC[Role-Based Access]
        OAuth[OAuth 2.0]
        KV[Key Vault]
        MI[Managed Identity]
    end

    subgraph "Monitoring Layer"
        SC[Security Center]
        Sentinel[Azure Sentinel]
        Monitor[Azure Monitor]
        Audit[Audit Logs]
    end

Security Controls¶

Identity & Access Management¶

Azure_AD:
  Authentication:
    - Multi-Factor Authentication
    - Conditional Access Policies
    - Risk-Based Authentication

  Authorization:
    - Role-Based Access Control
    - Attribute-Based Access
    - Just-In-Time Access

Service_Principals:
  - Managed Identities
  - Certificate Authentication
  - Secret Rotation

Data Protection¶

Encryption:
  At_Rest:
    - Azure Storage Service Encryption
    - Customer Managed Keys
    - Double Encryption

  In_Transit:
    - TLS 1.2 minimum
    - Certificate Pinning
    - IPSec for VPN

  In_Processing:
    - Confidential Computing
    - Secure Enclaves

Network Security¶

Network_Isolation:
  - Virtual Networks
  - Network Security Groups
  - Azure Firewall
  - DDoS Protection

Private_Connectivity:
  - Private Endpoints
  - Service Endpoints
  - ExpressRoute
  - VPN Gateway

🌐 Network Architecture¶

Network Topology¶

graph LR
    subgraph "On-Premises"
        Corp[Corporate Network]
        DC[Data Center]
    end

    subgraph "Azure Region - East US 2"
        subgraph "Hub VNet"
            FW2[Azure Firewall]
            VPN[VPN Gateway]
            ER[ExpressRoute]
        end

        subgraph "Spoke VNet - Data"
            subgraph "Subnet - Compute"
                DBR2[Databricks]
                ADF2[Data Factory]
            end

            subgraph "Subnet - Storage"
                ADLS[Data Lake]
                KV2[Key Vault]
            end

            subgraph "Subnet - Private Endpoints"
                PE1[Storage PE]
                PE2[Databricks PE]
            end
        end

        subgraph "Spoke VNet - Analytics"
            PBI[Power BI Gateway]
            API[API Management]
        end
    end

    Corp --> VPN
    DC --> ER
    VPN --> FW2
    ER --> FW2
    FW2 --> DBR2
    FW2 --> ADLS
    DBR2 --> PE1
    ADLS --> PE1

Network Configuration¶

📈 Scalability Design¶

Auto-Scaling Strategy¶

Databricks_Clusters:
  Streaming:
    Metric: CPU Utilization
    Scale_Up: >70% for 5 min
    Scale_Down: <30% for 10 min
    Min: 2 nodes
    Max: 50 nodes

  Batch:
    Metric: Queue Length
    Scale_Up: >10 jobs pending
    Scale_Down: 0 jobs for 15 min
    Min: 0 nodes
    Max: 100 nodes

Event_Hubs:
  Throughput_Units:
    Auto_Inflate: true
    Max_TU: 40

Storage:
  Performance_Tier: Auto-scale
  Throughput: 10,000 - 50,000 RU/s

Performance Targets¶

Capacity Planning¶

graph LR
    subgraph "Current State"
        C1[1.2M events/sec]
        C2[2.3 PB storage]
        C3[450 DBU/hour]
    end

    subgraph "6 Month Projection"
        P1[1.8M events/sec]
        P2[3.5 PB storage]
        P3[650 DBU/hour]
    end

    subgraph "12 Month Projection"
        F1[2.5M events/sec]
        F2[5.2 PB storage]
        F3[900 DBU/hour]
    end

    C1 --> P1 --> F1
    C2 --> P2 --> F2
    C3 --> P3 --> F3

📚 Related Documentation¶

• Component Details
• Data Flow Patterns
• Security Implementation
• Network Design
• Disaster Recovery

Last Updated: January 28, 2025
Version: 2.0.0
Maintainer: Platform Architecture Team