Solution Components Overview¶

Overview¶

This document provides detailed specifications for all components in the Azure Real-Time Analytics solution. Each component is designed for high availability, scalability, and enterprise-grade security.

Table of Contents¶

Data Ingestion Components
Processing Components
Storage Components
Analytics Components
Governance Components
Security Components
Monitoring Components
Component Dependencies

Data Ingestion Components¶

Azure Event Hubs¶

Purpose: High-throughput event streaming platform

Specifications:

Aspect	Configuration	Notes
Tier	Premium	Dedicated capacity
Throughput Units	20-100 (auto-inflate)	Dynamic scaling
Partitions	32 per hub	Parallel processing
Retention	7 days	Extended retention
Capture	Enabled to ADLS Gen2	Automatic backup

Features:

Kafka protocol compatibility
Geo-disaster recovery
Virtual network integration
Managed identity authentication
Auto-inflate enabled

Configuration Example:

az eventhubs namespace create \
  --name realtime-events-prod \
  --resource-group realtime-analytics-rg \
  --location eastus \
  --sku Premium \
  --capacity 20 \
  --enable-auto-inflate true \
  --maximum-throughput-units 100

Confluent Kafka¶

Purpose: Enterprise-grade streaming platform for high-volume data

Specifications:

Component	Configuration	Purpose
Cluster Type	Dedicated	Isolated resources
Brokers	3-9 nodes	High availability
Storage	1-10 TB per broker	Message retention
Replication	Factor 3	Data durability
Availability Zones	Multi-AZ	Fault tolerance

Features:

Schema Registry integration
ksqlDB for stream processing
Cluster linking for geo-replication
Role-based access control
Confluent Control Center monitoring

Topics Configuration:

# Production topics
events.raw.v1
  partitions: 32
  replication.factor: 3
  retention.ms: 604800000  # 7 days

events.processed.v1
  partitions: 16
  replication.factor: 3
  retention.ms: 259200000  # 3 days

Azure Stream Analytics¶

Purpose: Real-time stream processing for simple transformations

Specifications:

Aspect	Value	Description
Streaming Units	6-120 (auto-scale)	Processing capacity
Compatibility Level	1.2	Latest features
Output Error Policy	Drop	Error handling
Event Ordering	Adjust	5-second tolerance

Use Cases:

IoT telemetry processing
Log aggregation and filtering
Real-time alerting
Stream-to-storage routing

Processing Components¶

Azure Databricks¶

Purpose: Unified analytics platform for batch and streaming

Workspace Configuration:

Component	Specification	Notes
Tier	Premium	Unity Catalog enabled
Runtime	13.3 LTS ML	Long-term support
Cluster Policy	Job, Interactive	Enforced policies
Network	VNet injection	Private networking
Storage	ADLS Gen2	Delta Lake storage

Cluster Configurations:

High Concurrency Cluster¶

{
  "cluster_name": "shared-analytics-cluster",
  "spark_version": "13.3.x-scala2.12",
  "node_type_id": "Standard_DS3_v2",
  "autoscale": {
    "min_workers": 2,
    "max_workers": 50
  },
  "spark_conf": {
    "spark.databricks.delta.preview.enabled": "true",
    "spark.databricks.delta.optimizeWrite.enabled": "true",
    "spark.databricks.delta.autoCompact.enabled": "true"
  },
  "enable_elastic_disk": true,
  "data_security_mode": "USER_ISOLATION"
}

Streaming Job Cluster¶

{
  "cluster_name": "streaming-processor",
  "spark_version": "13.3.x-scala2.12",
  "node_type_id": "Standard_L8s_v2",
  "num_workers": 12,
  "spark_conf": {
    "spark.streaming.backpressure.enabled": "true",
    "spark.databricks.delta.properties.defaults.autoOptimize.optimizeWrite": "true"
  },
  "spot_bid_price_percent": 100,
  "enable_local_disk_encryption": true
}

Delta Lake¶

Purpose: ACID-compliant data lake storage layer

Configuration:

Feature	Setting	Benefit
Auto Optimize	Enabled	Automatic file compaction
Optimize Write	Enabled	Better file sizes
Data Skipping	Enabled	Query performance
Z-Ordering	Business keys	Colocation optimization
Vacuum	7 days retention	Storage cleanup

Table Properties:

CREATE TABLE gold.customer_analytics (
  customer_id STRING NOT NULL,
  event_timestamp TIMESTAMP,
  metrics MAP<STRING, DOUBLE>,
  aggregations STRUCT<...>
)
USING DELTA
PARTITIONED BY (DATE(event_timestamp))
TBLPROPERTIES (
  'delta.autoOptimize.optimizeWrite' = 'true',
  'delta.autoOptimize.autoCompact' = 'true',
  'delta.enableChangeDataFeed' = 'true',
  'delta.deletedFileRetentionDuration' = 'interval 7 days'
);

Azure Data Factory¶

Purpose: Orchestration and ETL pipeline management

Components:

Component	Purpose	Count
Pipelines	Workflow orchestration	50+
Linked Services	Connection management	25+
Datasets	Data definitions	100+
Triggers	Schedule/event-based	30+
Integration Runtime	Compute environment	3

Pipeline Patterns:

Incremental data loading
Full refresh with validation
Change data capture (CDC)
Event-driven processing
Scheduled batch jobs

Storage Components¶

Azure Data Lake Storage Gen2¶

Purpose: Scalable, secure data lake storage

Configuration:

Feature	Setting	Purpose
Tier	Premium	Low latency
Replication	ZRS	Zone redundancy
Hierarchical Namespace	Enabled	Directory operations
Encryption	Customer-managed keys	Data security
Lifecycle Management	Hot/Cool/Archive	Cost optimization

Container Structure:

realtime-analytics/
├── bronze/              # Raw ingested data
│   ├── events/
│   ├── logs/
│   └── telemetry/
├── silver/              # Cleansed data
│   ├── validated/
│   ├── enriched/
│   └── deduplicated/
├── gold/                # Business-ready data
│   ├── aggregations/
│   ├── dimensions/
│   └── facts/
└── checkpoints/         # Streaming checkpoints
    └── streaming/

Lifecycle Policies:

{
  "rules": [
    {
      "name": "MoveBronzeToCool",
      "type": "Lifecycle",
      "definition": {
        "actions": {
          "baseBlob": {
            "tierToCool": {
              "daysAfterModificationGreaterThan": 30
            },
            "tierToArchive": {
              "daysAfterModificationGreaterThan": 90
            }
          }
        },
        "filters": {
          "blobTypes": ["blockBlob"],
          "prefixMatch": ["bronze/"]
        }
      }
    }
  ]
}

Unity Catalog¶

Purpose: Unified governance for data and AI assets

Hierarchy:

Catalog: realtime_analytics
├── Schema: bronze
│   ├── Tables: events, logs, telemetry
│   └── Volumes: raw_files
├── Schema: silver
│   ├── Tables: validated_events, enriched_data
│   └── Views: latest_events
└── Schema: gold
    ├── Tables: customer_metrics, product_analytics
    ├── Views: executive_dashboard
    └── Functions: calculate_metrics()

Security Model:

-- Grant access to data engineers
GRANT USE CATALOG ON CATALOG realtime_analytics TO `data-engineers`;
GRANT USE SCHEMA, SELECT, MODIFY ON SCHEMA realtime_analytics.silver TO `data-engineers`;

-- Grant read-only access to analysts
GRANT USE CATALOG ON CATALOG realtime_analytics TO `analysts`;
GRANT USE SCHEMA, SELECT ON SCHEMA realtime_analytics.gold TO `analysts`;

Analytics Components¶

Azure OpenAI¶

Purpose: AI-powered analytics and insights

Deployments:

Model	Version	Purpose	RPM Limit
GPT-4	0125-Preview	Advanced reasoning	10K
GPT-3.5-Turbo	0125	High throughput	60K
Text-Embedding-3-Large	3.0	Vector search	100K

Use Cases:

Natural language query generation
Automated data insights
Anomaly explanation
Report summarization
Predictive analytics enhancement

Integration Example:

from openai import AzureOpenAI

client = AzureOpenAI(
    azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
    api_key=os.getenv("AZURE_OPENAI_API_KEY"),
    api_version="2024-02-15-preview"
)

# Generate SQL from natural language
response = client.chat.completions.create(
    model="gpt-4",
    messages=[{
        "role": "system",
        "content": "Generate SQL for Azure Databricks Delta tables"
    }, {
        "role": "user",
        "content": "Show top 10 customers by revenue last month"
    }]
)

MLflow¶

Purpose: Machine learning lifecycle management

Components:

Component	Purpose	Storage
Tracking Server	Experiment tracking	Azure SQL
Model Registry	Model versioning	ADLS Gen2
Artifacts Store	Model artifacts	ADLS Gen2

Model Management:

import mlflow
from mlflow.tracking import MlflowClient

# Configure MLflow
mlflow.set_tracking_uri("databricks")
mlflow.set_registry_uri("databricks-uc")

# Log experiment
with mlflow.start_run(run_name="customer_churn_v3"):
    mlflow.log_params({"max_depth": 10, "learning_rate": 0.01})
    mlflow.log_metrics({"accuracy": 0.94, "f1_score": 0.92})
    mlflow.sklearn.log_model(model, "model",
                             registered_model_name="customer_churn")

Power BI¶

Purpose: Business intelligence and visualization

Configuration:

Component	Specification	Purpose
Capacity	P1 (2 cores)	Premium features
Mode	Direct Lake	Real-time queries
Gateway	Not required	Direct connection
Refresh	Real-time	Live data

Features:

Direct Lake from Delta tables
Composite models
Incremental refresh
Query folding
RLS (Row-Level Security)

Governance Components¶

Azure Purview¶

Purpose: Data governance and discovery

Features:

Feature	Status	Purpose
Data Catalog	✅ Active	Asset discovery
Data Lineage	✅ Active	Impact analysis
Data Classification	✅ Active	Sensitivity labeling
Scanning	✅ Automated	Metadata extraction

Scanned Sources:

Azure Data Lake Storage Gen2
Azure Databricks (Unity Catalog)
Azure SQL Database
Power BI datasets

Azure Policy¶

Purpose: Governance and compliance enforcement

Policies:

{
  "policyName": "Require-Private-Endpoints",
  "description": "Enforce private endpoints for all data services",
  "effect": "Deny",
  "resources": [
    "Microsoft.Storage/storageAccounts",
    "Microsoft.Databricks/workspaces",
    "Microsoft.EventHub/namespaces"
  ]
}

Security Components¶

Azure Key Vault¶

Purpose: Secrets and certificate management

Stored Secrets:

Database connection strings
Service principal credentials
API keys and tokens
Encryption keys
SSL certificates

Access Policies:

# Grant Databricks access to secrets
az keyvault set-policy \
  --name realtime-kv-prod \
  --object-id <databricks-msi> \
  --secret-permissions get list

Azure Private Link¶

Purpose: Private network connectivity

Endpoints:

Service	Endpoint Type	Purpose
ADLS Gen2	Private	Storage access
Event Hubs	Private	Event streaming
Key Vault	Private	Secret access
Databricks	Private	Workspace access

Monitoring Components¶

Azure Monitor¶

Purpose: Platform monitoring and alerting

Components:

Application Insights (application telemetry)
Log Analytics (centralized logging)
Metrics (performance monitoring)
Alerts (proactive notifications)
Workbooks (custom dashboards)

Key Metrics:

// Event Hubs throughput
AzureMetrics
| where ResourceProvider == "MICROSOFT.EVENTHUB"
| where MetricName == "IncomingMessages"
| summarize Total = sum(Total) by bin(TimeGenerated, 5m)

Datadog¶

Purpose: Advanced APM and infrastructure monitoring

Integrations:

Azure Monitor integration
Databricks metrics
Custom application metrics
Synthetic monitoring
Real user monitoring (RUM)

Component Dependencies¶

Dependency Matrix¶

graph TB
    subgraph Ingestion["Ingestion Layer"]
        EH[Event Hubs]
        K[Kafka]
    end

    subgraph Processing["Processing Layer"]
        DB[Databricks]
        ADF[Data Factory]
    end

    subgraph Storage["Storage Layer"]
        ADLS[ADLS Gen2]
        DL[Delta Lake]
        UC[Unity Catalog]
    end

    subgraph Analytics["Analytics Layer"]
        ML[MLflow]
        AI[Azure OpenAI]
        PBI[Power BI]
    end

    subgraph Security["Security Layer"]
        KV[Key Vault]
        AAD[Azure AD]
        PL[Private Link]
    end

    EH --> DB
    K --> DB
    DB --> ADLS
    DB --> DL
    DL --> ADLS
    DB --> ML
    DB --> AI
    DL --> PBI
    UC --> DB
    KV --> DB
    KV --> ADF
    AAD --> DB
    AAD --> PBI
    PL --> ADLS
    PL --> EH

Critical Path¶

Data Ingestion: Event Hubs/Kafka → Databricks
Processing: Databricks → Delta Lake → ADLS Gen2
Governance: Unity Catalog → Access Control
Analytics: Power BI → Direct Lake → Delta Tables
Security: Azure AD → Key Vault → Private Link

Component Sizing Guide¶

Small Deployment (Dev/Test)¶

Component	Size	Monthly Cost
Event Hubs	Basic, 2 TU	$50
Databricks	Standard, 2 workers	$500
ADLS Gen2	100 GB	$5
Total		~$600

Medium Deployment (Production)¶

Component	Size	Monthly Cost
Event Hubs	Premium, 20 TU	$2,000
Databricks	Premium, 10 workers avg	$5,000
ADLS Gen2	10 TB	$500
Power BI	P1 capacity	$5,000
Total		~$13,000

Large Deployment (Enterprise)¶

Component	Size	Monthly Cost
Event Hubs	Premium, 100 TU	$10,000
Databricks	Premium, 50 workers avg	$25,000
ADLS Gen2	100 TB	$2,500
Power BI	P3 capacity	$20,000
Total		~$60,000

Last Updated: January 2025 Version: 1.0.0 Status: Production Ready

Solution Components Overview¶

Overview¶

Table of Contents¶

Data Ingestion Components¶

Azure Event Hubs¶

Confluent Kafka¶

Azure Stream Analytics¶

Processing Components¶

Azure Databricks¶

High Concurrency Cluster¶

Streaming Job Cluster¶

Delta Lake¶

Azure Data Factory¶

Storage Components¶

Azure Data Lake Storage Gen2¶

Unity Catalog¶

Analytics Components¶

Azure OpenAI¶

MLflow¶

Power BI¶

Governance Components¶

Azure Purview¶

Azure Policy¶

Security Components¶

Azure Key Vault¶

Azure Private Link¶

Monitoring Components¶

Azure Monitor¶

Datadog¶

Component Dependencies¶

Dependency Matrix¶

Critical Path¶

Component Sizing Guide¶

Small Deployment (Dev/Test)¶

Medium Deployment (Production)¶

Large Deployment (Enterprise)¶

Related Documentation¶