Azure Storage Services

This skill covers working with Azure Storage services including Blob, Table, Queue, and File storage for various data persistence scenarios.

When to use this skill

Use this skill when you need to:

• Store files and unstructured data (Blob)
• Build NoSQL data stores (Table)
• Implement asynchronous messaging (Queue)
• Share files across VMs (File)
• Design data archival strategies

Prerequisites

• Azure subscription with storage account
• Storage account access keys or SAS tokens
• Azure SDK for your programming language
• Understanding of storage redundancy options (LRS, GRS, ZRS)

Guidelines

Storage Account Configuration

Naming

• Globally unique, lowercase, alphanumeric only
• 3-24 characters (e.g., mycorpprodstorage)

Performance Tiers

• Standard: HDD-backed, cost-effective for most workloads
• Premium: SSD-backed, low latency for intensive applications

Access Tiers

• Hot: Frequently accessed data (higher storage, lower access cost)
• Cool: Infrequently accessed (30+ days retention)
• Archive: Rarely accessed (180+ days, retrieval latency hours)

Blob Storage

Container Organization

mycontainer/
├── uploads/           # User uploaded files
├── processed/         # Post-processing results
├── archived/          # Old data (Archive tier)
└── temp/              # Temporary files (lifecycle policy to delete)

SDK Usage (Python example)

from azure.storage.blob import BlobServiceClient

blob_service = BlobServiceClient.from_connection_string(conn_str)
container = blob_service.get_container_client("mycontainer")

# Upload
with open("file.txt", "rb") as f:
    container.upload_blob("path/file.txt", f, overwrite=True)

# Download
blob = container.get_blob_client("path/file.txt")
with open("downloaded.txt", "wb") as f:
    f.write(blob.download_blob().readall())

Security

• Use SAS tokens with expiration for temporary access
• Enable soft delete for blob recovery
• Use private endpoints for internal access
• Enable blob versioning for audit trails

Table Storage

Entity Design

• PartitionKey: Group related entities for query efficiency
• RowKey: Unique identifier within partition
• Keep entity size under 1MB

from azure.data.tables import TableClient

table = TableClient.from_connection_string(conn_str, "mytable")

entity = {
    "PartitionKey": "User",
    "RowKey": "user123",
    "Name": "John Doe",
    "Email": "john@example.com"
}
table.create_entity(entity)

Queue Storage

Message Processing Pattern

from azure.storage.queue import QueueClient

queue = QueueClient.from_connection_string(conn_str, "myqueue")

# Send message
queue.send_message(json.dumps({"task": "process", "id": 123}))

# Receive and process
messages = queue.receive_messages(max_messages=32)
for msg in messages:
    try:
        data = json.loads(msg.content)
        process_task(data)
        queue.delete_message(msg)
    except Exception:
        # Message will become visible again after visibility timeout
        pass

Best Practices

Performance

• Use block blobs for files > 100MB
• Enable CDN for frequently accessed static content
• Use batch operations for multiple entities
• Implement exponential backoff for retries

Cost Optimization

• Implement lifecycle policies for tier transitions
• Delete temporary data automatically
• Use Premium tier only when latency is critical
• Monitor storage analytics for optimization opportunities

Data Protection

• Enable point-in-time restore for blobs
• Use immutable storage for compliance
• Encrypt data at rest and in transit
• Regular backup testing and validation

Examples

See the examples/ directory for:

• blob-upload.py - File upload implementation
• table-crud.py - Table storage operations
• queue-processor.py - Message queue consumer
• lifecycle-policy.json - Storage lifecycle rules

References

• Azure Storage documentation
• Blob storage SDK
• Table storage SDK
• Queue storage SDK