Azure Event Hubs SDK for Python

Azure Event Hubs SDK for Python

Big data streaming platform for high-throughput event ingestion.

Installation

pip install azure-eventhub azure-identity
# For checkpointing with blob storage
pip install azure-eventhub-checkpointstoreblob-aio

Environment Variables

EVENT_HUB_FULLY_QUALIFIED_NAMESPACE=<namespace>.servicebus.windows.net
EVENT_HUB_NAME=my-eventhub
STORAGE_ACCOUNT_URL=https://<account>.blob.core.windows.net
CHECKPOINT_CONTAINER=checkpoints

Authentication

from azure.identity import DefaultAzureCredential
from azure.eventhub import EventHubProducerClient, EventHubConsumerClient

credential = DefaultAzureCredential()
namespace = "<namespace>.servicebus.windows.net"
eventhub_name = "my-eventhub"

# Producer
producer = EventHubProducerClient(
    fully_qualified_namespace=namespace,
    eventhub_name=eventhub_name,
    credential=credential
)

# Consumer
consumer = EventHubConsumerClient(
    fully_qualified_namespace=namespace,
    eventhub_name=eventhub_name,
    consumer_group="$Default",
    credential=credential
)

Client Types

Send Events

from azure.eventhub import EventHubProducerClient, EventData
from azure.identity import DefaultAzureCredential

producer = EventHubProducerClient(
    fully_qualified_namespace="<namespace>.servicebus.windows.net",
    eventhub_name="my-eventhub",
    credential=DefaultAzureCredential()
)

with producer:
    # Create batch (handles size limits)
    event_data_batch = producer.create_batch()
    
    for i in range(10):
        try:
            event_data_batch.add(EventData(f"Event {i}"))
        except ValueError:
            # Batch is full, send and create new one
            producer.send_batch(event_data_batch)
            event_data_batch = producer.create_batch()
            event_data_batch.add(EventData(f"Event {i}"))
    
    # Send remaining
    producer.send_batch(event_data_batch)

Send to Specific Partition

# By partition ID
event_data_batch = producer.create_batch(partition_id="0")

# By partition key (consistent hashing)
event_data_batch = producer.create_batch(partition_key="user-123")

Receive Events

Simple Receive

from azure.eventhub import EventHubConsumerClient

def on_event(partition_context, event):
    print(f"Partition: {partition_context.partition_id}")
    print(f"Data: {event.body_as_str()}")
    partition_context.update_checkpoint(event)

consumer = EventHubConsumerClient(
    fully_qualified_namespace="<namespace>.servicebus.windows.net",
    eventhub_name="my-eventhub",
    consumer_group="$Default",
    credential=DefaultAzureCredential()
)

with consumer:
    consumer.receive(
        on_event=on_event,
        starting_position="-1",  # Beginning of stream
    )

With Blob Checkpoint Store (Production)

from azure.eventhub import EventHubConsumerClient
from azure.eventhub.extensions.checkpointstoreblob import BlobCheckpointStore
from azure.identity import DefaultAzureCredential

checkpoint_store = BlobCheckpointStore(
    blob_account_url="https://<account>.blob.core.windows.net",
    container_name="checkpoints",
    credential=DefaultAzureCredential()
)

consumer = EventHubConsumerClient(
    fully_qualified_namespace="<namespace>.servicebus.windows.net",
    eventhub_name="my-eventhub",
    consumer_group="$Default",
    credential=DefaultAzureCredential(),
    checkpoint_store=checkpoint_store
)

def on_event(partition_context, event):
    print(f"Received: {event.body_as_str()}")
    # Checkpoint after processing
    partition_context.update_checkpoint(event)

with consumer:
    consumer.receive(on_event=on_event)

Async Client

from azure.eventhub.aio import EventHubProducerClient, EventHubConsumerClient
from azure.identity.aio import DefaultAzureCredential
import asyncio

async def send_events():
    credential = DefaultAzureCredential()
    
    async with EventHubProducerClient(
        fully_qualified_namespace="<namespace>.servicebus.windows.net",
        eventhub_name="my-eventhub",
        credential=credential
    ) as producer:
        batch = await producer.create_batch()
        batch.add(EventData("Async event"))
        await producer.send_batch(batch)

async def receive_events():
    async def on_event(partition_context, event):
        print(event.body_as_str())
        await partition_context.update_checkpoint(event)
    
    async with EventHubConsumerClient(
        fully_qualified_namespace="<namespace>.servicebus.windows.net",
        eventhub_name="my-eventhub",
        consumer_group="$Default",
        credential=DefaultAzureCredential()
    ) as consumer:
        await consumer.receive(on_event=on_event)

asyncio.run(send_events())

Event Properties

event = EventData("My event body")

# Set properties
event.properties = {"custom_property": "value"}
event.content_type = "application/​json"

# Read properties (on receive)
print(event.body_as_str())
print(event.sequence_number)
print(event.offset)
print(event.enqueued_time)
print(event.partition_key)

Get Event Hub Info

with producer:
    info = producer.get_eventhub_properties()
    print(f"Name: {info['name']}")
    print(f"Partitions: {info['partition_ids']}")
    
    for partition_id in info['partition_ids']:
        partition_info = producer.get_partition_properties(partition_id)
        print(f"Partition {partition_id}: {partition_info['last_enqueued_sequence_number']}")

Best Practices

1. Use batches for sending multiple events
2. Use checkpoint store in production for reliable processing
3. Use async client for high-throughput scenarios
4. Use partition keys for ordered delivery within a partition
5. Handle batch size limits — catch ValueError when batch is full
6. Use context managers (with/async with) for proper cleanup
7. Set appropriate consumer groups for different applications

Reference Files

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

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AGI Framework Integration

Adapted for @techwavedev/​agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Retrieve prior deployment configurations, rollback procedures, and incident post-mortems. Avoid re-discovering infrastructure patterns.

# Check for prior infrastructure context before starting
python3 execution/​memory_manager.py auto --query "deployment configuration and patterns for Azure Eventhub Py"

Storing Results

After completing work, store infrastructure decisions for future sessions:

python3 execution/​memory_manager.py store \
  --content "Deployment pipeline: configured blue-green deployment with health checks on port 8080" \
  --type technical --project <project> \
  --tags azure-eventhub-py devops

Multi-Agent Collaboration

Broadcast deployment changes so frontend and backend agents update their configurations accordingly.

python3 execution/​cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Deployed infrastructure changes — updated CI/​CD pipeline with new health check endpoints" \
  --project <project>

Playbook Integration

Use the ship-saas-mvp or full-stack-deploy playbook to sequence this skill with testing, documentation, and deployment verification.

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