azure-eventhub-py
Azure Event Hubs SDK for Python streaming. Use for high-throughput event ingestion, producers, consumers, and checkpointing. Triggers: "event hubs", "EventHubProducerClient", "EventHubConsumerClient", "streaming", "partitions".
Best use case
azure-eventhub-py is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. Azure Event Hubs SDK for Python streaming. Use for high-throughput event ingestion, producers, consumers, and checkpointing. Triggers: "event hubs", "EventHubProducerClient", "EventHubConsumerClient", "streaming", "partitions".
Azure Event Hubs SDK for Python streaming. Use for high-throughput event ingestion, producers, consumers, and checkpointing. Triggers: "event hubs", "EventHubProducerClient", "EventHubConsumerClient", "streaming", "partitions".
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "azure-eventhub-py" skill to help with this workflow task. Context: Azure Event Hubs SDK for Python streaming. Use for high-throughput event ingestion, producers, consumers, and checkpointing. Triggers: "event hubs", "EventHubProducerClient", "EventHubConsumerClient", "streaming", "partitions".
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/azure-eventhub-py/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How azure-eventhub-py Compares
| Feature / Agent | azure-eventhub-py | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Azure Event Hubs SDK for Python streaming. Use for high-throughput event ingestion, producers, consumers, and checkpointing. Triggers: "event hubs", "EventHubProducerClient", "EventHubConsumerClient", "streaming", "partitions".
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Azure Event Hubs SDK for Python
Big data streaming platform for high-throughput event ingestion.
## Installation
```bash
pip install azure-eventhub azure-identity
# For checkpointing with blob storage
pip install azure-eventhub-checkpointstoreblob-aio
```
## Environment Variables
```bash
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
```python
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="$Default",
credential=credential
)
```
## Client Types
| Client | Purpose |
|--------|---------|
| `EventHubProducerClient` | Send events to Event Hub |
| `EventHubConsumerClient` | Receive events from Event Hub |
| `BlobCheckpointStore` | Track consumer progress |
## Send Events
```python
from azure.eventhub import EventHubProducerClient, EventData
from azure.identity import DefaultAzureCredential
producer = EventHubProducerClient(
fully_qualified_namespace="<namespace>.servicebus.windows.net",
eventhub_name="my-eventhub",
credential=DefaultAzureCredential()
)
with producer:
# Create batch (handles size limits)
event_data_batch = producer.create_batch()
for i in range(10):
try:
event_data_batch.add(EventData(f"Event {i}"))
except ValueError:
# Batch is full, send and create new one
producer.send_batch(event_data_batch)
event_data_batch = producer.create_batch()
event_data_batch.add(EventData(f"Event {i}"))
# Send remaining
producer.send_batch(event_data_batch)
```
### Send to Specific Partition
```python
# By partition ID
event_data_batch = producer.create_batch(partition_id="0")
# By partition key (consistent hashing)
event_data_batch = producer.create_batch(partition_key="user-123")
```
## Receive Events
### Simple Receive
```python
from azure.eventhub import EventHubConsumerClient
def on_event(partition_context, event):
print(f"Partition: {partition_context.partition_id}")
print(f"Data: {event.body_as_str()}")
partition_context.update_checkpoint(event)
consumer = EventHubConsumerClient(
fully_qualified_namespace="<namespace>.servicebus.windows.net",
eventhub_name="my-eventhub",
consumer_group="$Default",
credential=DefaultAzureCredential()
)
with consumer:
consumer.receive(
on_event=on_event,
starting_position="-1", # Beginning of stream
)
```
### With Blob Checkpoint Store (Production)
```python
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="$Default",
credential=DefaultAzureCredential(),
checkpoint_store=checkpoint_store
)
def on_event(partition_context, event):
print(f"Received: {event.body_as_str()}")
# Checkpoint after processing
partition_context.update_checkpoint(event)
with consumer:
consumer.receive(on_event=on_event)
```
## Async Client
```python
from azure.eventhub.aio import EventHubProducerClient, EventHubConsumerClient
from azure.identity.aio import DefaultAzureCredential
import asyncio
async def send_events():
credential = DefaultAzureCredential()
async with EventHubProducerClient(
fully_qualified_namespace="<namespace>.servicebus.windows.net",
eventhub_name="my-eventhub",
credential=credential
) as producer:
batch = await producer.create_batch()
batch.add(EventData("Async event"))
await producer.send_batch(batch)
async def receive_events():
async def on_event(partition_context, event):
print(event.body_as_str())
await partition_context.update_checkpoint(event)
async with EventHubConsumerClient(
fully_qualified_namespace="<namespace>.servicebus.windows.net",
eventhub_name="my-eventhub",
consumer_group="$Default",
credential=DefaultAzureCredential()
) as consumer:
await consumer.receive(on_event=on_event)
asyncio.run(send_events())
```
## Event Properties
```python
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
```python
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
| File | Contents |
|------|----------|
| [references/checkpointing.md](references/checkpointing.md) | Checkpoint store patterns, blob checkpointing, checkpoint strategies |
| [references/partitions.md](references/partitions.md) | Partition management, load balancing, starting positions |
| [scripts/setup_consumer.py](scripts/setup_consumer.py) | CLI for Event Hub info, consumer setup, and event sending/receiving |Related Skills
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