# Event processing with HTTPS listeners

HTTPS listeners write events to a [stream](../../data-integration/streams/index.md). You can locate this stream by navigating to your listener's **Overview** page in Data Connection. Once your data resides in a stream, several processing options are available.

## Stream processing with Automate

Streams can be directly processed in [Automate](../../automate/streaming/index.md#automation-on-streaming-datasets), enabling you to execute an action or function for each inbound event. You can create objects in your ontology, run AIP logic, or use [sources](../set-up-source/index.md) in functions to interact with external systems, such as writing data back to the system that sent the event.

![Option to process streams in Automate.](../../../assets/a621706dbd9b788fdf7edabacdd8017483dd7f0e7ce04eed51cd9658a2829e29.webp)

Processing streaming events with Automate is appropriate when:

* The event stream is not high throughput
* Your event processing is stateless
* Latency of a few seconds is acceptable
* At-least-once processing is sufficient

This approach is suitable for most listener integrations, providing low-cost, low-maintenance event processing workflows.

You can learn more about processing listener events with Automate in the [guide to creating an AI-powered chatbot with listeners](../listeners-slack-bot/index.md).

## Streaming pipelines

Listener event streams can be processed with [streaming pipelines](../../building-pipelines/streaming-overview/index.md) as a lower-latency alternative. These pipelines support high-throughput streams, [stateful event processing](../../building-pipelines/streaming-stateful-transforms/index.md), and (optionally) exactly-once event processing. Streaming pipelines can additionally [leverage UDFs](../../functions/python-functions-builder/index.md) to build powerful real-time event-processing workflows.

![Build streaming pipelines in Pipeline Builder.](../../../assets/d98d9c429711b95ad0462688f703f74b2e2485de422c6c43d6dc021c39d4aedc.webp)

## Batch pipelines

Every few minutes, the listener event stream will [archive into a backing dataset](../../data-integration/streams/index.md#cold-buffer). This dataset can be used like any other dataset in the platform, allowing you to build [data pipelines](../../data-integration/data-pipeline/index.md) and [back your ontology](../../object-link-types/create-object-type/index.md#create-a-new-object-type).

![Switch to archive mode on your event stream to access the underlying dataset.](../../../assets/c68e5fcdafa159f38fa0ff4321d688debd53934109945d26c583148a7c18fdb3.webp)

For use cases that require historical analysis or that are not real-time, batch pipelines should be used.