> For the complete documentation index, see [llms.txt](https://academy.pentaho.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://academy.pentaho.com/pentaho-data-integration/use-cases.md).

# Use Cases

- [AI-PDI Pipelines](https://academy.pentaho.com/pentaho-data-integration/use-cases/ai-pdi-pipelines.md): Key concepts behind interacting with a LLM - Chatbot ..
- [GenAI](https://academy.pentaho.com/pentaho-data-integration/use-cases/ai-pdi-pipelines/genai.md): Generative artificial intelligence (GenAI) can create certain types of images, text, videos, and other media in response to prompts ..
- [LLM Integration](https://academy.pentaho.com/pentaho-data-integration/use-cases/ai-pdi-pipelines/llm-integration.md): Integrate LLMs into your pipelines ..
- [LangExtract](https://academy.pentaho.com/pentaho-data-integration/use-cases/ai-pdi-pipelines/langextract.md): Extract structured fields from unstructured text with LangExtract and load them with PDI.
- [Agent-as-a-Service](https://academy.pentaho.com/pentaho-data-integration/use-cases/ai-pdi-pipelines/agent-as-a-service.md): Pattern reasoning across unstructured data ..
- [Machine Learning](https://academy.pentaho.com/pentaho-data-integration/use-cases/machine-learning.md): Algorithms .. Credit Card Fraud
- [Prerequisite tasks](https://academy.pentaho.com/pentaho-data-integration/use-cases/machine-learning/prerequiste-tasks.md): Configure Google Colab and Pentaho Data Integration (PDI) for machine learning.
- [AutoML](https://academy.pentaho.com/pentaho-data-integration/use-cases/machine-learning/automl.md): Use PDI + H2O AutoML in Colab to prototype a credit-card fraud model.
- [Credit Card](https://academy.pentaho.com/pentaho-data-integration/use-cases/machine-learning/credit-card.md): ML - Gradient Boosting (GBM)
- [RESTful API](https://academy.pentaho.com/pentaho-data-integration/use-cases/restful-api.md): Using weather REST API ..
- [Streaming Data](https://academy.pentaho.com/pentaho-data-integration/use-cases/streaming-data.md): Streaming data from IOT devices ..
- [MQTT](https://academy.pentaho.com/pentaho-data-integration/use-cases/streaming-data/mqtt.md): Publish / Subscribe
- [EMQX](https://academy.pentaho.com/pentaho-data-integration/use-cases/streaming-data/mqtt/mosquitto.md): Use Case: Predictive maintenance
- [HiveMQ](https://academy.pentaho.com/pentaho-data-integration/use-cases/streaming-data/mqtt/hivemq.md): Use Case: Manufacturing ..
- [AMQP](https://academy.pentaho.com/pentaho-data-integration/use-cases/streaming-data/amqp.md): Advanced Message Queuing Protocol (AMQP) is an open source published standard for asynchronous messaging by wire ..
- [RabbitMQ](https://academy.pentaho.com/pentaho-data-integration/use-cases/streaming-data/amqp/rabbitmq.md): Allows for messages to be queued, received, and delivered asynchronously ..
- [Kafka](https://academy.pentaho.com/pentaho-data-integration/use-cases/streaming-data/kafka.md)
- [Use Cases](https://academy.pentaho.com/pentaho-data-integration/use-cases/streaming-data/kafka/use-cases.md)
- [Jenkins](https://academy.pentaho.com/pentaho-data-integration/use-cases/jenkins.md): Used as an open-source scheduler ..
- [Data Lineage](https://academy.pentaho.com/pentaho-data-integration/use-cases/data-lineage.md): Automate pipelines with Apache Airflow ..


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://academy.pentaho.com/pentaho-data-integration/use-cases.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.