> For the complete documentation index, see [llms.txt](https://docs.pentaho.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.pentaho.com/pdia-data-integration/10.2-data-integration/pdi-job-entries-reference-overview/pentaho-mapreduce/use-pdi-outside-and-inside-the-hadoop-cluster/big-data-tutorials.md). # Big data tutorials Pentaho big data tutorials provide examples of how you can use Pentaho technology as part of your overall big data strategy. Each section is a series of scenario-based tutorials that demonstrate the integration between Pentaho and Hadoop using a sample data set. The following videos help demonstrate using PDI to work with Hadoop from both inside and outside a Hadoop cluster. * Loading data into Hadoop from outside the Hadoop cluster is a 5-minute video that demonstrates moving data using a PDI job and transformation: * Use [Pentaho MapReduce](/pdia-data-integration/10.2-data-integration/pdi-job-entries-reference-overview/pentaho-mapreduce.md) to interactively design a data flow for a MapReduce job without writing scripts or code. Here is a 12 minute video that provides an overview of the process: --- # 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 by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.pentaho.com/pdia-data-integration/10.2-data-integration/pdi-job-entries-reference-overview/pentaho-mapreduce/use-pdi-outside-and-inside-the-hadoop-cluster/big-data-tutorials.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. 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.