> 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/9.3-data-integration/pdi-job-entries-reference-overview/google-bigquery-loader/before-you-begin.md).

# Before you begin

You must have a Google account and must create service account credentials in the form of a key file in JSON format to use the Google BigQuery loader job entry. You must also set permissions for your BigQuery and Google Cloud accounts. To create service account credentials, see <https://cloud.google.com/storage/docs/authentication>.

Perform the following steps to set up your system to use Google BigQuery:

1. Download the service account credentials file that you have created using the Google API Console to your local machine.
2. Create a new system environmental variable on your operating system named **GOOGLE\_APPLICATION\_CREDENTIALS**.
3. Set the path to the downloaded JSON service account credentials file as the value of the **GOOGLE\_APPLICATION\_CREDENTIALS** variable.
4. Reboot your local machine.

**Note:** The environment variable and credentials must be set up on each machine that runs the BigQuery loader job. The Google BigQuery loader dialog box will not open for editing in the job canvas until this procedure is completed.


---

# 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/9.3-data-integration/pdi-job-entries-reference-overview/google-bigquery-loader/before-you-begin.md?ask=<question>&goal=<user_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.
