> ## Documentation Index > Fetch the complete documentation index at: https://arize-ax.mintlify.dev/docs/llms.txt > Use this file to discover all available pages before exploring further. # Google BigQuery > Learn how to setup an import job using Google BigQuery ### Step 1 - Start the Data Upload Wizard Navigate to the 'Upload Data' page on the left navigation bar in the Arize platform. From there, select the 'Google BQ' card or navigate to the Data Warehouse tab to start a new table import job to begin **a new table import job.** **Storage Selection: Google BQ**

### Step 2 - Input the Project ID, Dataset, and Table / View Locate the Project ID, Dataset, and Table *or* View name of the table/view you would like to sync from Google BigQuery. * The GBQ Project ID is a unique identifier for a project. See [here](https://cloud.google.com/resource-manager/docs/creating-managing-projects#identifying_projects) for steps on how to retrieve this ID. * The dataset and table name correspond to the path where your table is located

Add your Table ID Arize. Arize will automatically parse your Dataset, Table Name, and GCP Project ID.

### Step 3 - Grant Access To Your Dataset, Table, or View Tag your dataset/table/view with the `arize-ingestion-key` and the provided label value using the steps below. For more details, see docs on [Adding labels to resources](https://cloud.google.com/bigquery/docs/adding-labels) for BigQuery. \*\*In Arize UI: \*\*Copy `arize-ingestion-key` value

You can grant access to [**a single table or view**](/ax/machine-learning/machine-learning/integrations-ml/google-bigquery#grant-access-to-a-table-view)**or **[**all the tables/views in a dataset**](/ax/machine-learning/machine-learning/integrations-ml/google-bigquery#grant-access-to-an-entire-dataset)**.** Consider creating an [authorized view](https://cloud.google.com/bigquery/docs/authorized-views) if you don't want to grant access to the underlying tables, or granting access to each underlying table is too cumbersome. [**Grant Access To A Table/View**](/ax/machine-learning/machine-learning/integrations-ml/google-bigquery#grant-access-to-a-table-view) [**Grant Access To An Entire Dataset**](/ax/machine-learning/machine-learning/integrations-ml/google-bigquery#grant-access-to-an-entire-dataset) #### Grant Access To A Table/View 1. **In Google Cloud console**: Navigate to the BigQuery SQL Workspace

2. Select the desired table or view, navigate to the **Details** tab and click "Edit Details". Under the **Labels** section, click "Add Labels". Add the following label: * Key as "**arize-ingestion-key**\*\*\*"\*\*\* * Value\* *as the*\*\* ***arize-ingestion-key*** value \*\*\*from the Arize UI

3. Grant the `roles/bigquery.jobUser` role to our service account. Go to the IAM page and click "Grant Access"

Add Arize service account as "Principal" with "BigQuery Job User" role * Navigate to your table/view from the Bigquery SQL Explorer page. * Select "Share" and click on "Permissions"

* Click "Add Principal"

* Add our service account: `fileimporter@production-269901.iam.gserviceaccount.com` as a BigQuery Data Viewer, and click "Save"

* For a view, you must grant access to \*\*all underlying tables, \*\*so you must repeat these step for all the underlying tables. For more details: see the official documentation for granting access [here](https://cloud.google.com/bigquery/docs/control-access-to-resources-iam#grant_access_to_a_table_or_view) 4. [Navigate to Step 4 - Configure your model and define your table’s schema](/ax/machine-learning/machine-learning/integrations-ml/google-bigquery#step-4-configure-your-model-and-define-your-tables-schema) You can create a cloud shell instance from the UI to run the following commands

1. Add the `arize-ingestion-key` key from the Arize UI as a label on the dataset ```ruby theme={null} bq update --set_label arize-ingestion-key:${KEY_FROM_UI} ${PROJECT_ID}:${DATASET} ``` 2. Grant the `roles/bigquery.jobUser` role to the Arize service account. ```ruby theme={null} gcloud projects add-iam-policy-binding ${PROJECT_ID} --member=serviceAccount:fileimporter@production-269901.iam.gserviceaccount.com --role=roles/bigquery.jobUser ``` 3. To grant the `roles/bigquery.dataViewer` role to the Arize service account your table or view * Table: ```powershell theme={null} bq add-iam-policy-binding \ --member='serviceAccount:fileimporter@production-269901.iam.gserviceaccount.com' \ --role='roles/bigquery.dataViewer' \ ${PROJECT_ID}:${DATASET}.${TABLE} ``` * View: See the Google BigQuery [guide to grant access to a view](https://cloud.google.com/bigquery/docs/authorized-views#authorize_a_view) and navigate to the tab `bq`. 4. [Navigate to Step 4 - Configure your model and define your table’s schema](/ax/machine-learning/machine-learning/integrations-ml/google-bigquery#step-4-configure-your-model-and-define-your-tables-schema) #### Grant Access To An Entire Dataset: 1. **In Google Cloud console**: Navigate to the BigQuery SQL Workspace

2. Select the desired dataset, and click "Edit Details". Under the **Labels** section, click "Add Labels". Add the following label: * Key as "**arize-ingestion-key**\*\*\*"\*\*\* * Value\* *as the*\*\* ***arize-ingestion-key*** *****value***** \*\*\*copied from the Arize UI *

3. Grant the `roles/bigquery.jobUser` role to the Arize service account. Go to the IAM page and click "Grant Access"

* Navigate to your dataset from the Bigquery SQL Explorer page. * Select "Sharing" and click on "Permissions"

* Click "Add Principal"

* Add Arize service account: `fileimporter@production-269901.iam.gserviceaccount.com` as a BigQuery Data Viewer, and click "Save"

For additional details: see the official documentation for granting access [here](https://cloud.google.com/bigquery/docs/control-access-to-resources-iam#grant_access_to_a_dataset) 4. [Navigate to Step 4 - Configure your model and define your table’s schema](/ax/machine-learning/machine-learning/integrations-ml/google-bigquery#step-4-configure-your-model-and-define-your-tables-schema) You can create a cloud shell instance from the UI to run the following commands

1. Add the `arize-ingestion-key` key from the Arize UI as a label on the dataset ```ruby theme={null} bq update --set_label arize-ingestion-key:${KEY_FROM_UI} ${PROJECT_ID}:${DATASET} ``` 2. Grant the `roles/bigquery.jobUser` role to the Arize service account. ```ruby theme={null} gcloud projects add-iam-policy-binding ${PROJECT_ID} --member=serviceAccount:fileimporter@production-269901.iam.gserviceaccount.com --role=roles/bigquery.jobUser ``` 3. To grant the `roles/bigquery.dataViewer` role to the Arize service account on your dataset, see the BigQuery [guide to grant access to a dataset](https://cloud.google.com/bigquery/docs/control-access-to-resources-iam#grant_access_to_a_dataset) and navigate to the tab `bq`. 4. [Navigate to Step 4 - Configure your model and define your table’s schema](/ax/machine-learning/machine-learning/integrations-ml/google-bigquery#step-4-configure-your-model-and-define-your-tables-schema) ### Step 4 - Configure Your Model And Define Your Table’s Schema Match your model schema to your [model type](/ax/machine-learning/machine-learning/use-cases-ml) and define your model schema through the form input or a json schema.

Learn more about Schema fields [here](/ax/machine-learning/machine-learning/concepts-ml/model-schema-reference#list-of-model-schema-fields-for-data-ingestion-integrations). Once finished, Arize will begin querying your table and ingesting your records as model inferences. ### Step 4b. Validate Model Schema Once you fill in your applicable predictions, actuals, and model inputs, click 'Validate Schema' to visualize your model schema in the Arize UI. Check that your column names and corresponding data match for a successful import job.

### Step 5 - Add Model Data To The Table Or View Arize will run queries to ingest records from your table based on your configured **refresh interval**. ### Step 6 - Check your Table Import Job Arize will attempt a dry run to validate your job for any access, schema, or record-level errors. If the dry run is successful, you can proceed to create the import job.

From there, you will be taken to the 'Job Status' tab where you can see the status of your import jobs.

**All active jobs** will regularly sync new data from your data source with Arize. You can view the job details and import progress by clicking on the job ID, which reveals more information about the job.

### Step 6.5 Pause or Delete An Import Job To pause or edit your table schema, click on 'Job Options'. * **Delete** a job if it is no longer needed or if you made an error connecting to the wrong bucket. This will set your job status as 'deleted' in Arize. * **Pause** a job if you have a set cadence to update your table. This way, you can 'start job' when you know there will be new data to reduce query costs. This will set your job status as 'inactive' in Arize.

### Step 7 - Troubleshooting An Import Job An import job may run into a few problems. Use the dry run and job details UI to troubleshoot and quickly resolve data ingestion issues. #### Validation Errors If there is an error validating a file or table against the model schema, Arize will surface an **actionable** error message. From there, click on the 'Fix Schema' button to adjust your model schema.

#### Dry Run File/Table Passes But The Job Fails If your dry run is successful, but your job fails, click on the job ID to view the\*\* job details\*\*. This uncovers job details such as information about the file path or query id, the last import job, potential errors, and error locations.

Within the Job Details section, you can select More Details on a specific query to view the start time and end time that was used in that query. The query start time represents the max value of the [change\_timestamp](/ax/machine-learning/machine-learning/integrations-ml/google-bigquery/google-bigquery-faq#how-do-i-update-fix-a-row-that-failed-to-ingest) based on the previous query, and the query end time is the current day/time that the query was run. The query start time will then be updated after each query to reflect the current max `change_timestamp`. This can help debug issues specifically related to the `change_timestamp` field.

Once you've identified the job failure point, append the edited row to the end of your table with an updated [change\_timestamp](/ax/machine-learning/machine-learning/integrations-ml/google-bigquery/google-bigquery-faq#how-do-i-update-fix-a-row-that-failed-to-ingest) value.