# Creating a BigQuery target node
<a name="creating-bigquery-target-node"></a>

## Prerequisites needed
<a name="creating-bigquery-target-node-prerequisites"></a>
+ A BigQuery type AWS Glue Data Catalog connection
+ An AWS Secrets Manager secret for your Google BigQuery credentials, used by the connection.
+ Appropriate permissions on your job to read the secret used by the connection.
+ The name and dataset of the table and corresponding Google Cloud project you would like to write to.

## Adding a BigQuery data target
<a name="creating-bigquery-target-node-add"></a>

**To add a **Data target – BigQuery** node:**

1.  Choose the connection for your BigQuery data target. Since you have created it, it should be available in the dropdown. If you need to create a connection, choose **Create BigQuery connection**. For more information, see [ Overview of using connectors and connections ](https://docs.aws.amazon.com/glue/latest/ug/connectors-chapter.html#using-connectors-overview). 

    Once you have chosen a connection, you can view the connection properties by clicking **View properties**. 

1. Identify what BigQuery table you would like to write to, then choose a **Write method**.
   + Direct – writes to BigQuery directly using the BigQuery Storage Write API.
   + Indirect – writes to Google Cloud Storage, then copies to BigQuery.

   If you would like to write indirectly, provide a destination GCS location with **Temporary GCS bucket**. You will need to provide additional configuration in your AWS Glue connection. For more information, see [Using indirect write with Google BigQuery](https://docs.aws.amazon.com/glue/latest/dg/aws-glue-programming-etl-connect-bigquery-home.html#aws-glue-programming-etl-connect-bigquery-indirect-write).

1.  Describe the data you would like to read

   **(Required) **set **Parent Project** to the project containing your table, or a billing parent project, if relevant.

   If you chose a single table, set **Table** to the name of a Google BigQuery table in the following format: `[dataset].[table]`