---
title: Google BigQuery
---
# Google BigQuery

Events can be sent to a Google BigQuery table using the `bigQuery` sink type.

Like all Sinks, BigQuery sinks can be created in the Stream Portal...

![big-query](/img/stream/bigquery-create.png)

... or [in the API](https://api.svix.com/docs#tag/Sink/operation/v1.streaming.sink.create).

```shell
curl -X 'POST' 'https://api.svix.com/api/v1/stream/strm_30XKA2tCdjHue2qLkTgc0/sink' \
  -H 'Authorization: Bearer AUTH_TOKEN' \
  -H 'Content-Type: application/json' \
  -d '{
  "type": "bigQuery",
  "config": {
    "projectId": "my-gcp-project",
    "datasetId": "my_dataset",
    "tableId": "events",
    "credentials": "<json encoded service account credentials>"
  },
  "uid": "unique-identifier",
  "status": "enabled",
  "batchSize": 1000,
  "maxWaitSecs": 300,
  "eventTypes": [],
  "metadata": {}
}'
```

Every event batch is inserted into the configured BigQuery table.

- `projectId` — the GCP project that owns the dataset.
- `datasetId` — the BigQuery dataset that contains the table.
- `tableId` — the table that receives the rows.
- `credentials` — a Google Cloud service account credentials JSON object, provided as a string.

## Destination table

Without a transformation, Svix inserts each event into the table identified by `projectId`, `datasetId`, and `tableId` using two columns: `id` and `payload`. Svix generates a unique `id` for each row and writes the raw event payload to `payload`.

The table must already exist before you enable the sink. For the default behavior, create it with:

```sql
CREATE TABLE `my-gcp-project.my_dataset.events` (
  id STRING,
  payload STRING
);
```

If you use a transformation (below), you control the columns each row contains, so your table can use any schema you like — as long as it matches the rows your transformation returns.

# Transformations

Each event is inserted into BigQuery as a row. A transformation returns the `rows` to insert, where each row is an object whose keys match your table's columns.

```JavaScript
/**
 * @param input - The input object
 * @param input.events - The array of events in the batch. The number of events in the batch is capped by the Sink's batch size.
 * @param input.events[].payload - The message payload (string or JSON)
 * @param input.events[].eventType - The message event type (string)
 *
 * @returns Object describing the rows to insert.
 * @returns returns.rows - The array of rows to insert. Each row is an object whose keys match the columns of your BigQuery table.
 */
function handler(input) {
    const rows = input.events.map((event) => ({
        id: crypto.randomUUID(),
        payload: JSON.stringify(event.payload)
    }));

    return { rows };
}
```

`input.events` matches the events sent in [`create_events`](https://api.svix.com/docs#tag/Event/operation/v1.streaming.events.create).

Each entry in the returned `rows` array is inserted as a separate row. The object keys must match the column names of your table. To write different columns, adjust the objects in `rows` and your table schema to match.

For example, if the following events are written to the stream:

```shell
curl -X 'POST' \
  'https://api.svix.com/api/v1/stream/{stream_id}/events' \
  -H 'Authorization: Bearer AUTH_TOKEN' \
  -H 'Accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
        "events": [
            {
                "eventType": "user.created",
                "payload": "{\"email\": \"joe@enterprise.io\"}"
            },
            {
                "eventType": "user.login",
                "payload": "{\"id\": 12, \"timestamp\": \"2025-07-21T14:23:17.861Z\"}"
            }
        ]
    }'
```

The transformation above inserts two rows into your table.

| `id` | `payload` |
| --- | --- |
| `1f0a8c1e-...` | `{"email":"joe@enterprise.io"}` |
| `4b9d2e7a-...` | `{"id":12,"timestamp":"2025-07-21T14:23:17.861Z"}` |