BigQuery

Setting up the BigQuery destination connector involves setting up the data loading method (BigQuery Standard method and Google Cloud Storage bucket) and configuring the BigQuery destination connector using the Airbyte UI.

This page guides you through setting up the BigQuery destination connector.

Prerequisites

• A Google Cloud project with BigQuery enabled

• A BigQuery dataset to sync data to.

  Note: Queries written in BigQuery can only reference datasets in the same physical location. If you plan on combining the data that Airbyte syncs with data from other datasets in your queries, create the datasets in the same location on Google Cloud. For more information, read Introduction to Datasets

• (Required for Airbyte Cloud; Optional for Airbyte Open Source) A Google Cloud Service Account with the BigQuery User and BigQuery Data Editor roles and the Service Account Key in JSON format.

Setup guide

Step 1: Set up a data loading method

Although you can load data using BigQuery's INSERTS, we highly recommend using a Google Cloud Storage bucket not only for performance and cost but reliability since larger datasets are prone to more failures when using standard inserts.

(Recommended) Using a Google Cloud Storage bucket

To use a Google Cloud Storage bucket:

1. Create a Cloud Storage bucket with the Protection Tools set to none or Object versioning. Make sure the bucket does not have a retention policy.
2. Create an HMAC key and access ID.
3. Grant the Storage Object Admin role to the Google Cloud Service Account. This must be the same service account as the one you configure for BigQuery access in the BigQuery connector setup step.
4. Make sure your Cloud Storage bucket is accessible from the machine running Airbyte. The easiest way to verify if Airbyte is able to connect to your bucket is via the check connection tool in the UI.

Your bucket must be encrypted using a Google-managed encryption key (this is the default setting when creating a new bucket). We currently do not support buckets using customer-managed encryption keys (CMEK). You can view this setting under the "Configuration" tab of your GCS bucket, in the Encryption type row.

Using INSERT

You can use BigQuery's INSERT statement to upload data directly from your source to BigQuery. While this is faster to set up initially, we strongly recommend not using this option for anything other than a quick demo. Due to the Google BigQuery SDK client limitations, using INSERT is 10x slower than using a Google Cloud Storage bucket, and you may see some failures for big datasets and slow sources (For example, if reading from a source takes more than 10-12 hours). For more details, refer to https://github.com/airbytehq/airbyte/issues/3549

Step 2: Set up the BigQuery connector

1. Click Destinations and then click + New destination.
2. On the Set up the destination page, select BigQuery from the Destination type dropdown.
3. Enter the name for the BigQuery connector.
4. For Project ID, enter your Google Cloud project ID.
5. For Dataset Location, select the location of your BigQuery dataset.

warning

You cannot change the location later.

7. For Default Dataset ID, enter the BigQuery Dataset ID.
8. For Loading Method, select Standard Inserts or GCS Staging.

tip

We recommend using the GCS Staging option.

9. For Service Account Key JSON (Required for cloud, optional for open-source), enter the Google Cloud Service Account Key in JSON format.
10. For Transformation Query Run Type (Optional), select interactive to have BigQuery run interactive query jobs or batch to have BigQuery run batch queries.

note

Interactive queries are executed as soon as possible and count towards daily concurrent quotas and limits, while batch queries are executed as soon as idle resources are available in the BigQuery shared resource pool. If BigQuery hasn't started the query within 24 hours, BigQuery changes the job priority to interactive. Batch queries don't count towards your concurrent rate limit, making it easier to start many queries at once.

11. For Google BigQuery Client Chunk Size (Optional), use the default value of 15 MiB. Later, if you see networking or memory management problems with the sync (specifically on the destination), try decreasing the chunk size. In that case, the sync will be slower but more likely to succeed.

Supported sync modes

The BigQuery destination connector supports the following:

• Full Refresh Sync
• Incremental - Append Sync
• Incremental - Append + Deduped

Output schema

Airbyte outputs each stream into its own raw table in airbyte_internal dataset by default (can be overriden by user) and a final table with Typed columns. Contents in raw table are NOT deduplicated.

Raw Table schema

Migration field	Description	Column type
_airbyte_raw_id	A UUID assigned to each processed event	STRING
_airbyte_extracted_at	A timestamp for when the event was pulled from the data source	TIMESTAMP
_airbyte_loaded_at	Timestamp to indicate when the record was loaded into Typed tables	TIMESTAMP
_airbyte_data	A JSON blob with the event data.	STRING

Note: Although the contents of the _airbyte_data are fairly stable, schema of the raw table could be subject to change in future versions.

Final Table schema

• airbyte_raw_id: A UUID assigned by Airbyte to each event that is processed. The column type in BigQuery is String.
• airbyte_extracted_at: A timestamp representing when the event was pulled from the data source. The column type in BigQuery is Timestamp.
• _airbyte_meta: A JSON blob representing typing errors. You can query these results to audit misformatted or unexpected data. The column type in BigQuery is JSON. ... and a column of the proper data type for each of the top-level properties from your source's schema. Arrays and Objects will remain as JSON columns in BigQuery.

The output tables in BigQuery are partitioned by the Time-unit column airbyte_extracted_at at a daily granularity and clustered by airbyte_extracted_at and the table Primary Keys. Partitions boundaries are based on UTC time. This is useful to limit the number of partitions scanned when querying these partitioned tables, by using a predicate filter (a WHERE clause). Filters on the partitioning column are used to prune the partitions and reduce the query cost. (The parameter Require partition filter is not enabled by Airbyte, but you may toggle it by updating the produced tables.)

BigQuery Naming Conventions

Follow BigQuery Datasets Naming conventions.

Airbyte converts any invalid characters into _ characters when writing data. However, since datasets that begin with _ are hidden on the BigQuery Explorer panel, Airbyte prepends the namespace with n for converted namespaces.

Data type map

Airbyte type	BigQuery type
STRING	STRING
STRING (BASE64)	STRING
STRING (BIG_NUMBER)	STRING
STRING (BIG_INTEGER)	STRING
NUMBER	NUMERIC
INTEGER	INT64
BOOLEAN	BOOL
STRING (TIMESTAMP_WITH_TIMEZONE)	TIMESTAMP
STRING (TIMESTAMP_WITHOUT_TIMEZONE)	DATETIME
STRING (TIME_WITH_TIMEZONE)	STRING
STRING (TIME_WITHOUT_TIMEZONE)	TIME
DATE	DATE
OBJECT	JSON
ARRAY	JSON

Troubleshooting permission issues

The service account does not have the proper permissions.

• Make sure the BigQuery service account has BigQuery User and BigQuery Data Editor roles or equivalent permissions as those two roles.
• If the GCS staging mode is selected, ensure the BigQuery service account has the right permissions to the GCS bucket and path or the Cloud Storage Admin role, which includes a superset of the required permissions.

The HMAC key is wrong.

• Make sure the HMAC key is created for the BigQuery service account, and the service account has permission to access the GCS bucket and path.