BigQuery
BigQuery is a serverless, highly scalable, and cost-effective data warehouse offered by Google Cloud Provider.

Features

Feature
Supported?(Yes/No)
Notes
Full Refresh Sync
Yes
Incremental - Append Sync
Yes
Incremental - Deduped History
Yes
Bulk loading
Yes
Namespaces
Yes
There are two flavors of connectors for this destination:
  1. 1.
    Bigquery: This is producing the standard Airbyte outputs using a _airbyte_raw_* tables storing the JSON blob data first. Afterward, these are transformed and normalized into separate tables, potentially "exploding" nested streams into their own tables if basic normalization is configured.
  2. 2.
    Bigquery (Denormalized): Instead of splitting the final data into multiple tables, this destination leverages BigQuery capabilities with Structured and Repeated fields to produce a single "big" table per stream. This does not write the _airbyte_raw_* tables in the destination and normalization from this connector is not supported at this time.

Troubleshooting

Check out common troubleshooting issues for the BigQuery destination connector on our Discourse here.

Output Schema for BigQuery

Each stream will be output into its own table in BigQuery. Each table will contain 3 columns:
  • _airbyte_ab_id: a uuid assigned by Airbyte to each event that is processed. The column type in BigQuery is String.
  • _airbyte_emitted_at: a timestamp representing when the event was pulled from the data source. The column type in BigQuery is Timestamp.
  • _airbyte_data: a json blob representing with the event data. The column type in BigQuery is String.
The output tables from the BigQuery destination are partitioned and clustered by the Time-unit column _airbyte_emitted_at at a daily granularity. 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 will be used to prune the partitions and reduce the query cost. (The parameter "Require partition filter" is not enabled by Airbyte, but you may toggle this by updating the produced tables if you wish so)

Getting Started (Airbyte Open-Source / Airbyte Cloud)

Requirements

To use the BigQuery destination, you'll need:
  • A Google Cloud Project with BigQuery enabled
  • A BigQuery Dataset into which Airbyte can sync your data
  • A Google Cloud Service Account with the "BigQuery User" and "BigQuery Data Editor" roles in your GCP project
  • A Service Account Key to authenticate into your Service Account
For GCS Staging upload mode:
  • GCS role enabled for same user as used for biqquery
  • HMAC key obtained for user. Currently, only the HMAC key is supported. More credential types will be added in the future.
See the setup guide for more information about how to create the required resources.

Google cloud project

If you have a Google Cloud Project with BigQuery enabled, skip to the "Create a Dataset" section.
First, follow along the Google Cloud instructions to Create a Project.
Enable BigQuery
BigQuery is typically enabled automatically in new projects. If this is not the case for your project, follow the "Before you begin" section in the BigQuery QuickStart docs.

BigQuery dataset for Airbyte syncs

Airbyte needs a location in BigQuery to write the data being synced from your data sources. If you already have a Dataset into which Airbyte should sync data, skip this section. Otherwise, follow the Google Cloud guide for Creating a Dataset via the Console UI to achieve this.
Note that queries written in BigQuery can only reference Datasets in the same physical location. So if you plan on combining the data Airbyte synced with data from other datasets in your queries, make sure you create the datasets in the same location on Google Cloud. See the Introduction to Datasets section for more info on considerations around creating Datasets.

Service account

In order for Airbyte to sync data into BigQuery, it needs credentials for a Service Account with the "BigQuery User" and "BigQuery Data Editor" roles, which grants permissions to run BigQuery jobs, write to BigQuery Datasets, and read table metadata. We highly recommend that this Service Account is exclusive to Airbyte for ease of permissioning and auditing. However, you can use a pre-existing Service Account if you already have one with the correct permissions.
The easiest way to create a Service Account is to follow GCP's guide for Creating a Service Account. Once you've created the Service Account, make sure to keep its ID handy as you will need to reference it when granting roles. Service Account IDs typically take the form <account-name>@<project-name>.iam.gserviceaccount.com
Then, add the service account as a Member in your Google Cloud Project with the "BigQuery User" role. To do this, follow the instructions for Granting Access in the Google documentation. The email address of the member you are adding is the same as the Service Account ID you just created.
At this point you should have a service account with the "BigQuery User" project-level permission.

Service account key

Service Account Keys are used to authenticate as Google Service Accounts. For Airbyte to leverage the permissions you granted to the Service Account in the previous step, you'll need to provide its Service Account Keys. See the Google documentation for more information about Keys.
Follow the Creating and Managing Service Account Keys guide to create a key. Airbyte currently supports JSON Keys only, so make sure you create your key in that format. As soon as you created the key, make sure to download it, as that is the only time Google will allow you to see its contents. Once you've successfully configured BigQuery as a destination in Airbyte, delete this key from your computer.
You should now have all the requirements needed to configure BigQuery as a destination in the UI. You'll need the following information to configure the BigQuery destination:
  • Project ID
  • Dataset Location
  • Dataset ID: the name of the schema where the tables will be created.
  • Service Account Key: the contents of your Service Account Key JSON file
Additional options can also be customized:
  • Google BigQuery client chunk size: Google BigQuery client's chunk(buffer) size (MIN=1, MAX = 15) for each table. The default 15MiB value is used if not set explicitly. It's recommended to decrease value for big data sets migration for less HEAP memory consumption and avoiding crashes. For more details refer to https://googleapis.dev/python/bigquery/latest/generated/google.cloud.bigquery.client.Client.html
  • Transformation Priority: configure the priority of queries run for transformations. Refer to https://cloud.google.com/bigquery/docs/running-queries. By default, Airbyte runs interactive query jobs on BigQuery, which means that the query is executed as soon as possible and count towards daily concurrent quotas and limits. If set to use batch query on your behalf, BigQuery starts the query as soon as idle resources are available in the BigQuery shared resource pool. This usually occurs within a few minutes. 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, which can make it easier to start many queries at once.
Once you've configured BigQuery as a destination, delete the Service Account Key from your computer.

Uploading Options

There are 2 available options to upload data to BigQuery Standard and GCS Staging.

GCS Staging

This is the recommended configuration for uploading data to BigQuery. It works by first uploading all the data to a GCS bucket, then ingesting the data to BigQuery. To configure GCS Staging, you'll need the following parameters:
  • GCS Bucket Name
  • GCS Bucket Path
  • Block Size (MB) for GCS multipart upload
  • GCS Bucket Keep files after migration
    • See this for instructions on how to create a GCS bucket. The bucket cannot have a retention policy. Set Protection Tools to none or Object versioning.
  • HMAC Key Access ID
    • See this on how to generate an access key. For more information on hmac keys please reference the GCP docs
    • We recommend creating an Airbyte-specific user or service account. This user or account will require the following permissions for the bucket:
      1
      storage.multipartUploads.abort
      2
      storage.multipartUploads.create
      3
      storage.objects.create
      4
      storage.objects.delete
      5
      storage.objects.get
      6
      storage.objects.list
      Copied!
      You can set those by going to the permissions tab in the GCS bucket and adding the appropriate the email address of the service account or user and adding the aforementioned permissions.
  • Secret Access Key
    • Corresponding key to the above access ID.
  • Make sure your GCS bucket is accessible from the machine running Airbyte. This depends on your networking setup. The easiest way to verify if Airbyte is able to connect to your GCS bucket is via the check connection tool in the UI.

Standard uploads

This uploads data directly from your source to BigQuery. While this is faster to setup initially, we strongly recommend that you do not use this option for anything other than a quick demo. It is more than 10x slower than the GCS uploading option and will fail for many datasets. Please be aware you may see some failures for big datasets and slow sources, e.g. if reading from source takes more than 10-12 hours. This is caused by the Google BigQuery SDK client limitations. For more details please check https://github.com/airbytehq/airbyte/issues/3549

Naming Conventions

When you create a dataset in BigQuery, the dataset name must be unique for each project. The dataset name can contain the following:
  • Up to 1,024 characters.
  • Letters (uppercase or lowercase), numbers, and underscores.
    Note: In the Cloud Console, datasets that begin with an underscore are hidden from the navigation pane. You can query tables and views in these datasets even though these datasets aren't visible.
  • Dataset names are case-sensitive: mydataset and MyDataset can coexist in the same project.
  • Dataset names cannot contain spaces or special characters such as -, &, @, or %.
Therefore, Airbyte BigQuery destination will convert any invalid characters into '_' characters when writing data.

CHANGELOG

bigquery

Version
Date
Pull Request
Subject
0.6.5
2022-01-18
#9573
BigQuery Destination : update description for some input fields
0.6.4
2022-01-17
#8383
Support dataset-id prefixed by project-id
0.6.3
2022-01-12
#9415
BigQuery Destination : Fix GCS processing of Facebook data
0.6.2
2022-01-10
#9121
Fixed check method for GCS mode to verify if all roles assigned to user
0.6.1
2021-12-22
#9039
Added part_size configuration to UI for GCS staging
0.6.0
2021-12-17
#8788
BigQuery/BiqQuery denorm Destinations : Add possibility to use different types of GCS files
0.5.1
2021-12-16
#8816
Update dataset locations
0.5.0
2021-10-26
#7240
Output partitioned/clustered tables
0.4.1
2021-10-04
#6733
Support dataset starting with numbers
0.4.0
2021-08-26
#5296
Added GCS Staging uploading option
0.3.12
2021-08-03
#3549
Add optional arg to make a possibility to change the BigQuery client's chunk\buffer size
0.3.11
2021-07-30
#5125
Enable additionalPropertities in spec.json
0.3.10
2021-07-28
#3549
Add extended logs and made JobId filled with region and projectId
0.3.9
2021-07-28
#5026
Add sanitized json fields in raw tables to handle quotes in column names
0.3.6
2021-06-18
#3947
Service account credentials are now optional.
0.3.4
2021-06-07
#3277
Add dataset location option

bigquery-denormalized

Version
Date
Pull Request
Subject
0.2.5
2022-01-18
#9573
BigQuery Destination : update description for some input fields
0.2.4
2022-01-17
#8383
BigQuery/BiqQuery denorm Destinations : Support dataset-id prefixed by project-id
0.2.3
2022-01-12
#9415
BigQuery Destination : Fix GCS processing of Facebook data
0.2.2
2021-12-22
#9039
Added part_size configuration to UI for GCS staging
0.2.1
2021-12-21
#8574
Added namespace to Avro and Parquet record types
0.2.0
2021-12-17
#8788
BigQuery/BiqQuery denorm Destinations : Add possibility to use different types of GCS files
0.1.11
2021-12-16
#8816
Update dataset locations
0.1.10
2021-11-09
#7804
handle null values in fields described by a $ref definition
0.1.9
2021-11-08
#7736
Fixed the handling of ObjectNodes with $ref definition key
0.1.8
2021-10-27
#7413
Fixed DATETIME conversion for BigQuery
0.1.7
2021-10-26
#7240
Output partitioned/clustered tables
0.1.6
2021-09-16
#6145
BigQuery Denormalized support for date, datetime & timestamp types through the json "format" key
0.1.5
2021-09-07
#5881
BigQuery Denormalized NPE fix
0.1.4
2021-09-04
#5813
fix Stackoverflow error when receive a schema from source where "Array" type doesn't contain a required "items" element
0.1.3
2021-08-07
#5261
🐛 Destination BigQuery(Denormalized): Fix processing arrays of records
0.1.2
2021-07-30
#5125
Enable additionalPropertities in spec.json
0.1.1
2021-06-21
#3555
Partial Success in BufferedStreamConsumer
0.1.0
2021-06-21
#4176
Destination using Typed Struct and Repeated fields
Last modified 6d ago