Google BigQuery is a fully managed, fast, low cost analytics data warehouse. BigQuery is pay-as-you-go, making it cost-effective for all volumes of data. Its serverless architecture makes powerful analytical and business intelligence queries available via SQL to companies of all types.

For more information, check out Google’s Google BigQuery overview.

This guide serves as a reference for version 2 of Stitch’s Google BigQuery destination.


Details and features

Stitch features

High-level details about Stitch’s implementation of Google BigQuery, such as supported connection methods, availability on Stitch plans, etc.

Release status

Beta

Stitch plan availability

All Stitch plans

Supported versions

Not applicable

Connect API availability Supported

This version of the Google BigQuery destination can be created and managed using Stitch’s Connect API. Learn more.

SSH connections Unsupported

Stitch does not support using SSH tunnels to connect to Google BigQuery destinations.

SSL connections Supported

Stitch will attempt to use SSL to connect by default. No additional configuration is needed.

VPN connections Unsupported

Virtual Private Network (VPN) connections may be implemented as part of an Enterprise plan. Contact Stitch Sales for more info.

Default loading behavior

Selected by you
Default loading behavior (Upsert or Append-Only) is defined by you. Learn more.

Nested structure support

Supported
Nested data structures will be maintained. Learn more.

Destination details

Details about the destination, including object names, table and column limits, reserved keywords, etc.

Note: Exceeding the limits noted below will result in loading errors or rejected data.

Maximum record size

4MB

Table name length

1,024 characters

Column name length

128 characters

Maximum table size

None

Maximum tables per database

None

Case sensitivity

Insensitive

Reserved keywords

Refer to the Reserved keywords documentation.

Supported Google Cloud Storage regions

When you set up a Google BigQuery destination, you’ll select a Google Storage location. This determines the location of the internal Google Storage bucket Stitch uses during the replication process.

Stitch supports the following Google Cloud Storage regions for version 2 of the Google BigQuery destination:

Region description Region name
Americas Los Angeles us-west2
Montréal northamerica-northeast1
Northern Virginia us-east4
São Paulo southamerica-east1
United States US
Europe European Union EU
Finland europe-north1
Frankfurt europe-west3
London europe-west2
Zürich europe-west6
Asia Pacific Hong Kong asia-east2
Mumbai asia-south1
Osaka asia-northeast2
Singapore asia-southeast1
Sydney australia-southeast1
Taiwan asia-east1
Tokyo asia-northeast1

Google BigQuery pricing

Unlike many other cloud-based data warehouse solutions, Google BigQuery’s pricing model is based on usage and not a fixed-rate. This means that your bill can vary over time.

Before fully committing yourself to using Google BigQuery as your data warehouse, we recommend familiarizing yourself with the Google BigQuery pricing model and how using Stitch may impact your costs.

Learn more about Stitch & Google BigQuery pricing


Replication

Replication process overview

Overview of the replication process for BigQuery v2 destinations

Step 1: Data extraction

Stitch requests and extracts data from a data source. Refer to the System overview guide for a more detailed explanation of the Extraction phase.

Step 2: Stitch's internal pipeline

The data extracted from sources is processed by Stitch. Stitch’s internal pipeline includes the Prepare and Load phases of the replication process:

  • Prepare: During this phase, the extracted data is buffered in Stitch’s durable, highly available internal data pipeline and readied for loading.
  • Load: During this phase, the prepared data is transformed to be compatible with the destination, and then loaded. Refer to the Transformations section for more info about the transformations Stitch performs for Google BigQuery destinations.

Refer to the System overview guide for a more detailed explanation of these phases.

Step 3: Google Cloud Storage bucket

Stitch loads the data into a Stitch-owned Google Cloud Storage (GCS) bucket in the region you select during destination setup.

Step 4: BigQuery staging tables

Using the IAM service account you provide during destination setup, data is read and transferred from the GCS bucket to staging tables in Google BigQuery. Staging tables from previous loads are deleted before the new load begins.

Step 5: Data merge

Data is merged from the staging tables into datasets in Google BigQuery.

The loading behavior you select during setup determines not only what the data looks like in the destination, but the method Stitch uses to load it. Note: The loading behavior can also affect your Google BigQuery costs.

Once completed, the data is deleted from Stitch’s internal GCS bucket.

Loading behavior

How data is loaded into Google BigQuery depends on the Loading behavior setting you define during destination setup:

  • Upsert: Existing rows are updated in tables with defined Primary Keys. A single version of a row will exist in the table.

  • Append-Only: Existing rows aren’t updated. Multiple versions of a row can exist in a table, creating a log of how a row changed over time.

    Because of this loading strategy, querying may require a different strategy than usual. Using some of the system columns Stitch inserts into tables will enable you to locate the latest version of a record at query time.

Refer to the Understanding loading behavior guide for more info.

Note: Loading behavior can impact your Google BigQuery costs.

Primary Keys

Stitch requires Primary Keys to de-dupe incrementally replicated data. To ensure Primary Key data is available, Stitch creates an _sdc_primary_keystable in every integration dataset. This table contains a list of all tables in an integration’s dataset and the columns those tables use as Primary Keys.

Refer to the _sdc_primary_keys table documentation for more info.

Note: Removing or altering this table can lead to replication issues.

Incompatible sources

No compatibility issues have been discovered between Google BigQuery and Stitch's integration offerings.

See all destination and integration incompatibilities.


Transformations

System tables and columns

Stitch will create the following tables in each integration’s dataset:

Additionally, Stitch will insert system columns (prepended with _sdc) into each table.

Data typing

Stitch converts data types only where needed to ensure the data is accepted by Google BigQuery. In the table below are the data types Stitch supports for Google BigQuery destinations, and the Stitch types they map to.

  • Stitch type: The Stitch data type the source type was mapped to. During the Extraction and Preparing phases, Stitch identifies the data type in the source and then maps it to a common Stitch data type.
  • Destination type: The destination-compatible data type the Stitch type maps to. This is the data type Stitch will use to store data in Google BigQuery.
  • Notes: Details about the data type and/or its allowed values in the destination, if available. If a range is available, values that exceed the noted range will be rejected by Google BigQuery.
Stitch type Destination type Notes
BIGINT INTEGER
  • Range : -9,223,372,036,854,775,808 to 9,223,372,036,854,775,807

BOOLEAN BOOLEAN
DATE TIMESTAMP
  • Description: Stored in UTC

  • Range : 0001-01-01 00:00:00 to 9999-12-31 23:59:59.999999 UTC

DOUBLE FLOAT
FLOAT FLOAT
INTEGER INTEGER
  • Range : -9,223,372,036,854,775,808 to 9,223,372,036,854,775,807

NUMBER NUMERIC
  • Description: Up to 38 digits of precision and 9 digits of scale

  • Range : -99999999999999999999999999999.999999999 to 99999999999999999999999999999.999999999

STRING STRING
  • Description: No maximum width

JSON structures

Google BigQuery supports nested records within tables, whether it’s a single record or repeated values. Refer to the Google BigQuery and Storing Nested Data Structures documentation for more info and examples.

Column names

Column names in Google BigQuery:

Stitch will perform the following transformations to ensure column names adhere to the rules imposed by Google BigQuery:

Transformation Source column Destination column
Convert uppercase and mixed case to lowercase CUSTOMERID or cUsTomErId customerid
Convert spaces to underscores customer id customer_id
Convert special characters to underscores customer#id or !customerid customer_id and _customerid
Convert leading numbers to underscores 4customerid _customerid

Timezones

Google BigQuery will store the value in UTC as TIMESTAMP.

More info about timestamp data types can be found in BigQuery’s documentation.


Compare destinations

Not sure if Google BigQuery is the data warehouse for you? Check out the Choosing a Stitch Destination guide to compare each of Stitch’s destination offerings.


Questions? Feedback?

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