# BigQuery
The tap-bigquery
extractor pulls data from a BigQuery data warehouse.
To learn more about tap-bigquery
, refer to the repository at https://github.com/anelendata/tap-bigquery.
# Getting Started
# Prerequisites
If you haven't already, follow the initial steps of the Getting Started guide:
Then, follow the steps in the "Activate the Google BigQuery API" section of the repository's README.
# Installation and configuration
Add the
tap-bigquery
extractor to your project usingmeltano add
:meltano add extractor tap-bigquery
Configure the settings below using
meltano config
.
# Next steps
Follow the remaining step of the Getting Started guide:
If you run into any issues, learn how to get help.
# Settings
tap-bigquery
requires the configuration of the following settings:
These and other supported settings are documented below. To quickly find the setting you're looking for, use the Table of Contents in the sidebar.
# Minimal configuration
A minimal configuration of tap-bigquery
in your meltano.yml
project file will look like this:
plugins:
extractors:
- name: tap-bigquery
variant: anelendata
config:
streams:
- name: users
table: "`my_project.my_dataset.users`"
columns: [id, first_name, last_name, updated_at]
datetime_key: updated_at
- name: widgets
table: "`my_project.my_dataset.widgets`"
columns: [id, name, created_at]
datetime_key: created_at
start_datetime: '2020-10-01T00:00:00Z'
# Streams
- Name:
streams
- Environment variable:
TAP_BIGQUERY_STREAMS
Array of objects with name
, table
, columns
, datetime_key
, and filters
keys:
name
: The entity name, used by most loaders as the name of the table to be created.table
: Fully qualified table name in BigQuery, with format`<project>.<dataset>.<table>`
. Since backticks have special meaning in YAML, values inmeltano.yml
should be wrapped in double quotes.columns
: Array of column names to select. Using["*"]
is not recommended as it can become very expensive for a table with a large number of columns.datetime_key
: Name of datetime column to use as replication key.filters
: Optional array ofWHERE
clauses to filter extracted data, e.g."column='value'"
.
# How to use
Manage this setting directly in your meltano.yml
project file:
plugins:
extractors:
- name: tap-bigquery
variant: anelendata
config:
streams:
- name: <stream_name>
table: "`<project>.<dataset>.<table>`"
columns: [<column>, <column2>]
datetime_key: <datetime_column>
filters:
- "<column>=<value>"
# ...
# ...
Alternatively, manage this setting using meltano config
or an environment variable:
meltano config tap-bigquery set streams '[{"name": "<stream_name>", "table": "`<project>.<dataset>.<table>`", "columns": ["<column>", ...], "date_time_key": "<datetime_column>", "filters": [...]}, ...]'
export TAP_BIGQUERY_STREAMS='[{"name": "<stream_name>", "table": "`<project>.<dataset>.<table>`", "columns": ["<column>", ...], "date_time_key": "<datetime_column>", "filters": [...]}, ...]'
# Credentials Path
- Name:
credentials_path
- Environment variable:
TAP_BIGQUERY_CREDENTIALS_PATH
, alias:GOOGLE_APPLICATION_CREDENTIALS
- Default:
$MELTANO_PROJECT_ROOT/client_secrets.json
Fully qualified path to client_secrets.json
for your service account.
See the "Activate the Google BigQuery API" section of the repository's README and https://cloud.google.com/docs/authentication/production.
By default, this file is expected to be at the root of your project directory.
# How to use
Manage this setting using meltano config
or an environment variable:
meltano config tap-bigquery set credentials_path /home/user/Downloads/client_secrets.json
export TAP_BIGQUERY_CREDENTIALS_PATH=/home/user/Downloads/client_secrets.json
# Start Datetime
- Name:
start_datetime
- Environment variable:
TAP_BIGQUERY_START_DATETIME
This property determines how much historical data will be extracted.
Please be aware that the larger the time period and amount of data, the longer the initial extraction can be expected to take.
# How to use
Manage this setting using meltano config
or an environment variable:
meltano config tap-bigquery set start_datetime YYYY-MM-DDTHH:MM:SSZ
export TAP_BIGQUERY_START_DATETIME=YYYY-MM-DDTHH:MM:SSZ
# For example:
meltano config tap-bigquery set start_datetime 2020-10-01T00:00:00Z
export TAP_BIGQUERY_START_DATETIME=2020-10-01T00:00:00Z
# End Datetime
- Name:
end_datetime
- Environment variable:
TAP_BIGQUERY_END_DATETIME
Date up to when historical data will be extracted.
# How to use
Manage this setting using meltano config
or an environment variable:
meltano config tap-bigquery set end_datetime YYYY-MM-DDTHH:MM:SSZ
export TAP_BIGQUERY_END_DATETIME=YYYY-MM-DDTHH:MM:SSZ
# For example:
meltano config tap-bigquery set end_datetime 2020-10-01T00:00:00Z
export TAP_BIGQUERY_END_DATETIME=2020-10-01T00:00:00Z
# Limit
- Name:
limit
- Environment variable:
TAP_BIGQUERY_LIMIT
Limits the number of records returned in each stream, applied as a limit in the query.
# How to use
Manage this setting using meltano config
or an environment variable:
meltano config tap-bigquery set limit 100
export TAP_BIGQUERY_LIMIT=100
# Start Always Inclusive
- Name:
start_always_inclusive
- Environment variable:
TAP_BIGQUERY_START_ALWAYS_INCLUSIVE
- Default:
true
When replicating incrementally, disable to only select records whose datetime_key
is greater than the maximum value replicated in the last run, by excluding records whose timestamps match exactly.
This could cause records to be missed that were created after the last run finished, but during the same second and with the same timestamp.
# How to use
Manage this setting using meltano config
or an environment variable:
meltano config tap-bigquery set start_always_inclusive false
export TAP_BIGQUERY_START_ALWAYS_INCLUSIVE=false