target-bigquery - Meltano Hub

BigQuery

target-bigquery from transferwise

For loading data into BigQuery

The target-bigquery Meltano loader pulls data from BigQuery that can then be sent to a destination using a loader.

Other Available Variants

adswerve (default)
transferwise

Getting Started

Prerequisites

If you haven't already, follow the initial steps of the Getting Started guide:

Installation and configuration

Add the target-bigquery loader to your project using
```
meltano add
```
:

meltano add loader target-bigquery --variant transferwise

Configure the target-bigquery settings using
```
meltano config
```
:

meltano config target-bigquery set --interactive

Next steps

Follow the remaining steps of the Getting Started guide:

Run a data integration (EL) pipeline

If you run into any issues, learn how to get help.

Capabilities

The current capabilities for

target-bigquery

may have been automatically set when originally added to the Hub. Please review the capabilities when using this loader. If you find they are out of date, please consider updating them by making a pull request to the YAML file that defines the capabilities for this loader.

This plugin has the following capabilities:

record-flattening
hard-delete
soft-delete

You can override these capabilities or specify additional ones in your meltano.yml by adding the capabilities key.

Settings

The target-bigquery settings that are known to Meltano are documented below. To quickly find the setting you're looking for, click on any setting name from the list:

credentials_path
dataset_id
project_id
location
batch_size_rows
flush_all_streams
parallelism
max_parallelism
default_target_schema
default_target_schema_select_permission
schema_mapping
add_metadata_columns
hard_delete
data_flattening_max_level
primary_key_required
validate_records
temp_schema

You can override these settings or specify additional ones in your meltano.yml by adding the settings key.

Please consider adding any settings you have defined locally to this definition on MeltanoHub by making a pull request to the YAML file that defines the settings for this plugin.

Credentials Path (credentials_path)

Environment variable: TARGET_BIGQUERY_CREDENTIALS_PATH
Default Value: $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.

Dataset Id (dataset_id)

Environment variable: TARGET_BIGQUERY_DATASET_ID

BigQuery dataset

Project Id (project_id)

Environment variable: TARGET_BIGQUERY_PROJECT_ID

BigQuery project

Location (location)

Environment variable: TARGET_BIGQUERY_LOCATION
Default Value: US

Region where BigQuery stores your dataset

Batch Size Rows (batch_size_rows)

Environment variable: TARGET_BIGQUERY_BATCH_SIZE_ROWS
Default Value: 100000

Maximum number of rows in each batch. At the end of each batch, the rows in the batch are loaded into BigQuery.

Flush All Streams (flush_all_streams)

Environment variable: TARGET_BIGQUERY_FLUSH_ALL_STREAMS
Default Value: false

Flush and load every stream into BigQuery when one batch is full. Warning - This may trigger transfer of data with low number of records, and may cause performance problems.

Parallelism (parallelism)

Environment variable: TARGET_BIGQUERY_PARALLELISM
Default Value: 0

The number of threads used to flush tables. 0 will create a thread for each stream, up to parallelism_max. -1 will create a thread for each CPU core. Any other positive number will create that number of threads, up to parallelism_max.

Max Parallelism (max_parallelism)

Environment variable: TARGET_BIGQUERY_MAX_PARALLELISM
Default Value: 16

Max number of parallel threads to use when flushing tables.

Default Target Schema (default_target_schema)

Environment variable: TARGET_BIGQUERY_DEFAULT_TARGET_SCHEMA

Name of the schema where the tables will be created. If schema_mapping is not defined then every stream sent by the tap is loaded into this schema.

Default Target Schema Select Permission (default_target_schema_select_permission)

Environment variable: TARGET_BIGQUERY_DEFAULT_TARGET_SCHEMA_SELECT_PERMISSION

Grant USAGE privilege on newly created schemas and grant SELECT privilege on newly created

Schema Mapping (schema_mapping)

Environment variable: TARGET_BIGQUERY_SCHEMA_MAPPING

(Experimental) Useful if you want to load multiple streams from one tap to multiple BigQuery schemas. If the tap sends the stream_id in - format then this option overwrites the default_target_schema value. Note, that using schema_mapping you can overwrite the default_target_schema_select_permission value to grant SELECT permissions to different groups per schemas or optionally you can create indices automatically for the replicated tables.

Add Metadata Columns (add_metadata_columns)

Environment variable: TARGET_BIGQUERY_ADD_METADATA_COLUMNS
Default Value: false

Metadata columns add extra row level information about data ingestions, (i.e. when was the row read in source, when was inserted or deleted in bigquery etc.) Metadata columns are creating automatically by adding extra columns to the tables with a column prefix sdc. The column names are following the stitch naming conventions documented at https://www.stitchdata.com/docs/data-structure/integration-schemas#sdc-columns. Enabling metadata columns will flag the deleted rows by setting the _sdc_deleted_at metadata column. Without the add_metadata_columns option the deleted rows from singer taps will not be recognisable in BigQuery.

Hard Delete (hard_delete)

Environment variable: TARGET_BIGQUERY_HARD_DELETE
Default Value: false

When hard_delete option is true then DELETE SQL commands will be performed in BigQuery to delete rows in tables. It's achieved by continuously checking the _sdc_deleted_at metadata column sent by the singer tap. Due to deleting rows requires metadata columns, hard_delete option automatically enables the add_metadata_columns option as well.

Data Flattening Max Level (data_flattening_max_level)

Environment variable: TARGET_BIGQUERY_DATA_FLATTENING_MAX_LEVEL
Default Value: 0

Object type RECORD items from taps can be loaded into VARIANT columns as JSON (default) or we can flatten the schema by creating columns automatically. When value is 0 (default) then flattening functionality is turned off.

Primary Key Required (primary_key_required)

Environment variable: TARGET_BIGQUERY_PRIMARY_KEY_REQUIRED
Default Value: true

Log based and Incremental replications on tables with no Primary Key cause duplicates when merging UPDATE events. When set to true, stop loading data if no Primary Key is defined.

Validate Records (validate_records)

Environment variable: TARGET_BIGQUERY_VALIDATE_RECORDS
Default Value: false

Validate every single record message to the corresponding JSON schema. This option is disabled by default and invalid RECORD messages will fail only at load time by BigQuery. Enabling this option will detect invalid records earlier but could cause performance degradation.

Temp Schema (temp_schema)

Environment variable: TARGET_BIGQUERY_TEMP_SCHEMA

Name of the schema where the temporary tables will be created. Will default to the same schema as the target tables.

Something missing?

This page is generated from a YAML file that you can contribute changes to.

Edit it on GitHub!

Looking for help?

If you're having trouble getting the target-bigquery loader to work, look for an existing issue in its repository, file a new issue, or join the Meltano Slack community and ask for help in the