target-bigquery-partition 0.1.0

Google BigQuery target of singer.io framework.

target-bigquery

ANELEN's implementation of target-bigquery.

This is a "lab" stage project with limited documentatioin and support. For other open-source projects by Anelen, please see https://anelen.co/open-source.html

What it does

Extract data from BigQuery tables.

This is a Singer tap that produces JSON-formatted data following the Singer spec.

This tap:

• Pulls data from Google BigQuery tables/views with datetime field.
• Infers the schema for each resource and produce catalog file.
• Incrementally pulls data based on the input state.

Installation

Step 0: Acknowledge LICENSE and TERMS

Please especially note that the author(s) of target-bigquery is not responsible for the cost, including but not limited to BigQuery cost) incurred by running this program.

Step 1: Activate the Google BigQuery API

(originally found in the Google API docs)

1. Use this wizard to create or select a project in the Google Developers Console and activate the BigQuery API. Click Continue, then Go to credentials.
2. On the Add credentials to your project page, click the Cancel button.
3. At the top of the page, select the OAuth consent screen tab. Select an Email address, enter a Product name if not already set, and click the Save button.
4. Select the Credentials tab, click the Create credentials button and select OAuth client ID.
5. Select the application type Other, enter the name "Singer BigQuery Tap", and click the Create button.
6. Click OK to dismiss the resulting dialog.
7. Click the Download button to the right of the client ID.
8. Move this file to your working directory and rename it client_secrets.json.

Export the location of the secret file:

export GOOGLE_APPLICATION_CREDENTIALS="./client_secret.json"

For other authentication method, please see Authentication section.

Step 2: Install

First, make sure Python 3 is installed on your system or follow these installation instructions for Mac or Ubuntu.

This program has not yet released via pypi. So do this to install the relatively stable version from GitHub:

pip install --no-cache-dir https://github.com/anelendata/target-bigquery/archive/71b51aa8128d7b50a8155f6d9974308cd1d4c2d4.tar.gz#egg=target-bigquery

Note: 71b51aa8128d7b50a8155f6d9974308cd1d4c2d4 in the URL is the commit hash.

Or you can install the latest development version:

pip install --no-cache-dir https://github.com/anelendata/target-bigquery/archive/master.tar.gz#egg=target-bigquery

Run

Step 1: Configure

Create a file called target_config.json in your working directory, following config.sample.json:

{
    "project_id": "your-gcp-project-id",
    "dataset_id": "your-bigquery-dataset",
    "table_id": "your-table-name",
    "stream": false,
}

Notes:

• stream: Make this true to run the streaming updates to BigQuery. Note that performance of batch update is better when keeping this option false.
• Optionally, you can define "partition_by": <some-timestamp-column-name> to create a partitioned table. Many production quailty taps implements a ingestion timestamp and it is recommended to use the column here to partition the table. It will increase the query performance and lower the BigQuery costs.

Step 2: Run

target-bigquery can be run with any Singer Target. As example, let use tap-exchangeratesapi.

pip install tap-exchangeratesapi

Run:

tap-exchangeratesapi | target-bigquery -c target_config.json

Authentication

It is recommended to use target-bigquery with a service account.

• Download the client_secrets.json file for your service account, and place it on the machine where target-bigquery will be executed.
• Set a GOOGLE_APPLICATION_CREDENTIALS environment variable on the machine, where the value is the fully qualified path to client_secrets.json

In the testing environment, you can also manually authenticate before runnig the tap. In this case you do not need GOOGLE_APPLICATION_CREDENTIALS defined:

gcloud auth application-default login

You may also have to set the project:

gcloud config set project <project-id>

Though not tested, it should also be possible to use the OAuth flow to authenticate to GCP as well:

• target-bigquery will attempt to open a new window or tab in your default browser. If this fails, copy the URL from the console and manually open it in your browser.
• If you are not already logged into your Google account, you will be prompted to log in.
• If you are logged into multiple Google accounts, you will be asked to select one account to use for the authorization.
• Click the Accept button to allow target-bigquery to access your Google BigQuery table.
• You can close the tab after the signup flow is complete.

Original repo

https://github.com/anelendata/target-bigquery

Copyright © 2020- Anelen Data