Metadata-Version: 2.1
Name: target-bigquery-partition
Version: 0.1.0
Summary: Google BigQuery target of singer.io framework.
Home-page: https://github.com/anelendata/target-bigquery
Author: Daigo Tanaka, Anelen Co., LLC
License: UNKNOWN
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
Requires-Dist: google-api-python-client (>=1.6.2)
Requires-Dist: google-cloud-bigquery (==1.16.0)
Requires-Dist: setuptools (>=40.3.0)
Requires-Dist: simplejson (==3.11.1)
Requires-Dist: singer-python (>=5.2.0)

# 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](https://singer.io) tap that produces JSON-formatted data
following the [Singer spec](https://github.com/singer-io/getting-started/blob/master/SPEC.md).

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](https://googlecloudplatform.github.io/google-cloud-python/latest/bigquery/usage.html))

 1. Use [this wizard](https://console.developers.google.com/start/api?id=bigquery-json.googleapis.com) 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](https://github.com/singer-io/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 &copy; 2020- Anelen Data


