BigQuery (via Collector method) - v3.0.0

About Collectors

Pre-requisites

Collector Server Minimum Requirements

BigQuery Requirements

• Access to BiqQuery

Step 1: Establish BigQuery Access

• Create a Service Account by going to the Google Cloud Admin or clicking on this link

  • Give the Service Account a name (e.g. KADA BQ Integration)

  • Select the Projects that include the BigQuery instance(s) that you want to catalog

  • Click Save
    
    

• Create a Service Token

  • Click on the Service Account

    Open

    

  • Select the Keys tab. Click on Create new key

    Open

    

  • Select the JSON option. After clicking ‘CREATE’, the JSON file will automatically download to your device. Provide this to the user(s) that will complete the next steps

• Add permission grants on the Service Account by going to IAM page or clicking on this link

  • Click on ADD

  • Add the Service Account to the ‘New principals’ field.

  • Grant the following roles this principal as shown in the following screenshot.

    • BigQuery Job User

    • BigQuery Metadata Viewer

    • BigQuery Read Session User

    • BigQuery Resource Viewer

  • Click SAVE

Step 2: Create the Source in K

Create a BigQuery source in K

• Go to Settings, Select Sources and click Add Source

• Select “Load from File system” option
  
  

   

• Give the source a Name - e.g. BigQuery Production

• Add the Host name for the BigQuery Server

• Click Finish Setup

Step 3: Getting Access to the Source Landing Directory

Step 4: Install the Collector

It is recommended to use a python environment such as pyenv or pipenv if you are not intending to install this package at the system level.

Some python packages also have dependencies on the OS level packages, so you may be required to install additional OS packages if the below fails to install.

You can download the Latest Core Library and whl via Platform Settings → Sources → Download Collectors

Run the following command to install the collector

You will also need to install the common library kada_collectors_lib for this collector to function properly.

Step 5: Configure the Collector

The collector requires a set of parameters to connect to and extract metadata from BigQuery

These parameters can be added directly into the run or you can use pass the parameters in via a JSON file.

KADA provides an out of the box script that reads a configuration JSON file and runs the extractor. Below is the configuration file.

kada_bigquery_extractor_config.json

Step 6: Run the Collector

The following code is an example of how to run the extractor. You may need to uplift this code to meet any code standards at your organisation.

This can be executed in any python environment where the whl has been installed. It will produce and read a high water mark file from the same directory as the execution called bigquery_hwm.txt and produce files according to the configuration JSON.

This is the wrapper script: kada_bigquery_extractor.py

Advance options:

If you wish to maintain your own high water mark files elsewhere you can use the above section’s script as a guide on how to call the extractor. The configuration file is simply the keyword arguments in JSON format. Refer to this document for more information Collector Integration General Notes | Storing HWM in another location

If you are handling external arguments of the runner yourself, you’ll need to consider additional items for the run method. Refer to this document for more information Collector Integration General Notes | The run method

regions: The list of regions specified by user to extract
projects: The list of projects specified by user to extract
host: The host value onboarded in K
json_credentials: The json credentials for connection to BQ
sql: The list of SQL queries that will be executed by the program
output_path: full or relative path to where the outputs should go
mask: To mask the META/DATABASE_LOG files or not
compress: To gzip output files or not

Step 7: Check the Collector Outputs

K Extracts

A set of files (eg metadata, databaselog, linkages, events etc) will be generated. These files will appear in the output_path directory you set in the configuration details

High Water Mark File

A high water mark file is created in the same directory as the execution called bigquery_hwm.txt and produce files according to the configuration JSON. This file is only produced if you call the publish_hwm method.

If you want prefer file managed hwm, you can edit the location of the hwn by following these instructions Collector Integration General Notes | Storing High Water Marks (HWM)

Step 8: Push the Extracts to K

Once the files have been validated, you can push the files to the K landing directory.

You can use Azure Storage Explorer if you want to initially do this manually. You can push the files using python as well (see Airflow example below)

Example: Using Airflow to orchestrate the Extract and Push to K