Extract, Transform, and Load Google Data Catalog Data in Python

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Google Data Catalog Python Connector

Python Connector Libraries for Google Data Catalog Data Connectivity. Integrate Google Data Catalog with popular Python tools like Pandas, SQLAlchemy, Dash & petl.



The CData Python Connector for Google Data Catalog enables you to create ETL applications and pipelines for Google Data Catalog data in Python with petl.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for Google Data Catalog and the petl framework, you can build Google Data Catalog-connected applications and pipelines for extracting, transforming, and loading Google Data Catalog data. This article shows how to connect to Google Data Catalog with the CData Python Connector and use petl and pandas to extract, transform, and load Google Data Catalog data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Google Data Catalog data in Python. When you issue complex SQL queries from Google Data Catalog, the driver pushes supported SQL operations, like filters and aggregations, directly to Google Data Catalog and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Google Data Catalog Data

Connecting to Google Data Catalog data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

Google Data Catalog uses the OAuth authentication standard. Authorize access to Google APIs on behalf on individual users or on behalf of users in a domain.

Before connecting, specify the following to identify the organization and project you would like to connect to:

  • OrganizationId: The ID associated with the Google Cloud Platform organization resource you would like to connect to. Find this by navigating to the cloud console.

    Click the project selection drop-down, and select your organization from the list. Then, click More -> Settings. The organization ID is displayed on this page.

  • ProjectId: The ID associated with the Google Cloud Platform project resource you would like to connect to.

    Find this by navigating to the cloud console dashboard and selecting your project from the Select from drop-down. The project ID will be present in the Project info card.

When you connect, the OAuth endpoint opens in your default browser. Log in and grant permissions to the application to completes the OAuth process. For more information, refer to the OAuth section in the Help documentation.

After installing the CData Google Data Catalog Connector, follow the procedure below to install the other required modules and start accessing Google Data Catalog through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for Google Data Catalog Data in Python

Once the required modules and frameworks are installed, we are ready to build our ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.googledatacatalog as mod

You can now connect with a connection string. Use the connect function for the CData Google Data Catalog Connector to create a connection for working with Google Data Catalog data.

cnxn = mod.connect("ProjectId=YourProjectId;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query Google Data Catalog

Use SQL to create a statement for querying Google Data Catalog. In this article, we read data from the Schemas entity.

sql = "SELECT Type, DatasetName FROM Schemas WHERE ProjectId = 'bigquery-public-data'"

Extract, Transform, and Load the Google Data Catalog Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the Google Data Catalog data. In this example, we extract Google Data Catalog data, sort the data by the DatasetName column, and load the data into a CSV file.

Loading Google Data Catalog Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'DatasetName')

etl.tocsv(table2,'schemas_data.csv')

With the CData Python Connector for Google Data Catalog, you can work with Google Data Catalog data just like you would with any database, including direct access to data in ETL packages like petl.

Free Trial & More Information

Download a free, 30-day trial of the Google Data Catalog Python Connector to start building Python apps and scripts with connectivity to Google Data Catalog data. Reach out to our Support Team if you have any questions.



Full Source Code


import petl as etl
import pandas as pd
import cdata.googledatacatalog as mod

cnxn = mod.connect("ProjectId=YourProjectId;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT Type, DatasetName FROM Schemas WHERE ProjectId = 'bigquery-public-data'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'DatasetName')

etl.tocsv(table2,'schemas_data.csv')