How to Build an ETL App for Google Cloud Storage Data in Python with CData

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 Cloud Storage and the petl framework, you can build Google Cloud Storage-connected applications and pipelines for extracting, transforming, and loading Google Cloud Storage data. This article shows how to connect to Google Cloud Storage with the CData Python Connector and use petl and pandas to extract, transform, and load Google Cloud Storage data.

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

Connecting to Google Cloud Storage Data

Connecting to Google Cloud Storage 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.

Authenticate with a User Account

You can connect without setting any connection properties for your user credentials. After setting InitiateOAuth to GETANDREFRESH, you are ready to connect.

When you connect, the Google Cloud Storage OAuth endpoint opens in your default browser. Log in and grant permissions, then the OAuth process completes

Authenticate with a Service Account

Service accounts have silent authentication, without user authentication in the browser. You can also use a service account to delegate enterprise-wide access scopes.

You need to create an OAuth application in this flow. See the Help documentation for more information. After setting the following connection properties, you are ready to connect:

• InitiateOAuth: Set this to GETANDREFRESH.
• OAuthJWTCertType: Set this to "PFXFILE".
• OAuthJWTCert: Set this to the path to the .p12 file you generated.
• OAuthJWTCertPassword: Set this to the password of the .p12 file.
• OAuthJWTCertSubject: Set this to "*" to pick the first certificate in the certificate store.
• OAuthJWTIssuer: In the service accounts section, click Manage Service Accounts and set this field to the email address displayed in the service account Id field.
• OAuthJWTSubject: Set this to your enterprise Id if your subject type is set to "enterprise" or your app user Id if your subject type is set to "user".
• ProjectId: Set this to the Id of the project you want to connect to.

The OAuth flow for a service account then completes.

After installing the CData Google Cloud Storage Connector, follow the procedure below to install the other required modules and start accessing Google Cloud Storage 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 Cloud Storage 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.googlecloudstorage as mod

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

cnxn = mod.connect("ProjectId='project1';InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query Google Cloud Storage

Use SQL to create a statement for querying Google Cloud Storage. In this article, we read data from the Buckets entity.

sql = "SELECT Name, OwnerId FROM Buckets WHERE Name = 'TestBucket'"

Extract, Transform, and Load the Google Cloud Storage Data

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

Loading Google Cloud Storage Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData Python Connector for Google Cloud Storage, you can work with Google Cloud Storage 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 CData Python Connector for Google Cloud Storage to start building Python apps and scripts with connectivity to Google Cloud Storage 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.googlecloudstorage as mod

cnxn = mod.connect("ProjectId='project1';InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT Name, OwnerId FROM Buckets WHERE Name = 'TestBucket'"

table1 = etl.fromdb(cnxn,sql)

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

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