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

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Connect AI Python SDK 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 Connect AI and use petl to extract, transform, and load Google Cloud Storage data.

The Connect AI Python SDK (cdata-connect-ai) is a DB-API 2.0 (PEP 249) compliant client, so petl can read directly from the SDK connection with etl.fromdb. There is no driver to install per source: connect with a Personal Access Token and build your pipeline.

Connect to Google Cloud Storage in Connect AI

CData Connect AI uses a straightforward, point-and-click interface to connect to data sources.

1. Log into Connect AI, click Sources, and then click Add Connection

2. Select "Google Cloud Storage" from the Add Connection panel

3. Enter the necessary authentication properties to connect to Google Cloud Storage.

   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.

   
4. Click Save & Test
5. Navigate to the Permissions tab and update the user-based permissions.

Generate a Personal Access Token (PAT)

The Python SDK authenticates to Connect AI with your account email and a Personal Access Token (PAT). It is best practice to create a separate PAT for each application to maintain granularity of access.

1. Click the Gear icon () at the top right of the Connect AI app to open the Settings page.
2. On the Settings page, go to the Access Tokens section and click Create PAT.
3. Give the PAT a name and click Create.
4. The PAT is only visible at creation, so copy it and store it securely.

Install Required Modules

Install the SDK and the petl framework using the pip utility:

pip install cdata-connect-ai
pip install petl

Build an ETL App for Google Cloud Storage Data in Python

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

First, import the modules and connect to Connect AI with your account email and PAT:

import petl as etl
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

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. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, GoogleCloudStorage1).

sql = (
    "SELECT Name, OwnerId "
    "FROM [GoogleCloudStorage1].[GoogleCloudStorage].[Buckets] "
    "WHERE Name = 'TestBucket'"
)

Extract, Transform, and Load the Google Cloud Storage Data

With a connection and query in hand, 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.

table1 = etl.fromdb(conn, sql)

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

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

Google Cloud Storage is a read-only source in Connect AI, so this pipeline can extract and transform Google Cloud Storage data but not load rows back. Close the connection when the extract is complete:

conn.close()

With the CData Connect AI Python SDK, 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.

More Information and Free Trial

Now you can pipe live Google Cloud Storage data through petl using the CData Connect AI Python SDK. For more information on connecting to Google Cloud Storage (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start building data pipelines for live Google Cloud Storage data in Python.

Full Source Code

import petl as etl
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

sql = (
    "SELECT Name, OwnerId "
    "FROM [GoogleCloudStorage1].[GoogleCloudStorage].[Buckets] "
    "WHERE Name = 'TestBucket'"
)

table1 = etl.fromdb(conn, sql)

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

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