How to Visualize Google Cloud Storage Data in Python with pandas via 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, the pandas and Matplotlib modules, you can build Google Cloud Storage-connected Python applications and scripts for visualizing Google Cloud Storage data. This article shows how to connect to Connect AI and use the pandas and Matplotlib built-in functions to query Google Cloud Storage data and visualize the results.

The Connect AI Python SDK (cdata-connect-ai) is a DB-API 2.0 (PEP 249) compliant client, so pandas can read query results directly from the SDK connection object. There is no driver to install per source and no SQLAlchemy engine to configure: connect with a Personal Access Token and pass the connection straight to pandas.read_sql.

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 (with the pandas extra) and Matplotlib using the pip utility:

pip install "cdata-connect-ai[full]"
pip install matplotlib

Visualize Google Cloud Storage Data in Python

Import the modules, then connect to Connect AI with your account email and PAT. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, GoogleCloudStorage1).

import pandas
import matplotlib.pyplot as plt
import cdata_connect_ai

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

Query Google Cloud Storage with pandas

Use the read_sql function from pandas to execute a SQL statement and store the result set in a DataFrame. Pass the SDK connection directly, no engine required.

df = pandas.read_sql(
    "SELECT Name, OwnerId "
    "FROM [GoogleCloudStorage1].[GoogleCloudStorage].[Buckets] "
    "WHERE Name = 'TestBucket'",
    conn,
)

Note: pandas may print a UserWarning stating that it only officially supports SQLAlchemy connectables. This is expected when passing a DB-API connection directly—the query runs correctly and the warning is safe to ignore.

Visualize Google Cloud Storage Data

With the query results stored in a DataFrame, use the plot function to build a chart. The show method displays the chart in a new window.

df.plot(kind="bar", x="Name", y="OwnerId")
plt.show()

conn.close()

More Information and Free Trial

Now you can read live Google Cloud Storage data into pandas through 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 working with live Google Cloud Storage data in Python.

Full Source Code

import pandas
import matplotlib.pyplot as plt
import cdata_connect_ai

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

df = pandas.read_sql(
    "SELECT Name, OwnerId "
    "FROM [GoogleCloudStorage1].[GoogleCloudStorage].[Buckets] "
    "WHERE Name = 'TestBucket'",
    conn,
)

df.plot(kind="bar", x="Name", y="OwnerId")
plt.show()

conn.close()