How to Build an ETL App for Cvent 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 Cvent-connected applications and pipelines for extracting, transforming, and loading Cvent data. This article shows how to connect to Connect AI and use petl to extract, transform, and load Cvent 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 Cvent 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 "Cvent" from the Add Connection panel

3. Enter the necessary authentication properties to connect to Cvent.

   Before you can authenticate to Cvent, you must create a workspace and an OAuth application.

   Creating a Workspace

   To create a workspace:

   1. Sign into Cvent and navigate to App Switcher (the blue button in the upper right corner of the page) >> Admin.
   2. In the Admin menu, navigate to Integrations >> REST API.
   3. A new tab launches for Developer Management. Click on Manage API Access in the new tab.
   4. Create a Workspace and name it. Select the scopes you would like your developers to have access to. Scopes control what data domains the developer can access.
      • Choose All to allow developers to choose any scope, and any future scopes added to the REST API.
      • Choose Custom to limit the scopes developers can choose for their OAuth apps to selected scopes. To access all tables exposed by the driver, you need to set the following scopes:

        event/attendees:read	event/attendees:write	event/contacts:read
        event/contacts:write	event/custom-fields:read	event/custom-fields:write
        event/events:read	event/events:write	event/sessions:delete
        event/sessions:read	event/sessions:write	event/speakers:delete
        event/speakers:read	event/speakers:write	budget/budget-items:read
        budget/budget-items:write	exhibitor/exhibitors:read	exhibitor/exhibitors:write
        survey/surveys:read	survey/surveys:write

   Creating an OAuth Application

   After you have set up a Workspace and invited them, developers can sign up and create a custom OAuth app. See the Creating a Custom OAuth Application section in the Help documentation for more information.

   Connecting to Cvent

   After creating an OAuth application, set the following connection properties to connect to Cvent:

   • InitiateOAuth: GETANDREFRESH. Used to automatically get and refresh the OAuthAccessToken.
   • OAuthClientId: The Client ID associated with the OAuth application. You can find this on the Applications page in the Cvent Developer Portal.
   • OAuthClientSecret: The Client secret associated with the OAuth application. You can find this on the Applications page in the Cvent Developer Portal.
   
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 Cvent 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 Cvent

Use SQL to create a statement for querying Cvent. In this article, we read data from the Events entity. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, Cvent1).

sql = (
    "SELECT Id, Title "
    "FROM [Cvent1].[Cvent].[Events] "
    "WHERE Virtual = 'true'"
)

Extract, Transform, and Load the Cvent Data

With a connection and query in hand, use petl to extract, transform, and load the Cvent data. In this example, we extract Cvent data, sort the data by the Title column, and load the data into a CSV file.

table1 = etl.fromdb(conn, sql)

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

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

Load New Rows Back into Cvent

When Cvent supports writes, load rows back with a batch INSERT. The SDK's executemany takes @name placeholders and a list of parameter dictionaries, one per row.

cur = conn.cursor()
cur.executemany(
    "INSERT INTO [Cvent1].[Cvent].[Events] (Id, Title) "
    "VALUES (@val1, @val2)",
    [
        {"@val1": "New value 1", "@val2": "New value 1"},
        {"@val1": "New value 2", "@val2": "New value 2"},
    ],
)
print(f"Rows inserted: {cur.rowcount}")

conn.close()

Note: Even for writable sources, a read-only PAT or connection permission will reject write operations.

With the CData Connect AI Python SDK, you can work with Cvent 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 Cvent data through petl using the CData Connect AI Python SDK. For more information on connecting to Cvent (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start building data pipelines for live Cvent 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 Id, Title "
    "FROM [Cvent1].[Cvent].[Events] "
    "WHERE Virtual = 'true'"
)

table1 = etl.fromdb(conn, sql)

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

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

cur = conn.cursor()
cur.executemany(
    "INSERT INTO [Cvent1].[Cvent].[Events] (Id, Title) "
    "VALUES (@val1, @val2)",
    [
        {"@val1": "New value 1", "@val2": "New value 1"},
        {"@val1": "New value 2", "@val2": "New value 2"},
    ],
)
print(f"Rows inserted: {cur.rowcount}")
conn.close()