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

3. Enter the necessary authentication properties to connect to Act CRM.

   The User and Password properties, under the Authentication section, must be set to valid Act! user credentials. In addition to the authentication values, see the following:

   • Connecting to Act! Premium

     In addition to the authentication values, the URL to Act! is also required; for example https://eup1-iis-04.eu.hosted.act.com/.

     Additionally, you must specify the ActDatabase you will connect to. This is found by going to the About Act! Premium menu of your account, at the top right of the page, in the ? menu. Use the Database Name in the window that appears.

   • Connecting to Act! Premium Cloud

     To connect to your Act! Premium Cloud account, you also need to specify the ActCloudName property. This property is found in the URL address of the Cloud account; for example https://eup1-iis-04.eu.hosted.act.com/ActCloudName/.

   Note that retrieving ActCRM metadata can be expensive. It is advised that you set the CacheMetadata property to store the metadata locally.

   
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 Act CRM 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 Act CRM

Use SQL to create a statement for querying Act CRM. In this article, we read data from the Activities entity. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, ActCRM1).

sql = (
    "SELECT ActivityDisplayName, Subject "
    "FROM [ActCRM1].[ActCRM].[Activities] "
    "WHERE Subject = 'Sample subject'"
)

Extract, Transform, and Load the Act CRM Data

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

table1 = etl.fromdb(conn, sql)

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

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

Load New Rows Back into Act CRM

When Act CRM 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 [ActCRM1].[ActCRM].[Activities] (ActivityDisplayName, Subject) "
    "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 Act CRM 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 Act CRM data through petl using the CData Connect AI Python SDK. For more information on connecting to Act CRM (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start building data pipelines for live Act CRM 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 ActivityDisplayName, Subject "
    "FROM [ActCRM1].[ActCRM].[Activities] "
    "WHERE Subject = 'Sample subject'"
)

table1 = etl.fromdb(conn, sql)

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

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

cur = conn.cursor()
cur.executemany(
    "INSERT INTO [ActCRM1].[ActCRM].[Activities] (ActivityDisplayName, Subject) "
    "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()