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

3. Enter the necessary authentication properties to connect to QuickBooks Time.

   TSheets uses the OAuth2 standard for authentication and authorization. To construct your own OAuth app and connect to data, refer to OAuth section in the Help.

   
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 QuickBooks Time 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 QuickBooks Time

Use SQL to create a statement for querying QuickBooks Time. In this article, we read data from the Timesheets entity. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, TSheets1).

sql = (
    "SELECT Id, JobcodeId "
    "FROM [TSheets1].[TSheets].[Timesheets] "
    "WHERE JobCodeType = 'regular'"
)

Extract, Transform, and Load the QuickBooks Time Data

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

table1 = etl.fromdb(conn, sql)

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

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

QuickBooks Time is a read-only source in Connect AI, so this pipeline can extract and transform QuickBooks Time 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 QuickBooks Time 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 QuickBooks Time data through petl using the CData Connect AI Python SDK. For more information on connecting to QuickBooks Time (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start building data pipelines for live QuickBooks Time 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, JobcodeId "
    "FROM [TSheets1].[TSheets].[Timesheets] "
    "WHERE JobCodeType = 'regular'"
)

table1 = etl.fromdb(conn, sql)

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

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