How to Build an ETL App for Insightly Data in Python with CData

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData API Driver for Python and the petl framework, you can build Insightly-connected applications and pipelines for extracting, transforming, and loading Insightly data. This article shows how to connect to Insightly with the CData Python Connector and use petl and pandas to extract, transform, and load Insightly data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Insightly data in Python. When you issue complex SQL queries from Insightly, the driver pushes supported SQL operations, like filters and aggregations, directly to Insightly and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Insightly Data

Connecting to Insightly data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

Start by setting the Profile connection property to the location of the Insightly Profile on disk (e.g. C:\profiles\Insightly.apip). Next, set the ProfileSettings connection property to the connection string for Insightly (see below).

Insightly API Profile Settings

Navigate to your Insightly profile and select User Settings > API to find your API key. The Pod value (e.g.,

na1

) is derived from the subdomain of your API URL.

After installing the CData Insightly Connector, follow the procedure below to install the other required modules and start accessing Insightly through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for Insightly Data in Python

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

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.api as mod

You can now connect with a connection string. Use the connect function for the CData Insightly Connector to create a connection for working with Insightly data.

cnxn = mod.connect("Profile=C:\profiles\Insightly.apip;ProfileSettings='APIKey=your_api_key;Pod=na1';")

Create a SQL Statement to Query Insightly

Use SQL to create a statement for querying Insightly. In this article, we read data from the ActivitySets entity.

sql = "SELECT ActivitysetId, Name FROM ActivitySets WHERE ForContacts = 'true'"

Extract, Transform, and Load the Insightly Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the Insightly data. In this example, we extract Insightly data, sort the data by the Name column, and load the data into a CSV file.

Loading Insightly Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData API Driver for Python, you can work with Insightly data just like you would with any database, including direct access to data in ETL packages like petl.

Free Trial & More Information

Download a free, 30-day trial of the CData API Driver for Python to start building Python apps and scripts with connectivity to Insightly data. Reach out to our Support Team if you have any questions.

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Full Source Code

import petl as etl
import pandas as pd
import cdata.api as mod

cnxn = mod.connect("Profile=C:\profiles\Insightly.apip;ProfileSettings='APIKey=your_api_key;Pod=na1';")

sql = "SELECT ActivitysetId, Name FROM ActivitySets WHERE ForContacts = 'true'"

table1 = etl.fromdb(cnxn,sql)

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

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