How to Build an ETL App for Accelo 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 Accelo-connected applications and pipelines for extracting, transforming, and loading Accelo data. This article shows how to connect to Accelo with the CData Python Connector and use petl and pandas to extract, transform, and load Accelo data.

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

Connecting to Accelo Data

Connecting to Accelo 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 Accelo Profile on disk (e.g. C:\profiles\Accelo.apip). Next, set the ProfileSettings connection property to the connection string for Accelo (see below).

Accelo API Profile Settings

Register an OAuth application in Accelo via Configuration > API > Register Application. Your client ID, client secret, and redirect URI are provided in your app settings.

After installing the CData Accelo Connector, follow the procedure below to install the other required modules and start accessing Accelo 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 Accelo 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 Accelo Connector to create a connection for working with Accelo data.

cnxn = mod.connect("Profile=C:\profiles\Accelo.apip;Authscheme=OAuth;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;")

Create a SQL Statement to Query Accelo

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

sql = "SELECT Id, ActivityClass FROM Activities WHERE Subject = 'Project Meeting'"

Extract, Transform, and Load the Accelo Data

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

Loading Accelo Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData API Driver for Python, you can work with Accelo 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 Accelo data. Reach out to our Support Team if you have any questions.

Full Source Code

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

cnxn = mod.connect("Profile=C:\profiles\Accelo.apip;Authscheme=OAuth;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;")

sql = "SELECT Id, ActivityClass FROM Activities WHERE Subject = 'Project Meeting'"

table1 = etl.fromdb(cnxn,sql)

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

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