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

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

Connecting to Asana Data

Connecting to Asana 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.

You can optionally set the following to refine the data returned from Asana.

• WorkspaceId: Set this to the globally unique identifier (gid) associated with your Asana Workspace to only return projects from the specified workspace. To get your workspace id, navigate to https://app.asana.com/api/1.0/workspaces while logged into Asana. This displays a JSON object containing your workspace name and Id.
• ProjectId: Set this to the globally unique identifier (gid) associated with your Asana Project to only return data mapped under the specified project. Project IDs can be found in the URL of your project's Overview page. This will be the numbers directly after /0/.

Connect Using OAuth Authentication

You must use OAuth to authenticate with Asana. OAuth requires the authenticating user to interact with Asana using the browser. See the "Getting Started" chapter of the help documentation for a guide to using OAuth.

After installing the CData Asana Connector, follow the procedure below to install the other required modules and start accessing Asana 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 Asana 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.asana as mod

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

cnxn = mod.connect("OAuthClientId=YourClientId;OAuthClientSecret=YourClientSecret;CallbackURL='http://localhost:33333';InitiateOAuth=GETANDREFRESH;")

Create a SQL Statement to Query Asana

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

sql = "SELECT Id, WorkspaceId FROM projects WHERE Archived = 'true'"

Extract, Transform, and Load the Asana Data

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

Loading Asana Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData Python Connector for Asana, you can work with Asana 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 Python Connector for Asana to start building Python apps and scripts with connectivity to Asana 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.asana as mod

cnxn = mod.connect("OAuthClientId=YourClientId;OAuthClientSecret=YourClientSecret;CallbackURL='http://localhost:33333';InitiateOAuth=GETANDREFRESH;")

sql = "SELECT Id, WorkspaceId FROM projects WHERE Archived = 'true'"

table1 = etl.fromdb(cnxn,sql)

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

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