Asana to QuickSight

This page provides you with instructions on how to extract data from Asana and analyze it in Amazon QuickSight. (If the mechanics of extracting data from Asana seem too complex or difficult to maintain, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

What is Asana?

Asana is a work management platform that helps teams map out each step and organize all the details of their projects. Team members can assign work to each other, specify deadlines, and communicate about tasks within Asana. The software provides visual project management charts in the form of kanban boards, timelines (or Gantt charts), and calendars.

What is QuickSight?

Amazon QuickSight is the AWS business intelligence tool for creating dashboards and visualizations. Users are charged per session only for the time when they access dashboards or reports. QuickSight supports a variety of data sources, such as individual databases (Amazon Aurora, MariaDB, and Microsoft SQL Server), data warehouses (Amazon Redshift and Snowflake), and SaaS sources (Adobe Analytics, GitHub, and Salesforce), along with several common standard file formats.

Getting data out of Asana

Asana provides a RESTful API that lets developers retrieve data stored in the platform about tasks, projects, conversations, and more. For example, to get information about a particular project, you would call GET /projects/{project_gid}.

Sample Asana data

Here's an example of the kind of response you might see with a query like the one above.

{
  "data": {
    "notes": "Document all the things",
    "null": "...",
    "id": 1331,
    "gid": "1331",
    "resource_type": "project",
    "name": "Things to Document"
  }
}

Preparing Asana data

If you don't already have a data structure in which to store the data you retrieve, you'll have to create a schema for your data tables. Then, for each value in the response, you'll need to identify a predefined datatype (INTEGER, DATETIME, etc.) and build a table that can receive them. Asana's documentation should tell you what fields are provided by each endpoint, along with their corresponding datatypes.

Complicating things is the fact that the records retrieved from the source may not always be "flat" – some of the objects may actually be lists. In these cases you'll likely have to create additional tables to capture the unpredictable cardinality in each record.

Loading data into QuickSight

You must replicate data from your SaaS applications to a data warehouse (such as Redshift) before you can report on it using QuickSight. Once you specify a data source you want to connect to, you must specify a host name and port, database name, and username and password to get access to the data. You then choose the schema you want to work with, and a table within that schema. You can add additional tables by specifying them as new datasets from the main QuickSight page.

Using data in QuickSight

QuickSights provides both a visual report builder and the ability to use SQL to select, join, and sort data. QuickSight lets you combine visualizations into dashboards that you can share with others, and automatically generate and send reports via email.

Keeping Asana data up to date

At this point you've coded up a script or written a program to get the data you want and successfully moved it into your data warehouse. But how will you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow and resource-intensive.

The key is to build your script in such a way that it can identify incremental updates to your data. Thankfully, Asana's API results include fields like created_at that allow you to identify records that are new since your last update (or since the newest record you've copied). Once you've take new data into account, you can set your script up as a cron job or continuous loop to keep pulling down new data as it appears.

From Asana to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing Asana data in Amazon QuickSight is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites Asana to Redshift, Asana to BigQuery, Asana to Azure Synapse Analytics, Asana to PostgreSQL, Asana to Panoply, and Asana to Snowflake.

Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data automatically, making it easy to integrate Asana with Amazon QuickSight. With just a few clicks, Stitch starts extracting your Asana data, structuring it in a way that's optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Amazon QuickSight.