Urban Airship to QuickSight

This page provides you with instructions on how to extract data from Urban Airship and analyze it in Amazon QuickSight. (If the mechanics of extracting data from Urban Airship 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 Urban Airship?

Urban Airship is a cloud-based mobile marketing platform that aims to improve customer engagement.

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 Urban Airship

Urban Airship offers a REST API that lets you access data stored in the system. The API exposes 16 endpoints, including channels, segments, and schedules. To get information about static lists, for example, you could call GET /api/lists with an optional type parameter, in which you can specify user, lifecycle, or all lists.

Sample Urban Airship data

The Urban Airship API returns data in JSON format. For example, the results of a call to retrieve lists might look like this.

{
   "ok" : true,
   "lists" : [
      {
         "name" : "platinum_members",
         "description" : "loyalty program platinum members",
         "extra" : { "key" : "value" },
         "created" : "2017-08-08T20:41:06",
         "last_modified" : "2017-11-01T18:00:27",
         "channel_count": 3145,
         "status": "ready"
      },
      {
         "name": "gold_members",
         "description": "loyalty program gold member",
         "extra": { "key": "value" },
         "created": "2017-08-08T20:41:06",
         "last_updated": "2017-11-01T18:00:27",
         "channel_count": 678,
         "status": "ready"
      }
   ]
}

Preparing Urban Airship 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. Urban Airship'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. This means 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 Urban Airship 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.

Instead, identify key fields that your script can use to bookmark its progression through the data and use to pick up where it left off as it looks for updated data. Auto-incrementing fields such as updated_at or created_at work best for this. When you've built in this functionality, you can set up your script as a cron job or continuous loop to get new data as it appears in Urban Airship.

And remember, as with any code, once you write it, you have to maintain it. If Urban Airship modifies its API, or the API sends a field with a datatype your code doesn't recognize, you may have to modify the script. If your users want slightly different information, you definitely will have to.

From Urban Airship to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing Urban Airship 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 Urban Airship to Redshift, Urban Airship to BigQuery, Urban Airship to Azure Synapse Analytics, Urban Airship to PostgreSQL, Urban Airship to Panoply, and Urban Airship 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 Urban Airship with Amazon QuickSight. With just a few clicks, Stitch starts extracting your Urban Airship 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.