AppsFlyer to Redshift

This page provides you with instructions on how to extract data from AppsFlyer and load it into Redshift. (If this manual process sounds onerous, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

What is AppsFlyer?

AppsFlyer is an attribution stack for mobile marketers. It lets businesses attribute every install of their apps to the marketing campaign and media source that drove that install. It also provides an analytics dashboard that shows which users engage with an app, how they use it, and how much revenue they generate.

Getting data out of AppsFlyer

AppsFlyer exposes data through its Pull API, which developers can use to extract information. Each API call, which is made in the form of an https query, must contain the user’s external API Authorization Key, as well as from and to dates that specify the date range of the data requested.

Additional parameters can request information like media source, currency, and specific fields. The parameters must be added to the https query – for example:


https://hq.appsflyer.com/export/com.greatapp/installs_report/v5?api_token=xxxx&from=2017-11-19%2001%3A30&to=2017-11-19%2013%3A30&category=standard&media_source=googleadwords_int&fields=country_code,city

Each successful API query returns a CSV file of data that you can use as an import source to your data warehouse. The query you use will determine what fields you receive.

Loading data into Redshift

Once you have identified all of the columns you will want to insert, you can use the CREATE TABLE statement in Redshift to create a table that can receive all of this data.

With a table built, it may seem like the easiest way to migrate your data (especially if there isn't much of it) is to build INSERT statements to add data to your Redshift table row by row. If you have any experience with SQL, this will be your gut reaction. But beware! Redshift isn't optimized for inserting data one row at a time. If you have a high volume of data to be inserted, you would be better off loading the data into Amazon S3 and then using the COPY command to load it into Redshift.

Keeping AppsFlyer 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 AppsFlyer.

And remember, as with any code, once you write it, you have to maintain it. If AppsFlyer modifies its API, or 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.

Other data warehouse options

Redshift is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Google BigQuery, PostgreSQL, or Snowflake, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To BigQuery, To Postgres, To Snowflake, and To Panoply.

Easier and faster alternatives

If all this sounds a bit overwhelming, don’t be alarmed. If you have all the skills necessary to go through this process, chances are building and maintaining a script like this isn’t a very high-leverage use of your time.

Thankfully, products like Stitch were built to solve this problem automatically. With just a few clicks, Stitch starts extracting your AppsFlyer data via the API, structuring it in a way that is optimized for analysis, and inserting that data into your Redshift data warehouse.