AppsFlyer to BigQuery

This page provides you with instructions on how to extract data from AppsFlyer and load it into Google BigQuery. (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.

What is Google BigQuery?

Google BigQuery is a data warehouse that delivers super-fast results from SQL queries, which it accomplishes using a powerful engine dubbed Dremel. With BigQuery, there's no spinning up (and down) clusters of machines as you work with your data. With that said, it's clear why some claim that BigQuery prioritizes querying over administration. It's super fast, and that's the reason why most folks use it.

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:,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 Google BigQuery

Google Cloud Platform provides an introduction to loading data into BigQuery. Use the bq tool, and in particular the bq load command, to upload data. Its syntax is documented in the Quickstart guide for bq. You can supply the table or partition schema, or, for supported data formats, you can use schema auto-detection. Iterate through this process as many times as it takes to load all of your tables and table data into BigQuery.

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

BigQuery is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Amazon Redshift, 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 Redshift, 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 Google BigQuery data warehouse.