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.

Panoply can spin up a new Amazon Redshift instance in just a few clicks. Panoply's managed data warehouse service uses machine learning and natural language processing (NLP) to learn, model, and automate data management activities from source to analysis. It can import data with no schema, no modeling, and no configuration, and lets you use analysis, SQL, and visualization tools just as you would if you were creating a Redshift data warehouse on your own.

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.

Once you know all of the columns you want to insert, use the CREATE TABLE statement in Panoply's Redshift data warehouse to set up a table to receive all the data.

Next, migrate your data. It may seem like the easiest course would be to build INSERT statements to add data to your Redshift table row by row. That would be a mistake; Redshift isn't optimized for inserting data one row at a time. If you have a high volume of data to be inserted, a better approach is to copy the data into Amazon S3 and then use the COPY command to load it into Redshift.

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.

Panoply 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, Google BigQuery, PostgreSQL, Snowflake, or Microsoft Azure Synapse Analytics, which are RDBMSes that use similar SQL syntax. Others choose a data lake, like Amazon S3 or Delta Lake on Databricks. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To BigQuery, To Postgres, To Snowflake, To Azure SQL Data Warehouse, To S3, and To Delta Lake.