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AppsFlyer to BigQuery: Move Data in Minutes

AppsFlyer to BigQueryDo you have a lot of your attribution data on AppsFlyer? Are you looking for a way to move this data to a robust data warehouse like BigQuery to power in-depth analytics? In this blog, we are going to discuss two methods of moving data from AppsFlyer to BigQuery. However, before we dive in, let’s understand these two applications. 

What is AppsFlyer?

Appsflyer is a SaaS mobile analytics and attribution platform for mobile apps marketers. AppsFlyer provides you with conversion data of your user acquisition and retention campaigns. Advertisers login to their AppsFlyer dashboard and monitor mobile activity and the media source responsible for these activities. This information helps them to optimize their advertising budget.

AppsFlyer helps advertisers in decision making by offering these features: Retention reports, TV app ad attribution, and cohort analysis. 

What is BigQuery?

BigQuery is a data warehouse that is scalable, cost-effective and fast. It supports RESTful web services and works in conjunction with Google Cloud Storage. It is a fully-managed SaaS (Software as a Service) product that allows fast analysis of big data and has some built-in machine learning capabilities. Other features include managing data (creating and deleting tables based on JSON schema, importing CSV or JSON encoded data from Google Cloud), querying, integration and access control. Read more BigQuery here.

Methods to Load Data from Appsflyer to Google BigQuery

There are many ways of loading data from AppsFlyer to BigQuery. In this blog, we are going to look into two popular ways. In the end, you will have a good understanding of each of these two methods. This will help you to make the right decision based on your use case.

Method 1: Building Custom ETL Scripts

This method would require you to invest in the engineering team and bandwidth. The method involves; Understanding export APIs of the AppsFlyer data, building code to fetch data from Appsflyer, and loading data into BigQuery.Once the set up is completed, you would need to continuously monitor and maintain the infrastructure.

Method 2: Use Hevo Data, a Fully-Managed Data Integration Platform

Hevo’s fully managed capabilities make it easy to load data from AppsFlyer to BigQuery in real-time without writing any piece of code.

AppsFlyer to BigQuery: Extracting and loading data using custom ETL scripts

Step 1: Extracting data from AppsFlyer

AppsFlyer supports a broad range of APIs. This helps you to pull different data points in both raw and aggregated format. The raw format may include clicks, installs, and impressions while the aggregated formats may include impressions, clicks, summed and filtered by media source, country, etc. Read more about them here.

Before implementing the API call, you need to understand the use case and choose the API to implement based on that.

Note: Some APIs would only be available based on your AppsFlyer’s current plan.

In this article, we will fetch data from PULL API. This API allows the customers to get the CSV file of raw and aggregated data. Learn more about PULL APIs.

To fetch data, you need to make an API call that describes the data points to be returned. The user’s authorization key and the data range should be included in the API call. The date range specifies the data to be extracted. To fetch more parameters about the source, currency and other specific fields, include more parameters in the API call. 

An example of a PULL API is shown below. 

https://hq.appsflyer.com/export/master_report/v4?api_token=[api_token]&app_id=[app_id]&from=[from_date]&to=[to_date]&groupings=[list]&kpis=[list] 

Each successful API request will return a CSV file. Import the CSV data into BigQuery. 

Step 2: Loading data into Google BigQuery

An introduction to loading data into BigQuery is provided by GCP (Google Cloud Platform). You can find it here. To upload data, use the bq tool particularly the bq load command. The syntax for bq command is documented here

Supply the table or a partition schema or use schema auto-detection for the supported data formats. To load all your tables and data into BigQuery, iterate the above process until all the data is loaded.

Limitations and challenges of loading data from Appsflyer to BigQuery using ETL scripts: 

  1. Real-time access of AppsFlyer data:  So far, you have created a program that extracts and loads data from AppsFlyer to BigQuery. However, there is a challenge of loading new and updated data into the warehouse. You may decide to replicate data when a new and updated record is created (using a cron job or an equivalent for it) but this process is resource-intensive and slows down the operation. 
  2. Infrastructure Maintenance: Many things may go wrong when moving data from AppsFlyer to BigQuery. For example, updating of AppsFlyer API. This will cause the data flow to stop resulting in severe data loss. Therefore, a team will be required to continuously monitor and maintain the infrastructure. 
  3. Data Transformation: If you need clean, transform, and enrich data, the above method would not support this. You would need you to write more code.

AppsFlyer to BigQuery: Using Hevo Data

Loading data into BigQuery using Hevo (14-day free trial) is easier, reliable and fast. Hevo is a no-code automated data pipeline platform that solves all the challenges described above. You move data from AppsFlyer in just two steps without writing any piece of code. 

  1. You authenticate and connect Appsflyer Data Source
  2. You configure the Redshift Data warehouse where you want to load the data

Here are more reasons to try Hevo:

  1. Hevo requires minimal setup – Since it is fully managed, a minimal effort and bandwidth are required to set up.
  2. No data loss – The Hevo architecture is fault-tolerant. This allows easier transfer of data from AppsFlyer to BigQuery without data loss.  
  3. Out of the box data Integrations – Hevo transfers data from other different sources such as  Cloud Applications – Sales and Marketing Tools, Databases, Analytics Apps into BigQuery. So, you don’t have to worry about your growing data needs since you will always have a reliable partner.  
  4. Automatic detection and mapping of data schema – Incoming data from AppsFlyer is scanned automatically. Any changes detected are handled seamlessly and incorporated into BigQuery.
  5. Exceptional support – Hevo provides 24×7 Technical support through emails and chats. 

Now that you’ve understood the two popular methods of moving data from AppsFlyer to BigQuery, let us know your thoughts in the comments.

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