AppsFlyer to BigQuery: 2 Easy Methods

AppsFlyer is a leading Mobile Attribution and Marketing Analysis platform, which is used by companies to quantify the effectiveness of their marketing activities. It supports the Marketing teams in making decisions based on the analysis that AppsFlyer provides,

Google BigQuery Data Warehouse is popular for its Scalability and Simplicity when it comes to data storage. BigQuery allows you to store vast volumes of data securely and seamlessly. Now the questions that arise are: Do 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?

This article will discuss two methods of transferring data from AppsFlyer to BigQuery. However, it will first introduce these two applications and then elaborate on the steps by which the two methods of integrating AppsFlyer to BigQuery can be set up. Read along to know more about these two methods and decide which one will suit you the best.

Methods to Set up AppsFlyer to BigQuery Integration

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: Using an Automated Data Pipeline Platform to Set up AppsFlyer to BigQuery Integration

Hevo Data, a No-code Data Pipeline, helps you directly transfer data from AppsFlyer and 150+ other data sources to Data Warehouses such as BigQuery, Databases, BI tools, or a destination of your choice in a completely hassle-free & automated manner. Hevo is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.

Hevo Data takes care of all your data preprocessing needs and lets you focus on key business activities and draw a much powerful insight on how to generate more leads, retain customers, and take your business to new heights of profitability. It provides a consistent & reliable solution to manage data in real-time and always have analysis-ready data in your desired destination.

Loading data into BigQuery using Hevo 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 to BigQuery in the following two steps without writing any piece of code. 

• Authenticate Data Source: Authenticate and connect your AppsFlyer account as a data source.

To get more details about Authenticating AppsFlyer with Hevo Data visit here.

• Configure your Destination: Configure your Google BigQuery account as the destination.

To get more details about Configuring BigQuery with Hevo Data visit this link.

You now have a real-time pipeline for syncing data from AppsFlyer to BigQuery.

More reasons to try Hevo

• Simplicity: Hevo is easy to use and intuitive. Using Hevo ensures that your data is transferred in just a few clicks without any developer help.
• Minimal Setup: Setting up Hevo requires minimal effort on your end. This is because it is fully managed and completely automated.
• Reliable Data Load: Hevo has a fault-tolerant architecture which ensures that data loads are done reliably with minimal data loss
• Scalability: Hevo easily handles data from a wide array of free data sources including Mailchimp at any scale. Thus, it helps you scale your data infrastructure to meet your growing needs
• Real-time: Hevo allows you to gain real-time insights through its real-time architecture. This ensures that you can move your data instantly and without delays.

Method 2: Manual ETL Process to Set up AppsFlyer to BigQuery Integration

The Manual ETL process can be implemented by the following two steps:

• Step 1: Extracting Data from AppsFlyer using ETL Scripts
• Step 2: Loading Data into Google BigQuery

Step 1: Extracting Data from AppsFlyer using ETL Scripts

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 a media source, country, etc.

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

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

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 the 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 AppsFlyer BigQuery Data Transfer Process using ETL Scripts

• 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. 

• 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. 

• Data Transformation: If you need to clean, transform, and enrich data, the above method would not support this. You would need to write more code.

Conclusion

This article provided a detailed step-by-step guide of the two methods using which you can set up your AppsFlyer to BigQuery Integration. The first method involves writing executable scripts to set up the ETL process. It is a time-consuming method that will regularly require manual troubleshooting.

Visit our Website to Explore Hevo

If you want a hassle-free data transfer from AppsFlyer to BigQuery, you can try the second method. Hevo is the only real-time ELT No-code Data Pipeline platform that cost-effectively automates data pipelines that are flexible to your needs. With integration with 150+ Data Sources such as PostgreSQL, MySQL, and MS SQL Server, we help you not only export data from sources & load data to the destinations but also transform & enrich your data, & make it analysis-ready. The unique combination of features differentiates Hevo from its competitors, including Fivetran.

Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You can also have a look at our unbeatable pricing that will help you choose the right plan for your business needs!

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