How to load data from AppsFlyer to BigQuery

Learn how to use Airbyte to synchronize your AppsFlyer data into BigQuery within minutes.

Try Airbyte CloudManual method (the hard way)

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Bespoke pipelines are:
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Furthermore, you will need to build and maintain Y x Z pipelines with Y sources and Z destinations to cover all your needs.

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a AppsFlyer connector in Airbyte

Connect to or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up BigQuery for your extracted AppsFlyer data

Select where you want to import data from your source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the AppsFlyer to BigQuery in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.
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Check out our interactive demo and our how-to videos to learn how you can sync data from any source to any destination.

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Tech Lead at Symend

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Operational Intelligence Manager

"With Airbyte, we could just push a few buttons, allow API access, and bring all the data into Google BigQuery. By blending all the different marketing data sources, we can gain valuable insights."

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How to Sync to Manually

Step 1: Set Up Google Cloud Storage

  1. Create a Google Cloud project if you don’t already have one.
  2. Enable billing for your project.
  3. Create a Google Cloud Storage bucket where you will store the exported data from AppsFlyer.

Step 2: Configure AppsFlyer Data Locker

  1. Log in to your AppsFlyer account and navigate to the Data Locker setup.
  2. Configure Data Locker to export data to your Google Cloud Storage bucket. You will need to provide your bucket details and configure the data export schedule.

Step 3: Export Data from AppsFlyer to Google Cloud Storage

  1. Once Data Locker is configured, AppsFlyer will start exporting data to your Google Cloud Storage bucket according to your schedule.
  2. You will find the data in files, typically gzipped CSVs, in your bucket.

Step 4: Set Up Google Cloud Functions

  1. Enable the Cloud Functions API for your project.
  2. Create a new Cloud Function that will trigger on the creation of new files in your Google Cloud Storage bucket.
  3. Write a function in your preferred runtime (Node.js, Python, etc.) that will:
    • Trigger when new data is uploaded to your Cloud Storage bucket.
    • Unzip the file if necessary.
    • Parse the CSV data.
    • Stream the data into BigQuery.

Step 5: Set Up BigQuery

  1. Enable the BigQuery API for your project.
  2. Create a dataset in BigQuery where you will store your AppsFlyer data.
  3. Design your table schema based on the AppsFlyer data you will be importing.

Step 6: Write the Cloud Function Code

  1. Implement the code to read the CSV file from the Cloud Storage bucket.
  2. Parse the CSV data into a format that BigQuery can ingest.
  3. Use the BigQuery client library to insert data into your BigQuery table.

Step 7: Deploy the Cloud Function

  1. After writing and testing your function locally, deploy it to Google Cloud Functions.
  2. Ensure that the function is set to trigger when new files are added to your Cloud Storage bucket.

Step 8: Monitor the Data Transfer

  1. Check the Cloud Function logs to make sure it’s being triggered correctly and that data is being processed without errors.
  2. Verify that data is appearing in your BigQuery table as expected.

Step 9: Set Up Scheduling (Optional)

If you need more control over when the data is transferred, you can set up a Cloud Scheduler job to trigger your Cloud Function at specific times.

Step 10: Maintain and Optimize

  1. Monitor your setup to ensure it continues to work as expected.
  2. Optimize costs by managing the frequency of data transfers and the processing resources used.
  3. Update your Cloud Function and BigQuery schema as needed if your AppsFlyer data changes.