How to load data from Appfollow to BigQuery

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

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Appfollow 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 Appfollow 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 Appfollow 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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How to Sync to Manually

Step 1: Extract Data from AppFollow

Start by logging into your AppFollow account. Navigate to the reports or data section where you can access the data you need. Use AppFollow's export functionality to download the data as a CSV or Excel file. Ensure you have all the necessary fields and that the data is formatted correctly for your needs.

Open the downloaded file in a spreadsheet application like Microsoft Excel or Google Sheets. Clean and format the data to ensure consistency. Remove any unnecessary columns, handle missing values, and ensure the data types are correct (e.g., dates are formatted as dates, numbers are numbers). Save the cleaned data as a CSV file, as this format is compatible with BigQuery.

Go to the Google Cloud Platform Console (https://console.cloud.google.com/). If you don't have a project, create a new one by clicking on "Select a Project" and then "New Project." Name your project and note the Project ID, as you will need it later.

In the Google Cloud Console, navigate to BigQuery. Click the "Create Dataset" button. Provide a dataset ID and choose your data location. This dataset will serve as a container for your tables and should be named meaningfully to reflect the data it will contain.

Within your BigQuery dataset, click on "Create Table." For the source, select "Upload" and then choose the CSV file you prepared earlier. Configure the table schema manually or let BigQuery auto-detect it. Ensure that the data types match your CSV's content. Name your table and complete the creation process.

After uploading, run a few queries in the BigQuery console to ensure the data has been imported correctly. Check for any discrepancies in the number of rows and the integrity of the data types and values. This step is crucial to ensure that the data is accurate and usable for analysis.

To keep your data in BigQuery up-to-date, consider automating the export and import process. You can use Google Cloud SDK and a simple script to automate the export of data from AppFollow and the import into BigQuery. Schedule this script using a cron job or Google Cloud Scheduler to run at regular intervals, ensuring your data remains current without manual intervention.

By following these steps, you can effectively transfer and maintain your AppFollow data in BigQuery, enabling deeper analysis and insights without relying on third-party connectors.