How to load data from AppsFlyer to Databricks Lakehouse
Learn how to use Airbyte to synchronize your AppsFlyer data into Databricks Lakehouse within minutes.
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How to Sync to Manually
Step 1: Export Data from AppsFlyer
Begin by logging into your AppsFlyer account. Navigate to the dashboard where your desired data resides. Use the AppsFlyer API to export the data. You can utilize the Pull API to extract raw data reports. Ensure you have the necessary API permissions and follow the API documentation to structure your request correctly. Save the extracted data locally in a CSV or JSON format.
Step 2: Prepare the Data for Transfer
Once you have exported the data, inspect it for any necessary transformations or cleaning. Check for duplicates, null values, or data types that may require conversion. Use Python or another scripting language to process and clean the data if necessary. Ensure the data is structured in a way that is compatible with Databricks Lakehouse schemas.
Step 3: Set Up Databricks Environment
Access your Databricks account and create a new cluster if one isn’t already available. Ensure the cluster is running and has the necessary configurations to handle the incoming data. Install any required libraries or dependencies that might be necessary for loading data into Databricks from your local environment.
Step 4: Transfer Data to Cloud Storage
Before importing data into Databricks, upload the cleaned and prepared data to a cloud storage service that's accessible by Databricks, such as AWS S3, Azure Blob Storage, or Google Cloud Storage. Use the command-line interface or relevant SDKs of the cloud provider to upload your files securely.
Step 5: Access Cloud Storage from Databricks
In Databricks, mount the cloud storage location containing your data files. Use Databricks' built-in support for cloud storage services to establish a connection. This typically involves configuring a secure access path and using the appropriate credentials or access keys to authenticate the connection.
Step 6: Load Data into Databricks Lakehouse
With the data accessible from your cloud storage, use Databricks' data import features to load the data into your Lakehouse environment. Use Spark SQL or Databricks’ DataFrame API to read the data from the cloud storage path. Perform any additional data transformations or validations as required during the loading process.
Step 7: Verify Data Integrity and Structure
After loading the data, conduct a thorough verification to ensure that the data in Databricks Lakehouse matches the original data extracted from AppsFlyer. Run queries to check for data completeness, consistency, and integrity. Verify that all fields are properly mapped and that the data types are correct. Make any necessary adjustments or re-transformations if discrepancies are found.
By following these steps, you can effectively move your data from AppsFlyer to Databricks Lakehouse without relying on third-party connectors or integrations.