How to load data from Fullstory to Databricks Lakehouse

Learn how to use Airbyte to synchronize your Fullstory data into Databricks Lakehouse within minutes.

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Fullstory connector in Airbyte

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

Set up Databricks Lakehouse for your extracted Fullstory 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 Fullstory to Databricks Lakehouse 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: Export Data from FullStory

Begin by exporting the data from FullStory. Navigate to the FullStory dashboard and use the export feature to download your data. FullStory allows you to export data in CSV or JSON format, which is suitable for further processing. Make sure you have the necessary permissions and API access for data export.

Before importing data, ensure that your Databricks environment is set up. Create a new Databricks workspace if you haven't already. This includes setting up the cluster that will process your data. Ensure your environment has the necessary permissions and configurations to handle data import and storage.

Once you have exported your data from FullStory, you may need to clean or transform it. Use a programming language like Python or a tool like Excel to ensure the data is formatted correctly and ready for ingestion. Pay attention to data types and ensure the data schema aligns with your Lakehouse's schema design.

Upload the cleaned and prepared data files to a cloud storage service that your Databricks workspace can access, such as AWS S3, Azure Blob Storage, or Google Cloud Storage. This step involves using the web interface of the cloud provider or command-line tools to place your files in a designated bucket or container.

In Databricks, create a notebook to access the data stored in your chosen cloud storage. Use Spark commands within Databricks to read the data files. For example, if using AWS S3, you would configure access credentials and use Spark's `read` function to load the data into a DataFrame. Make sure to verify the data is read correctly.

Perform any necessary transformations on the data using Spark SQL or PySpark operations. This could include filtering, joining, or aggregating data to fit your analytical needs. Once the data is transformed, write it to the Databricks Lakehouse. Use Delta Lake format for efficient storage and querying, writing directly to a designated table or location.

After the data is loaded into the Lakehouse, perform validation checks to ensure data integrity and accuracy. Verify that the data in the Lakehouse matches your expectations and is accessible for analysis. To streamline future data movements, consider automating parts of the process using Databricks Jobs or scheduled notebooks that can handle regular data updates.