How to load data from Vitally to Databricks Lakehouse

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

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Vitally 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 Vitally 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 Vitally 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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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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How to Sync to Manually

Step 1: Export Data from Vitally

Begin by exporting the data you need from Vitally. Access your Vitally dashboard and navigate to the data export section. Choose the datasets you want to export and download them in a format that is compatible with Databricks, such as CSV or JSON.

Ensure your Databricks environment is properly set up. Log into your Databricks account and create or access a workspace. Set up a cluster if you haven’t already. This cluster will be used for data processing and transformation tasks.

Use the Databricks web interface to upload the exported data files to the Databricks File System (DBFS). Navigate to the "Data" tab in your Databricks workspace, select "DBFS", and upload your CSV or JSON files to an appropriate directory.

Create a new notebook within your Databricks workspace to handle data processing tasks. Choose your preferred language (e.g., Python, Scala) and prepare the notebook to read and process the uploaded files.

Write code in your Databricks notebook to read the data files from DBFS into DataFrames. For example, if you are using Python with Spark, use the `spark.read.csv()` or `spark.read.json()` functions to load your data into DataFrames. Specify any necessary options like headers or infer schema.

Perform any necessary transformations or cleaning operations on the DataFrames. This might include removing duplicates, handling missing values, or converting data types. Use Spark SQL or DataFrame operations to achieve this, ensuring the data is prepared for analysis or further processing.

Finally, save the processed DataFrames to the Databricks Lakehouse. Use the `write` method to specify the format (e.g., Delta Lake format) and the destination path in the Lakehouse. Ensure that the data is partitioned and optimized for efficient querying and storage.

By following these steps, you can efficiently migrate data from Vitally to a Databricks Lakehouse without the need for third-party connectors or integrations.