How to load data from Primetric to Databricks Lakehouse

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

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Primetric 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 Primetric 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 Primetric 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 Primetric

Begin by exporting the data you need from Primetric. Navigate to the data section within your Primetric dashboard and identify the datasets you want to move. Use the export functionality to download the data in a common format such as CSV or JSON. Ensure the data is organized and clean to avoid issues during the import process.

Save the exported files in an organized manner on your local machine or a secure local storage solution. Ensure the files are easily accessible and named systematically to avoid confusion during the upload process. Check the file integrity and completeness of each dataset before proceeding.

Log in to your Databricks account and set up your workspace. If you haven’t already, create a new cluster in Databricks. Configure the cluster with appropriate settings that will handle the data size and processing needs. Ensure the cluster is started and ready for data import.

Use Databricks' web interface to upload your exported data files to the Databricks File System (DBFS). Navigate to the "Data" section, click on "Add Data", and choose "Upload File". Select your local files and upload them to a designated directory in the DBFS. Confirm the upload was successful by checking the file listings.

Using Databricks SQL or Spark, create tables that match the schema of your Primetric data. Open a new Notebook in Databricks and define the schema for each dataset. Use SQL commands like `CREATE TABLE` or Spark DataFrame API to define and create tables. Tailor the schema to match the structure of the exported files.

Load the uploaded data files into the newly created tables. Use SQL commands such as `COPY INTO` from Databricks SQL or Spark DataFrame functions like `spark.read.csv()` to read the data from DBFS and insert it into the corresponding tables. Ensure data types and structure align with your table definitions.

Once the data is loaded, perform a thorough verification to ensure its integrity and quality. Run SQL queries or use DataFrame operations to check row counts, data types, and sample records to confirm that the data in Databricks matches the original data from Primetric. Address any discrepancies by reviewing the data processing steps.

This guide allows you to manually move data from Primetric to Databricks Lakehouse without relying on third-party connectors or integrations, providing you full control over the process.