How to load data from Kyriba to Databricks Lakehouse
Learn how to use Airbyte to synchronize your Kyriba data into Databricks Lakehouse within minutes.
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How to Sync to Manually
Step 1: Export Data from Kyriba
Begin by accessing the Kyriba platform and exporting the required data. Kyriba typically allows data exports in various formats such as CSV, Excel, or XML. Choose a format that can be easily handled by your data processing tools. Ensure that the export contains all necessary data fields and is saved securely on your local system or a server.
Step 2: Set Up Secure File Transfer Protocol (SFTP) Server
Set up an SFTP server to securely transfer the exported files from Kyriba. SFTP is a protocol that provides secure file access, transfer, and management over a reliable data stream. Configure your SFTP server with the necessary security measures, such as SSH keys or user authentication, to ensure the safety of your data during transfer.
Step 3: Transfer Data to SFTP Server
Once your SFTP server is configured, upload the exported data files from Kyriba to the server. Use an SFTP client, such as WinSCP or FileZilla, to connect to your SFTP server and transfer the files. Verify that the files have been uploaded successfully and are accessible on the server.
Step 4: Configure Databricks Environment
Set up your Databricks environment to prepare for data ingestion. This includes creating a Databricks workspace and setting up the necessary clusters. Ensure that your Databricks workspace has access to the storage where the data will reside, such as AWS S3, Azure Blob Storage, or DBFS (Databricks File System).
Step 5: Access Data in Databricks
Within Databricks, access the files stored on your SFTP server. You can use libraries such as Paramiko in Python to connect to your SFTP server directly from a Databricks notebook, allowing you to download the files into a location accessible by Databricks, such as DBFS.
Step 6: Load Data into Databricks Lakehouse
Once the files are accessible in Databricks, load the data into your Lakehouse. Use Databricks' built-in functionalities, such as Spark's DataFrame API, to read the files from DBFS. Transform the data as needed to fit the schema of your Lakehouse, and write the transformed data to your Lakehouse storage.
Step 7: Validate and Transform Data
After loading the data, perform data validation to ensure accuracy and completeness. Use SQL queries or DataFrame operations to check for data consistency, missing values, or duplicates. Apply any necessary transformations to align the data with your analytics requirements. Once validated and transformed, your data is ready for analysis and reporting in the Databricks Lakehouse.
By following these steps, you can efficiently transfer data from Kyriba to Databricks Lakehouse without relying on third-party connectors or integrations.