How to load data from Lever Hiring to Databricks Lakehouse
Learn how to use Airbyte to synchronize your Lever Hiring data into Databricks Lakehouse within minutes.
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How to Sync to Manually
Step 1: Export Data from Lever Hiring
Begin by exporting the required data from Lever Hiring. Navigate to the Lever Hiring dashboard, locate the data export feature, and export the data in a common format such as CSV or Excel. Ensure that you have the necessary permissions to export the data and that you export all relevant fields needed for analysis and reporting.
Step 2: Review and Clean Exported Data
Once you have the exported data, review it for completeness and accuracy. Check for any missing values, duplicates, or inconsistencies. Use tools like Excel or Google Sheets to clean the data, ensuring it is structured correctly for import into Databricks. This step is crucial to maintain data integrity and quality.
Step 3: Convert Data to a Suitable Format
After cleaning the data, convert it to a format that is compatible with Databricks. While Databricks supports multiple formats, converting your data to a Parquet or Delta format can optimize performance. You can use Python scripts or data processing tools like Pandas to achieve this conversion.
Step 4: Set Up Databricks Environment
Ensure that your Databricks Lakehouse environment is properly configured for data ingestion. Log into your Databricks account, create a new cluster if necessary, and set up a workspace where you will manage the data import process. Make sure you have the necessary permissions to create and manage resources in Databricks.
Step 5: Upload Data to Databricks File System (DBFS)
Use the Databricks web interface or the Databricks CLI to upload your converted data files to the Databricks File System (DBFS). DBFS acts as the intermediary storage layer for processing data in Databricks. Ensure that the files are uploaded to an accessible directory within DBFS.
Step 6: Create Tables in Databricks Lakehouse
With the data files uploaded to DBFS, the next step is to create tables in the Databricks Lakehouse. Use SQL commands within a Databricks notebook to define tables and load data from the DBFS files. For example, use the `CREATE TABLE` and `COPY INTO` SQL statements to create tables and populate them with the data.
Step 7: Verify and Validate Data Load
After loading the data into Databricks Lakehouse, conduct thorough verification and validation checks. Run queries to ensure that the data has been imported correctly and that there are no discrepancies between the source data and the data in Databricks. Validate data types, counts, and key metrics to ensure accuracy and consistency.
By following these steps, you can effectively move data from Lever Hiring to the Databricks Lakehouse without relying on third-party connectors or integrations, while maintaining data quality and integrity throughout the process.