How to load data from Recruitee to Databricks Lakehouse
Learn how to use Airbyte to synchronize your Recruitee data into Databricks Lakehouse within minutes.
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How to Sync to Manually
Step 1: Export Data from Recruitee
Start by logging into your Recruitee account. Navigate to the data or reports section where you can access the information you need. Use the export functionality available in Recruitee to download the data in a CSV or Excel format. Ensure that you export all the necessary fields and records relevant to your analysis or storage needs.
Step 2: Prepare the Data for Transfer
After downloading the data, review the file to ensure all necessary data has been correctly exported. Clean up the data by removing any unnecessary columns, handling missing values, and verifying data integrity. Save the cleaned data in a CSV format, as this is a common format that Databricks can easily ingest.
Step 3: Set Up Databricks Environment
Access your Databricks account and create a new Databricks Lakehouse or use an existing one. Ensure that you have the necessary permissions to create tables and upload data. Set up any clusters you may need for processing the data once it’s uploaded.
Step 4: Upload Data to Databricks File System (DBFS)
Use the Databricks web interface or command-line interface to upload your CSV file to the Databricks File System (DBFS). In the Databricks web UI, navigate to the "Data" tab, select "Add Data," and choose "Upload File." Select your CSV file from your local system and upload it to DBFS.
Step 5: Create a Table in Databricks
Once the file is uploaded to DBFS, you need to create a table in Databricks to hold the data. Use a Databricks notebook to run a command to create a table. For example:
```sql
CREATE TABLE recruitee_data USING CSV OPTIONS (path '/dbfs/path/to/your/file.csv', header 'true', inferSchema 'true');
```
This command creates a table called `recruitee_data` using the CSV file you uploaded.
Step 6: Verify Data Integrity
After creating the table, verify that the data has been correctly ingested into Databricks. Run SQL queries to check the number of records and ensure that data types are correctly assigned. This step is crucial to ensure that the data is intact and usable for further analysis or processing.
Step 7: Process and Analyze the Data
With the data now stored in Databricks Lakehouse, you can proceed to process and analyze the data using SQL or Databricks' workspace capabilities. You can join this data with other datasets, perform transformations, and run analytics to derive insights from the Recruitee data.
By following these steps, you can successfully move data from Recruitee to Databricks Lakehouse manually, enabling you to take advantage of Databricks' powerful analytics and processing capabilities.