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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.
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.
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.
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.
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.
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.
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.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
Recruitee is the collaborative hiring software that delivers a complete solution to help internal teams hire better together. As an Applicant Tracking System, it enables recruitment teams to easily manage the hiring process from start to finish while keeping hiring managers and colleagues as active participants. Recruitee is on a mission to empower teams with the best tech tools to hire better together. Its vision is to put collaboration at the core of hiring teams.
Recruitee's API provides access to a wide range of data related to recruitment and hiring processes. The following are the categories of data that can be accessed through the API:
1. Candidates: Information about candidates who have applied for a job, including their name, contact details, resume, and application status.
2. Jobs: Details about job openings, including the job title, description, location, and requirements.
3. Applications: Data related to the application process, such as the date and time of application, the source of the application, and the status of the application.
4. Users: Information about users who have access to the Recruitee account, including their name, email address, and role.
5. Teams: Details about teams within the organization, including the team name, members, and permissions.
6. Stages: Information about the different stages of the recruitment process, such as screening, interviewing, and hiring.
7. Tags: Data related to tags that can be assigned to candidates, jobs, and applications to help with organization and filtering.
8. Custom fields: Information about custom fields that can be added to candidates, jobs, and applications to capture additional data.
Overall, the Recruitee API provides a comprehensive set of data that can be used to streamline recruitment processes and improve hiring outcomes.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
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