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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.
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.
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.
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.
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.
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.
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.
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.
The Lever Hire and Lever Nurture features allow leaders to scale and grow their people pipeline and build authentic and long-lasting relationships. The lever is a leading Talent Acquisition Suite that makes it easy for talent teams to reach their hiring goals and to connect companies with top talent. Lever hire is a complete talent acquisition suite that provides all the tools needed for businesses to discover and hire the best talents.
Lever Hiring's API provides access to a wide range of data related to the hiring process. 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 the job openings, including the job title, location, description, and requirements.
3. Interviews: Information about the interviews scheduled for the candidates, including the date, time, location, and interviewer details.
4. Offers: Details about the job offers made to the candidates, including the salary, benefits, and start date.
5. Users: Information about the users who have access to the Lever Hiring platform, including their name, email address, and role.
6. Teams: Details about the teams within the organization, including the team name, members, and roles.
7. Stages: Information about the different stages of the hiring process, including the names and descriptions of each stage.
8. Sources: Details about the sources from which the candidates have applied, including job boards, social media, and referrals.
Overall, Lever Hiring's API provides a comprehensive set of data that can be used to streamline the hiring process and improve the overall efficiency of the recruitment process.
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?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
