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Begin by accessing the Lever Hiring API. You will need an API key from Lever, which can be obtained from the Lever Developer Portal under the API credentials section. Ensure you have the necessary permissions to access the data you need.
Before moving data, ensure your Google Firestore database is set up. Go to the Firebase Console, create a new project if necessary, and activate Firestore by choosing between Firestore in Native Mode or Datastore Mode based on your needs.
Use the Lever API to extract the data. Write a script in Python, Node.js, or your preferred language to send HTTP GET requests to the Lever API endpoints. For instance, use endpoints like `/candidates` to fetch candidate information. Parse the JSON responses and handle pagination as required.
Once data is extracted, transform it into a structure that matches your Firestore database design. This might involve restructuring JSON objects or flattening nested data. Ensure that the transformed data adheres to Firestore's document-based structure.
Set up authentication to securely interact with your Firestore database. This involves using Firebase Admin SDK. Install it in your script environment and initialize it with your service account credentials. This will allow your script to perform read/write operations on Firestore.
With data transformed and authentication set up, write the data to Firestore. Use batch writes to improve efficiency and handle any potential errors. Ensure that each document is placed in the correct collection and has a unique identifier.
After migrating the data, verify its integrity by reviewing a sample of records in Firestore to ensure they match the source data from Lever. Check for completeness and accuracy, and handle any discrepancies by re-extracting or re-transforming the affected records.
By following these steps, you can manually move data from Lever Hiring to Google Firestore without relying on third-party tools or integrations.
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:
