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First, sign in to your Lever account and navigate to the API settings. Generate an API key by creating a new API credential. Ensure that you have sufficient permissions set for the API key to access the data you need, such as candidates, jobs, and opportunities.
Use the Lever API to fetch the desired data. You can use HTTP GET requests to endpoints like `/candidates` to retrieve candidate data. Leverage tools like `curl` or create a script using programming languages like Python or JavaScript. Make sure to handle pagination and rate limits as per Lever's API documentation.
Once you have retrieved the data, transform it into a format suitable for Google Pub/Sub. Typically, this involves converting the data into JSON format. Ensure that the JSON structure is valid and contains all the necessary fields you intend to publish.
If you haven't already, create a Google Cloud project. Go to the Google Cloud Console, create a new project, and enable the Pub/Sub API. This setup will allow you to create topics and publish messages.
Within your Google Cloud project, navigate to the Pub/Sub section and create a new topic. This topic will serve as the endpoint where your Lever data will be published. Name your topic appropriately to keep track of the data source.
Set up authentication to interact with Google Cloud services programmatically. Download a service account key in JSON format from the Google Cloud Console and set the environment variable `GOOGLE_APPLICATION_CREDENTIALS` to point to this file. This allows your script to authenticate with Google Cloud using the service account.
Write a script to publish the transformed data to the Pub/Sub topic. Use the Google Cloud client library for your preferred programming language (e.g., Python, Node.js). The script should read the JSON data and publish it as messages to the Pub/Sub topic. Ensure to handle any errors and confirm successful publishing by checking the Pub/Sub topic for new messages.
By following these steps, you can manually move data from Lever Hiring to Google Pub/Sub without relying on third-party connectors 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:
