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Start by accessing the Lever Hiring API. You need API credentials, which you can obtain from the Lever developer portal. Ensure you have the necessary permissions to access the data you wish to move. Familiarize yourself with the API documentation to understand the endpoints and data formats.
On your local machine or server, set up a development environment with the necessary tools. Install Python, as it is a versatile language for handling HTTP requests and data processing. Additionally, install necessary libraries like `requests` for API interaction and `boto3` for AWS S3 operations.
Write a script using Python to fetch the data you need from the Lever API. Utilize the `requests` library to make HTTP GET requests to the API endpoints. Handle authentication by including your API key in the request headers. Parse the response data, which is typically in JSON format.
Once the data is fetched, process it as needed. This may involve filtering, cleaning, or transforming the data into a structure that suits your storage needs in S3. Convert the data into a CSV, JSON, or any other format that meets your requirements.
Configure your AWS credentials on the machine or server where the script is running. This can be done by setting up an AWS IAM user with programmatic access and the necessary permissions for S3 operations. Store the credentials in a configuration file or environment variables to allow `boto3` to authenticate.
Using the `boto3` library, write a script to upload the processed data to an S3 bucket. Create a new bucket if necessary and decide on the naming convention and folder structure. Use the `put_object` method to upload the data file to the specified bucket and key.
To ensure data is moved regularly, automate the script execution. Use tools like cron jobs on Linux or Task Scheduler on Windows to run the script at specified intervals. Ensure logging is set up to capture any errors or issues during the execution for troubleshooting.
By following these steps, you can effectively transfer data from Lever Hiring to S3 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:
