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Begin by setting up your AWS environment. Ensure you have an AWS account and access to necessary services such as S3, IAM, and AWS Glue. Create an S3 bucket where you intend to store the data from Lever. Set appropriate permissions for your bucket to allow data writing from your environment.
Log in to your Lever account and navigate to the API section. Generate an API token that will allow you to access the data you need. Note that you will need administrative permissions to create this token. Keep this token secure as it will be used to authenticate API requests.
Determine the specific data you want to move from Lever to S3. Lever provides various APIs for different types of data (e.g., candidates, jobs, interviews). Review Lever's API documentation to understand the endpoints and data schemas that are relevant to your needs.
Write a script in a programming language like Python to extract data from Lever using the API token. Use libraries such as `requests` to make API calls to Lever. Paginate through results if necessary and format the data as needed (e.g., JSON or CSV) for storage in S3.
Ensure the extracted data is in a format suitable for storage in S3. If you're storing the data as JSON or CSV, verify that it adheres to a consistent schema. Consider compressing large datasets to save space and speed up the transfer process.
Using AWS SDKs (such as Boto3 for Python), write a script to upload the transformed data files to your S3 bucket. Ensure the S3 bucket policies allow the upload from your environment. Test with a small dataset to confirm the upload process works correctly.
Configure AWS Glue to process the data in your S3 bucket. Create a Glue Crawler to automatically detect the schema of your data and populate the Glue Data Catalog. Once the crawler has run, you can create Glue jobs to transform or query the data as needed, using AWS Glue's ETL 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.
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:
