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Begin by familiarizing yourself with the Lever Hiring API documentation. This includes understanding how to authenticate, access endpoints, and retrieve the data you need. Ensure you have the necessary API credentials and permissions to extract data.
Use the Lever Hiring API to fetch the data. Start by writing a script (using Python, for example) to make GET requests to the relevant API endpoints. Focus on extracting candidate data, job postings, applications, and any other necessary data objects. Store the retrieved data in a structured format such as JSON or CSV files.
Install Apache Iceberg within your desired environment. This could be on a local machine or a server. Ensure you have a compatible version of Apache Spark or another compute engine that supports Iceberg. Set up the necessary configurations to create and manage Iceberg tables.
Convert the extracted Lever Hiring data into a format compatible with Apache Iceberg. This typically involves transforming JSON or CSV data into Parquet or ORC files. Use a script to process and convert the data to these columnar formats, which are required for Iceberg tables.
Define a schema for your Iceberg table based on the structure of the data extracted from Lever Hiring. The schema should include all necessary fields, data types, and partitioning strategies if needed. Use this schema to create an Iceberg table using your compute engine.
Load the transformed data into the newly created Iceberg tables. Use your compute engine to write the data files (in Parquet or ORC format) into the Iceberg tables. Ensure that the data is correctly partitioned and matches the table schema.
After loading the data, run queries to validate that the data in Iceberg matches the original data from Lever Hiring. Check for data integrity, completeness, and accuracy. Ensure that all fields are correctly mapped and that there are no discrepancies between the source and the destination.
By following these steps, you can effectively move data from Lever Hiring to Apache Iceberg without relying on external 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?
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