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Begin by accessing your Lever Hiring account. Use the Lever API to extract the necessary data. You can authenticate via OAuth or API keys, depending on your setup. Once authenticated, make API requests to endpoints that provide the data you need, such as candidates, job postings, or interview stages. Save the extracted data in a structured format like JSON or CSV for further processing.
After extracting the data, review it to identify any inconsistencies or errors. Clean the data by removing duplicates, handling missing values, and correcting any inaccuracies. Structure the data in a way that aligns with your intended usage in Weaviate, ensuring that you maintain data integrity and consistency.
Before importing data into Weaviate, define a schema that represents the data structure in Weaviate. This schema should include classes and properties that correspond to the data fields extracted from Lever. Document the relationships and data types to ensure that the data is correctly mapped for semantic search capabilities in Weaviate.
Set up your Weaviate instance by configuring the necessary resources, such as memory and storage, based on the volume of data you plan to import. Ensure that your Weaviate instance is running and accessible through its API. If necessary, create API keys or other authentication methods to secure access to your Weaviate environment.
Transform the structured data into a format compatible with Weaviate's import requirements. This involves converting data into JSON-LD format, which is used by Weaviate to understand the semantic context of the data. Ensure that all attributes and relationships defined in your Weaviate schema are correctly represented in the JSON-LD files.
Use Weaviate's RESTful API to import the transformed JSON-LD data into your Weaviate instance. This can be done by making POST requests to the appropriate endpoints for each class defined in your schema. Monitor the import process for any errors or issues, and verify that the data is accurately reflected in Weaviate after the import is complete.
Once the data import is complete, verify the integrity and accuracy of the data within Weaviate. Conduct tests to ensure that the data is correctly indexed and that semantic searches yield expected results. Use Weaviate's querying capabilities to validate that relationships and properties are functioning as intended. Adjust the data or schema as necessary based on your verification findings.
By following these steps, you can successfully move data from Lever Hiring to Weaviate 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:
