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Begin by accessing the Lever Hiring API. You'll need to authenticate using API keys or tokens provided by Lever. Make API calls to extract the necessary data, such as candidate information, job postings, and interview feedback. Ensure that you respect API rate limits and handle paginated responses if the data set is large.
Once you have the raw data from Lever, convert it into a format suitable for Redshift. This typically involves transforming JSON or XML data into CSV format, which is easily ingested by Redshift. During this transformation, clean the data by handling null values, ensuring consistent data types, and removing any unnecessary fields.
Before loading the data into Redshift, set up an Amazon S3 bucket to temporarily store the transformed CSV files. This step is crucial, as Redshift can directly copy data from S3. Ensure your S3 bucket is in the same AWS region as your Redshift cluster for optimal performance and avoid unnecessary data transfer costs.
Use AWS CLI, SDK, or web interface to upload your transformed CSV files to the S3 bucket. Organize the files in a structured manner, perhaps by date or data type, to make them easy to manage and retrieve later. Ensure that the appropriate permissions are set on the S3 bucket to allow Redshift to access it.
Ensure that your Amazon Redshift cluster is set up and accessible. Create the necessary database and tables in Redshift to match the structure of your transformed data. Define the schema precisely, considering data types and constraints to ensure data integrity upon loading.
Use the Redshift `COPY` command to load data from S3 into your Redshift tables. The `COPY` command is designed for high-performance data ingestion and supports various options that you can use to handle different data formats and compression types. Monitor the loading process for any errors and adjust your data transformation process if necessary.
After loading the data, perform thorough checks to ensure data integrity and completeness. Run queries to verify that all data has been transferred correctly and that no records are missing. Compare the data against the original data in Lever to confirm accuracy. Address any discrepancies by adjusting your extraction or transformation processes.
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:
