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Begin by familiarizing yourself with the Workable API documentation. Understand the available endpoints, authentication methods, and data formats. This will give you a clear idea of how to extract the data you need from Workable.
Write a script in a language of your choice (such as Python, JavaScript, or Ruby) to interact with the Workable API. Use the API key provided by Workable to authenticate your requests. Test your script to ensure it can successfully retrieve the desired data.
Once data is retrieved from Workable, format it into a structure that is suitable for Google Pub/Sub, typically JSON. Ensure the structure adheres to any schema you plan to use later in Pub/Sub or subsequent processing stages.
Install and configure the Google Cloud SDK on your local machine. Use the SDK to authenticate and set up your working environment for interacting with Google Cloud services, specifically Pub/Sub.
In your Google Cloud Platform (GCP) project, navigate to Pub/Sub and create a new topic. This topic will be the endpoint where your data from Workable will be published. Note the topic name as you will need it in your script.
Extend your script to publish the formatted data to the Google Pub/Sub topic. Use the Pub/Sub client library in your chosen programming language to send messages. Ensure that your script includes error handling to manage any issues during the publishing process.
Test the entire process end-to-end to ensure data is accurately extracted from Workable and published to Pub/Sub. Once verified, set up a cron job or use a cloud function to automate the script execution at desired intervals, ensuring continuous data flow from Workable to Pub/Sub.
By following these steps, you can effectively move data from Workable to Google Pub/Sub 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.
Workable is a cloud-based recruitment software that helps businesses streamline their hiring process. It offers a range of tools to help companies manage job postings, applicant tracking, candidate communication, and interview scheduling. Workable also provides features such as resume parsing, candidate scoring, and background checks to help businesses make informed hiring decisions. The platform integrates with popular job boards and social media sites, making it easy for companies to reach a wider pool of candidates. Workable is designed to be user-friendly and customizable, allowing businesses to tailor the software to their specific needs.
Workable's API provides access to a wide range of data related to recruitment and hiring processes. The following are the categories of data that can be accessed through Workable's API:
1. Candidates: Information about candidates who have applied for a job, including their name, contact details, resume, cover letter, and application status.
2. Jobs: Details about the job openings, including the job title, description, location, salary, and hiring manager.
3. Hiring pipeline: Information about the hiring process, including the stages of the pipeline, the number of candidates in each stage, and the time spent in each stage.
4. Interviews: Details about the interviews conducted with candidates, including the date, time, location, interviewer, and feedback.
5. Reports: Analytics and insights related to recruitment and hiring processes, including the number of applications, the time to hire, and the cost per hire.
6. Integrations: Information about the third-party tools and services integrated with Workable, including the ATS, HRIS, and job boards.
Overall, Workable's API provides a comprehensive set of data that can help organizations streamline their recruitment and hiring processes and make data-driven decisions.
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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