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Start by accessing the Workable API to extract your data. You'll need to authenticate using your API key, which you can find in your Workable account settings. Use HTTP GET requests to retrieve data, such as candidates or job postings, in a JSON format. Ensure you understand the API endpoints and the structure of the data you need.
Prepare your PostgreSQL database to receive the data. This involves creating tables that match the structure of the data you are extracting from Workable. Use SQL commands like `CREATE TABLE` to define your schema, ensuring that data types in PostgreSQL match those of the incoming data.
Once you have your data in JSON format, transform it to match the PostgreSQL table schema. This step may involve data cleaning and normalization. Use scripting languages like Python to parse the JSON data and rearrange it in a tabular format compatible with PostgreSQL.
Ensure you have the necessary tools to interact with both Workable API and PostgreSQL. Python, along with libraries like `requests` for API interaction and `psycopg2` or `SQLAlchemy` for PostgreSQL connectivity, will be useful. These tools will help you automate data extraction and insertion.
Develop a script in Python that automates the process of data extraction from Workable and insertion into PostgreSQL. The script should:
- Fetch data from the Workable API.
- Transform and map JSON data to the PostgreSQL schema.
- Establish a connection to the PostgreSQL database.
- Execute SQL `INSERT` commands to populate the tables.
Execute your script to transfer data from Workable to PostgreSQL. Monitor the process to ensure data is being transferred correctly, and check for any errors or exceptions that may arise during execution. Validate the data in PostgreSQL to ensure accuracy and completeness.
To keep your PostgreSQL database updated with the latest data from Workable, consider scheduling your data transfer script to run at regular intervals. Use cron jobs on Unix-based systems or Task Scheduler on Windows to automate this process, ensuring your database remains current without manual intervention.
By following these steps, you can efficiently move data from Workable to a PostgreSQL database 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?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
