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Start by reviewing the Workable API documentation to understand how to programmatically access data. Identify the endpoints needed for the data you want to extract, understand the authentication mechanism, and note any rate limits or pagination requirements.
Ensure you have an AWS account with necessary permissions to access AWS S3 and AWS Glue. Create an S3 bucket where you’ll store the data. Consider bucket naming conventions and set up appropriate IAM policies to ensure secure access.
Develop a script in Python or another language of your choice to extract data from Workable using their API. Use libraries like `requests` to handle HTTP requests. Ensure the script handles authentication, pagination, and error checking. Transform the data into a CSV or JSON format suitable for storage in S3.
Use the AWS SDK (Boto3 for Python) to upload the extracted data to your S3 bucket. The script should specify the target S3 bucket and object key. Implement error handling to ensure successful uploads and log any failed attempts for troubleshooting.
Set up an AWS Glue Crawler to catalog the data you’ve uploaded to S3. This will allow AWS Glue to automatically infer the schema of your data and create tables in the AWS Glue Data Catalog. Configure the crawler with the appropriate IAM role and specify the S3 path.
Create an AWS Glue ETL job to transform and process the data as needed. The Glue ETL job can read data from the Data Catalog tables created by the crawler and perform any transformations you require (e.g., cleaning, formatting). Write the processed data back to S3 or to another destination supported by Glue.
Automate the data extraction, uploading, and Glue processes using AWS Lambda or AWS Step Functions for orchestration. Set up CloudWatch Alarms to monitor the execution of your scripts and Glue jobs, ensuring any issues are promptly addressed. This will provide an automated and reliable pipeline from Workable to S3 via AWS Glue.
By following these steps, you can effectively move data from Workable to AWS S3 using AWS Glue without relying on third-party connectors.
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:
