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Begin by logging into your Workable account. Navigate to the data or reports section where you can export the data you need. Workable typically allows you to export data in CSV format. Choose the appropriate data fields and export the data, saving it as a CSV file on your local machine.
Log in to your AWS Management Console. Navigate to the S3 service and create a new bucket to store your Workable data. Choose a unique bucket name and select the appropriate region. Configure permissions to ensure that your IAM user has access to upload files to this bucket.
Install and configure the AWS Command Line Interface (CLI) on your local machine if not already installed. Use the `aws configure` command to set up your AWS credentials (Access Key ID, Secret Access Key, Region, and Output Format). Ensure that the IAM user associated with these credentials has the necessary permissions for S3 operations.
Use the AWS CLI to upload your exported CSV file to the S3 bucket. The command will look something like this: `aws s3 cp /path/to/your/file.csv s3://your-bucket-name/`. Verify the upload by checking the S3 bucket through the AWS Management Console.
In the AWS Management Console, navigate to the Glue service. Create a new crawler to catalog the data in your S3 bucket. Configure the crawler to point to the S3 bucket where your CSV file is stored, and create a new database or select an existing one to store the metadata. Run the crawler to populate the AWS Glue Data Catalog with your data schema.
Use Amazon Athena to query the data stored in your S3 bucket. Navigate to the Athena service in the AWS Management Console, and ensure it is configured to use the Glue Data Catalog. Write SQL queries to explore and analyze your data directly. Athena allows querying data in S3 using SQL without the need for loading it into a database.
To automate future data transfers, consider creating a script that exports the data from Workable, uploads it to S3, and triggers the Glue crawler. You can use AWS Lambda to execute this script based on a schedule or event, ensuring that your data lake stays up-to-date without manual intervention.
By following these steps, you will successfully move data from Workable to an AWS Data Lake 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:
