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Begin by logging into your Workable account. Navigate to the reports or data export section, and select the data set you wish to export. Most platforms, including Workable, offer an option to export data in CSV format. Export the data and download the CSV file to your local machine.
Open the CSV file in a spreadsheet editor like Microsoft Excel or Google Sheets. Review the data for any inconsistencies, such as missing values or incorrect data types. Ensure the column names are clear and suitable for use as field names in BigQuery (avoid spaces and special characters).
If you haven’t already, set up a Google Cloud Platform account. Navigate to the Google Cloud Console, create a new project, and ensure that BigQuery is enabled for your project. If BigQuery is not enabled, you can do so by navigating to the 'API & Services' section and enabling BigQuery API.
In the Google Cloud Console, go to the BigQuery section. Click on your project name in the Explorer panel and select "Create Dataset." Provide a name for your dataset and configure any location or expiration settings as needed. This dataset will serve as a container for your tables.
Ensure your CSV file is in a format compatible with BigQuery. BigQuery can directly import CSV files, but they must adhere to specific formatting, such as using UTF-8 encoding. Check your CSV file’s encoding and update it if necessary using a text editor or tool that supports encoding changes.
In the BigQuery console, click on your dataset and select "Create Table." Choose "Upload" as the source and select your CSV file. Configure the file format as "CSV" and customize any schema options, like field names and data types. Click "Create Table" to start the import process. Monitor the job status to ensure the data is imported successfully.
Once the import process is complete, navigate to your dataset and select the new table. Use the BigQuery SQL workspace to run some basic queries to verify that the data has been imported correctly. Check for correct data types, field names, and data integrity. This validation ensures that your data is ready for analysis or further processing within BigQuery.
By following these steps, you will successfully move data from Workable to BigQuery 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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