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First, you need to extract the data from Workable. This can typically be done by using the export feature within the Workable platform. Navigate to the reports or data section and choose the option to export your desired dataset, such as candidate information or job listings. Export the data in a common format like CSV or JSON, which can be easily processed and imported into ClickHouse.
Once exported, review the data to ensure it contains all necessary fields and is in a clean, structured format. If needed, use a scripting language like Python or tools like Excel to clean and format the data. Ensure that the data types are consistent and aligned with the schema you plan to use in ClickHouse, as this will facilitate smoother importing.
Before importing, ensure your ClickHouse environment is correctly set up. This involves installing ClickHouse on your server if it isn't already installed. You can use the official ClickHouse installation guide to set it up on your preferred operating system. Verify that you have the necessary permissions to create databases and tables.
Using the ClickHouse client or your preferred SQL editor, define the table schema in ClickHouse that matches the structure of your Workable data. Use the `CREATE TABLE` statement to specify the data types for each column, ensuring compatibility with the data you plan to import. This step is crucial for avoiding errors during the import process.
Transfer your prepared CSV or JSON file to the server where ClickHouse is installed. You can use command-line tools like `scp` (for secure copy) to move the file from your local machine to the server. Ensure the file is placed in a directory with appropriate permissions so it can be accessed during the import process.
Utilize ClickHouse's `INSERT INTO ... FROM INFILE` command to begin importing the data. Connect to the ClickHouse server using the command-line client and execute the command, specifying the path to your CSV or JSON file. Make sure to include format specifications if necessary, like `FORMAT CSV` or `FORMAT JSONEachRow`.
After importing, perform checks to verify that all data has been successfully and accurately imported into ClickHouse. Use SQL queries to compare record counts, check for null values, and validate data integrity against the Workable export. This step ensures that the data transfer was successful and that the data can be reliably used for analysis or reporting.
By following these steps, you can manually transfer data from Workable to a ClickHouse warehouse 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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