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Begin by familiarizing yourself with the data structure in Workable. Identify what data you need to export, such as candidate information, job postings, and application statuses. Note the format, such as JSON or CSV, in which Workable exports data.
Use Workable's built-in export functionality to extract the required data. Typically, this can be done via their user interface by selecting the data types and date range you wish to export. Ensure the data is exported in a format that is easy to work with, such as CSV.
Set up your TiDB environment if it's not already configured. Ensure that you have access to the TiDB cluster and have the necessary privileges to create tables and insert data. Verify that the TiDB instance is running and accessible.
Based on the data structure from Workable, create corresponding tables in TiDB. Use the exported data to define the schema of each table, ensuring that data types and constraints are compatible with those in Workable.
If necessary, transform the exported data to match the schema and data types of the TiDB tables. This step may involve converting data types, normalizing data, or cleaning up any inconsistencies. Use scripts or tools such as Python or SQL to automate this process.
Utilize TiDB's built-in tools like the `LOAD DATA` statement for CSV files or custom scripts to import the transformed data into TiDB. Ensure that the data is inserted into the correct tables and validate that all entries are accurate and complete.
After loading the data, perform thorough checks to verify data integrity and consistency. Run SQL queries to compare row counts, check for missing or duplicate entries, and validate that all data fields are correctly populated. Rectify any issues found during this step to ensure a successful migration.
By following these steps, you can effectively move data from Workable to TiDB 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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