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First, log into your Workable account and navigate to the reports or data export section. Use the available export feature to download the data you need in a CSV or Excel format. Ensure that the export includes all the necessary fields and records you intend to transfer.
Open the exported CSV or Excel file using a spreadsheet program such as Microsoft Excel or Google Sheets. Review the data for consistency and completeness. Clean up any unnecessary columns, fix any data inconsistencies, and ensure that the data types align with what is expected in the MSSQL database.
Ensure you have access to the MSSQL database where you intend to import the data. This includes having login credentials and the necessary permissions to create tables and insert data. Use a tool like SQL Server Management Studio (SSMS) to interact with the database.
Using SSMS, write a SQL script to create a table that matches the structure of your cleaned data. Define the appropriate data types for each column to ensure compatibility. For example, use `VARCHAR` for text fields, `INT` for integer fields, and `DATETIME` for date fields.
Convert your CSV data into SQL `INSERT` statements. This can be done manually for small datasets or by using a simple script in a language like Python. The script should read each row of the CSV file and generate a corresponding `INSERT` statement for the target table in MSSQL.
Once you have the SQL `INSERT` statements prepared, execute them in your MSSQL database. You can do this by opening a new query window in SSMS and running the statements. Ensure that each statement executes successfully and verify that the data appears correctly in the target table.
After importing the data, perform a series of checks to ensure data integrity. This includes comparing record counts between your source file and the MSSQL table, checking for any discrepancies or errors in the data, and ensuring all data types have been preserved and correctly formatted in the MSSQL environment.
By following these steps, you will be able to move data from Workable to an MSSQL destination 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:
