How to load data from Recruitee to MS SQL Server

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Learn how to use Airbyte to synchronize your Recruitee data into MS SQL Server within minutes.

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Set up a Recruitee connector in Airbyte

Connect to Recruitee or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up MS SQL Server for your extracted Recruitee data

Select MS SQL Server where you want to import data from your Recruitee source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Recruitee to MS SQL Server in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.

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How to Sync Recruitee to MS SQL Server Manually

Start by reviewing Recruitee's data export capabilities. Typically, Recruitee allows data exports in formats such as CSV or Excel files. Check their documentation or your account settings to ensure you can export the data you need.

Use Recruitee's interface to export the desired data. Navigate to the relevant section (e.g., candidates, jobs), apply necessary filters, and use the export function to download the data in CSV format. Save this file securely on your system.

Open the exported CSV file and examine the data to ensure it matches your expectations. Clean the data by removing any unnecessary columns, correcting any discrepancies, and ensuring consistency in data formats. This step is crucial for a smooth import into MSSQL.

If you haven't already, set up your MSSQL database. Create a new database or use an existing one, and define the table(s) that will store the Recruitee data. Ensure that the table schema matches the structure of the CSV data. Use SQL Server Management Studio (SSMS) or a similar tool to execute the necessary SQL commands.

Open SQL Server Management Studio (SSMS) and connect to your MSSQL instance. Use the SQL Server Import and Export Wizard, which is built into SSMS, to import data from the CSV file. Specify the CSV file as the data source and your MSSQL database as the destination. Follow the wizard's steps to map the CSV columns to the database table columns accurately.

Once the import process is complete, verify the data in your MSSQL database. Run SELECT queries to check if all records were imported correctly and that there are no discrepancies. Validate the data by comparing it against the original CSV file to ensure consistency and accuracy.

If you need to perform this data transfer regularly, consider writing a script using a language like Python or PowerShell to automate the export and import process. You can schedule the script using Task Scheduler (Windows) or cron jobs (Linux) to run at desired intervals, ensuring your MSSQL database remains up-to-date with Recruitee data.

By following these steps, you can effectively transfer data from Recruitee to an MSSQL destination without relying on third-party connectors or integrations.

How to Sync Recruitee to MS SQL Server Manually - Method 2:

FAQs

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.

Recruitee is the collaborative hiring software that delivers a complete solution to help internal teams hire better together. As an Applicant Tracking System, it enables recruitment teams to easily manage the hiring process from start to finish while keeping hiring managers and colleagues as active participants. Recruitee is on a mission to empower teams with the best tech tools to hire better together. Its vision is to put collaboration at the core of hiring teams.

Recruitee'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 the API:  

1. Candidates: Information about candidates who have applied for a job, including their name, contact details, resume, and application status.  
2. Jobs: Details about job openings, including the job title, description, location, and requirements.  
3. Applications: Data related to the application process, such as the date and time of application, the source of the application, and the status of the application.  
4. Users: Information about users who have access to the Recruitee account, including their name, email address, and role.  
5. Teams: Details about teams within the organization, including the team name, members, and permissions.  
6. Stages: Information about the different stages of the recruitment process, such as screening, interviewing, and hiring.  
7. Tags: Data related to tags that can be assigned to candidates, jobs, and applications to help with organization and filtering.  
8. Custom fields: Information about custom fields that can be added to candidates, jobs, and applications to capture additional data.  

Overall, the Recruitee API provides a comprehensive set of data that can be used to streamline recruitment processes and improve hiring outcomes.

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps: 
1. Set up Recruitee to MSSQL - SQL Server as a source connector (using Auth, or usually an API key)
2. Choose a destination (more than 50 available destination databases, data warehouses or lakes) to sync data too and set it up as a destination connector
3. Define which data you want to transfer from Recruitee to MSSQL - SQL Server and how frequently
You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud. 

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.

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.

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