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Begin by logging into your Recruitee account. Navigate to the data export options within your dashboard. Typically, you can find an option to export candidate or job data. Choose the data sets you need and export them as CSV files. Save these files on your local machine to ensure they are ready for transfer.
Open each CSV file you've exported and review the data. Ensure that the column headers and data types align with the schema you plan to use in BigQuery. Clean the data by removing any unnecessary columns or rows, and correct any data inconsistencies such as formatting errors or missing values to ensure a smooth import process.
Log in to Google Cloud Console and create a new project or select an existing one. Ensure that you have billing enabled for the project. This step is crucial as BigQuery is a paid service and requires a billing account linked to your project.
In the Google Cloud Console, navigate to Cloud Storage and create a new bucket. This bucket will temporarily store your CSV files before they are imported into BigQuery. Choose a globally unique name for your bucket and select the appropriate region that matches your BigQuery datasets' location to avoid any potential latency.
Upload your prepared CSV files from your local machine to the newly created Cloud Storage bucket. You can do this directly through the Cloud Console by selecting "Upload files" in the bucket's interface or by using the `gsutil` command-line tool if you prefer working via terminal. Ensure all files are correctly uploaded and accessible.
In the BigQuery section of the Google Cloud Console, create a new dataset. This serves as a container for your tables and should be named in a way that reflects the data it will contain. Set the appropriate data retention and encryption settings based on your organization's compliance requirements.
Use the BigQuery Web UI or the command-line tool `bq` to create tables and load data from your Cloud Storage bucket into BigQuery. Choose the option to create a new table and select "Google Cloud Storage" as the source. Specify the path to your CSV file, configure the schema by defining each column's data type, and execute the load job. Verify the data integrity by running a few queries to ensure everything was imported correctly.
By following these steps, you can manually move data from Recruitee 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.
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.
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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