How to load data from Recruitee to BigQuery
Learn how to use Airbyte to synchronize your Recruitee data into BigQuery within minutes.
Building your pipeline or Using Airbyte
Airbyte is the only open source solution empowering data teams to meet all their growing custom business demands in the new AI era.
Building in-house pipelines
- Inconsistent and inaccurate data
- Laborious and expensive
- Brittle and inflexible
After Airbyte
- Reliable and accurate
- Extensible and scalable for all your needs
- Deployed and governed your way
Start syncing with Airbyte in 3 easy steps within 10 minutes
Take a virtual tour
Demo video of Airbyte Cloud
Demo video of AI Connector Builder
Setup Complexities simplified!
Simple & Easy to use Interface
Airbyte is built to get out of your way. Our clean, modern interface walks you through setup, so you can go from zero to sync in minutes—without deep technical expertise.
Guided Tour: Assisting you in building connections
Whether you’re setting up your first connection or managing complex syncs, Airbyte’s UI and documentation help you move with confidence. No guesswork. Just clarity.
Airbyte AI Assistant that will act as your sidekick in building your data pipelines in Minutes
Airbyte’s built-in assistant helps you choose sources, set destinations, and configure syncs quickly. It’s like having a data engineer on call—without the overhead.
What sets Airbyte Apart
Modern GenAI Workflows
Streamline AI workflows with Airbyte: load unstructured data into vector stores like Pinecone, Weaviate, and Milvus. Supports RAG transformations with LangChain chunking and embeddings from OpenAI, Cohere, etc., all in one operation.
Move Large Volumes, Fast
Quickly get up and running with a 5-minute setup that enables both incremental and full refreshes for databases of any size, seamlessly scaling to handle large data volumes. Our optimized architecture overcomes performance bottlenecks, ensuring efficient data synchronization even as your datasets grow from gigabytes to petabytes.
An Extensible Open-Source Standard
More than 1,000 developers contribute to Airbyte’s connectors, different interfaces (UI, API, Terraform Provider, Python Library), and integrations with the rest of the stack. Airbyte’s AI Connector Builder lets you edit or add new connectors in minutes.
Full Control & Security
Airbyte secures your data with cloud-hosted, self-hosted or hybrid deployment options. Single Sign-On (SSO) and Role-Based Access Control (RBAC) ensure only authorized users have access with the right permissions. Airbyte acts as a HIPAA conduit and supports compliance with CCPA, GDPR, and SOC2.
Fully Featured & Integrated
Airbyte automates schema evolution for seamless data flow, and utilizes efficient Change Data Capture (CDC) for real-time updates. Select only the columns you need, and leverage our dbt integration for powerful data transformations.
Enterprise Support with SLAs
Airbyte Self-Managed Enterprise comes with dedicated support and guaranteed service level agreements (SLAs), ensuring that your data movement infrastructure remains reliable and performant, and expert assistance is available when needed.
What our users say
Raman Singh
Predictable, straightforward pricing model that simplified budgeting and significantly reduced overall spend
Chase Zieman
“Airbyte helped us accelerate our progress by years, compared to our competitors. We don’t need to worry about connectors and focus on creating value for our users instead of building infrastructure. That’s priceless. The time and energy saved allows us to disrupt and grow faster.”
Rupak Patel
"With Airbyte, we could just push a few buttons, allow API access, and bring all the data into Google BigQuery. By blending all the different marketing data sources, we can gain valuable insights."
How to Sync to Manually
Step 1: Export Data from Recruitee
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.
Step 2: Prepare Your Data for Upload
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.
Step 3: Set Up Google Cloud Project
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.
Step 4: Configure Google Cloud Storage
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.
Step 5: Upload CSV Files to Cloud Storage
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.
Step 6: Create a BigQuery Dataset
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.
Step 7: Load Data into BigQuery
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.