How to load data from GitHub to BigQuery
Learn how to use Airbyte to synchronize your GitHub 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: Access GitHub Data
Start by identifying the GitHub repository from which you want to extract data. You'll need to decide whether you want to extract data from files in the repository or use the GitHub API to extract metadata (like issues, pull requests, etc.). If you choose to use the GitHub API, you'll need to generate a personal access token for authentication.
Step 2: Extract Data from GitHub
If you are extracting data from files, you can clone the repository using Git commands (`git clone `) to your local machine. For metadata, use the GitHub API with a suitable programming language (Python, JavaScript, etc.) to send requests and retrieve data in a structured format like JSON.
Step 3: Transform Data to CSV or JSON
Once you have the data locally, transform it into a format suitable for BigQuery. If the data is in files, ensure it is formatted as CSV or JSON. For data retrieved via the GitHub API, parse the JSON responses and save them to CSV or JSON files. Use scripting (e.g., Python pandas) to clean and structure the data as needed.
Step 4: Prepare Your Google Cloud Project
Log into your Google Cloud Platform account and create a new project if you don’t already have one. Then, enable the BigQuery API for your project by navigating to the "API & Services" section and enabling it.
Step 5: Upload Data to Google Cloud Storage
Before importing data into BigQuery, upload your CSV or JSON files to Google Cloud Storage. Use the `gsutil` command-line tool to upload files. For example, run `gsutil cp gs:///` to upload files to your bucket.
Step 6: Load Data into BigQuery
Open the BigQuery web UI in the Google Cloud Console. Create a new dataset if you don’t have one. Then, use the "Create Table" feature to import data from Google Cloud Storage. Specify the source format (CSV or JSON), and configure the schema manually or let BigQuery auto-detect it. Complete the process to load data into a BigQuery table.
Step 7: Verify Data in BigQuery
After the import is complete, verify the data by running simple queries in the BigQuery console. Check for completeness and accuracy by comparing some sample records against the original data from GitHub. Make adjustments as necessary and ensure your data is ready for analysis or further processing.
By following these steps, you can effectively move data from GitHub to BigQuery without using third-party connectors or integrations.