How to load data from Confluence to BigQuery
Learn how to use Airbyte to synchronize your Confluence 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: Extract Data from Confluence
Begin by using Confluence's REST API to extract the data you need. You can perform API calls using command-line tools like `curl` or by writing a script in Python or JavaScript. Use the API endpoint `/wiki/rest/api/content` to query the required pages or spaces. Ensure you have the necessary permissions and API tokens for authentication.
Step 2: Transform and Format Data
Once you've extracted the data, it will likely be in JSON format. You need to transform and format this data into a CSV or another BigQuery-compatible format. Use a scripting language like Python (with libraries such as `pandas`) to clean, normalize, and structure your data according to your needs.
Step 3: Store Transformed Data Locally
After transforming the data, save it locally on your machine or a server. For example, export the cleaned data to a CSV file. This file will be the intermediate storage before uploading it to Google Cloud Storage.
Step 4: Set Up Google Cloud Storage (GCS)
Log in to your Google Cloud Console and create a new bucket in Google Cloud Storage. This bucket will temporarily store your data files before importing them into BigQuery. Ensure the bucket is in the same region as your BigQuery dataset to optimize for performance.
Step 5: Upload Data to Google Cloud Storage
Use the `gsutil` command-line tool to upload your CSV file to the GCS bucket you created. The command will look something like `gsutil cp /local/path/to/yourfile.csv gs://your-bucket-name/yourfile.csv`. Ensure you have the necessary permissions and that your Google Cloud SDK is configured correctly.
Step 6: Load Data into BigQuery
Navigate to the BigQuery console in the Google Cloud Platform. Use the “Create Table” option, selecting Google Cloud Storage as the source. Specify your CSV file location in the GCS bucket, choose the appropriate file format, and configure the schema for your BigQuery table. Execute the import operation to load the data into BigQuery.
Step 7: Verify and Query Data in BigQuery
Once the data is imported, run a few queries in BigQuery to ensure that the data was imported correctly and is structured as expected. Check for any discrepancies or errors that may have occurred during the import process. Make necessary adjustments in your transformation script if needed and re-import until the data is accurate.
This step-by-step guide allows you to move data from Confluence to BigQuery using native capabilities without relying on third-party connectors or integrations.