How to load data from Confluence to Convex

Learn how to use Airbyte to synchronize your Confluence data into Convex within minutes.

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Bespoke pipelines are:
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Furthermore, you will need to build and maintain Y x Z pipelines with Y sources and Z destinations to cover all your needs.
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All your pipelines in minutes, however custom they are, thanks to Airbyte’s connector marketplace and AI Connector Builder.

Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Confluence connector in Airbyte

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

Set up Convex for your extracted Confluence data

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

Configure the Confluence to Convex 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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Check out our interactive demo and our how-to videos to learn how you can sync data from any source to any destination.

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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.

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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.

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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.

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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.

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Tech Lead at Symend

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Chase Zieman

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"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."

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How to Sync to Manually

Step 1: Export Data from Confluence

Start by exporting the data you need from Confluence. Navigate to the space or page you want to export, and use Confluence's built-in export feature. Choose a suitable format, such as XML or HTML, which retains the data structure and content. If you're exporting a whole space, ensure that all pages are included.

Once you have the exported file, prepare it by organizing the data in a way that is easy to manipulate. If it's an XML file, you might want to use an XML editor to inspect and clean up unnecessary metadata. For HTML, ensure that the content is standardized across all files to facilitate easier parsing.

Convert your prepared data into a CSV format, which is more manageable and commonly used for data imports. You can use a scripting language like Python or a text editor to extract the necessary fields and save them as a CSV. Ensure each CSV column corresponds to a relevant data field that exists in Convex.

Log into your Convex account and identify the target database or collection where you want to import the data. Familiarize yourself with the schema and structure of the database to map the CSV data accordingly.

Create a script to automate the data import process. Using a language like Python, write a script that reads the CSV file and uses Convex's API to insert data into the appropriate collections. Ensure that your script handles authentication with Convex securely and includes error handling for failed insertions.

Execute the script to begin importing data into Convex. Monitor the process to ensure that all data is transferred correctly. If any errors occur, debug the script by checking the error logs and adjust the data format or mappings as necessary.

After the import process is complete, verify the data integrity by checking Convex for discrepancies or missing information. Compare a sample of entries in Convex with the original data in Confluence to ensure consistency. Make any necessary adjustments and re-import data if needed.

By following these steps, you can efficiently move data from Confluence to Convex without relying on third-party connectors or integrations.