Trusted by data-driven companies
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.
- Inconsistent and inaccurate data
- Laborious and expensive
- Brittle and inflexible
- 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
Move Large Volumes, Fast
An Extensible Open-Source Standard
Full Control & Security
Fully Featured & Integrated
Enterprise Support with SLAs
What our users say
Andre Exner
"For TUI Musement, Airbyte cut development time in half and enabled dynamic customer experiences."
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."
Start by exporting the data you need from Confluence. Use the built-in export feature in Confluence to export pages or spaces. Navigate to the space you want to export, click on the space settings, and choose the export option. You can export the data in various formats such as XML, PDF, or HTML. For structured data, XML is often the most useful format.
Once you have the exported data, you may need to transform it into a format suitable for S3 and Glue. Use a local script or tool (like Python with libraries such as `pandas` or `xmltodict`) to transform the XML data into CSV or JSON format. This step is crucial because AWS Glue can easily catalog and process these formats.
Install and configure the AWS Command Line Interface (CLI) on your local machine. This will allow you to interact with AWS services directly. Use the command `aws configure` to set up your credentials and default region. Ensure you have the right permissions to upload data to S3 and to interact with AWS Glue.
Use the AWS CLI to upload your transformed CSV or JSON data to an S3 bucket. The command `aws s3 cp [local_file_path] s3://[your_bucket_name]/[desired_path]` will upload your file to the specified S3 bucket. Ensure that your S3 bucket is properly configured with the right permissions to allow Glue to access the data.
Go to the AWS Glue console and create a new crawler. A crawler will scan your data in S3 and create or update the corresponding metadata tables in the AWS Glue Data Catalog. Configure the crawler to point to the S3 path where your data is stored, and set it to run on demand or on a schedule, depending on your needs.
Execute the crawler to populate the Glue Data Catalog with metadata about your data. This process involves Glue scanning the data in your S3 bucket and creating table definitions that describe the structure of your data. Once complete, you can view the metadata in the Glue Data Catalog.
Create and run AWS Glue jobs to process or transform your data as needed. You can write scripts in Python or Scala to manipulate your data, and Glue will handle the execution. The processed data can be further stored in S3, queried with Athena, or loaded into other AWS services for analysis or reporting.
By following these steps, you can effectively move and manage your data from Confluence to AWS S3 and Glue without the need for 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.
Confluence defines your reason for being so you can form actionable business strategies and it can share performance results and customer insights with stakeholders. Confluence presents your business vision and help your team understand your strategic plan. It is your remote-friendly team workspace where knowledge and collaboration meet. Confluence is purpose-built for teams which requires a secure and reliable way to collaborate on mission-critical projects. Confluence sites are entirely protected by privacy controls and data encryption, and meet industry-verified compliance standards.
Confluence's API provides access to a wide range of data, including:
1. Pages: Confluence pages are the primary unit of content in the platform, and the API allows developers to create, read, update, and delete pages.
2. Spaces: Spaces are containers for pages and other content, and the API provides access to space metadata, permissions, and other settings.
3. Users and groups: The API allows developers to manage users and groups, including creating, updating, and deleting them.
4. Comments: Confluence pages can have comments, and the API provides access to comment metadata and content.
5. Attachments: Pages can have attachments, such as images or documents, and the API allows developers to manage attachments.
6. Labels: Labels are used to categorize content in Confluence, and the API provides access to label metadata and allows developers to add or remove labels from pages.
7. Search: The API provides a search endpoint that allows developers to search for pages, spaces, and other content in Confluence.
Overall, Confluence's API provides access to a wide range of data that developers can use to build custom integrations and applications that extend the functionality of the platform.
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?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
