Summarize
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."
Begin by cloning the GitHub repository onto your local machine. Use the Git command line tool for this purpose. Run the command `git clone ` where `` is the URL of your GitHub repository. This will download all the files from the repository to your local system.
Once the data is cloned locally, prepare it for upload. This may involve organizing files, ensuring the correct format, or compressing the data if necessary. This step ensures that the data is ready for transfer and meets any necessary requirements for processing or storage.
Install and configure the AWS Command Line Interface (CLI) on your local machine. This tool will allow you to interact with AWS services from your terminal. Use the command `aws configure` and enter your AWS Access Key, Secret Access Key, region, and output format when prompted. Make sure you have the necessary permissions to access S3 and Glue services.
Transfer the prepared data to an Amazon S3 bucket using the AWS CLI. Use the command `aws s3 cp s3:////` to copy files from your local system to the specified S3 bucket. Replace `` with the path to your data files, `` with your S3 bucket name, and `` with the desired path within the bucket.
In the AWS Management Console, navigate to AWS Glue and create a new crawler. Configure it to scan the S3 bucket where your data is stored. This crawler will infer the schema of your data and create the necessary table definitions in the AWS Glue Data Catalog. Set the crawler to run on demand or on a schedule, depending on your needs.
Execute the crawler you configured in the previous step. This will scan the data in your S3 bucket, infer the schema, and populate the AWS Glue Data Catalog with tables that represent your data. Once the crawler has completed its run, verify that the tables and schema are correctly set up in the Data Catalog.
Finally, create an AWS Glue ETL (Extract, Transform, Load) job to process the data as needed. Use the AWS Glue Studio or Glue Console to define the job, specifying the source data from the Glue Data Catalog, any transformations required, and the target S3 location for the processed data. Run the ETL job to complete the data processing workflow.
By following these steps, you should be able to move data from GitHub to Amazon S3 and process it using AWS Glue, without relying on third-party tools 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.
GitHub is a renowned and respected development platform that provides code hosting services to developers for building software for both open source and private projects. It is a heavily trafficked platform where users can store and share code repositories and obtain support, advice, and help from known and unknown contributors. Three features in particular—pull request, fork, and merge—have made GitHub a powerful ally for developers and earned it a place as a (developers’) household name.
GitHub's API provides access to a wide range of data related to repositories, users, organizations, and more. Some of the categories of data that can be accessed through the API include:
- Repositories: Information about repositories, including their name, description, owner, collaborators, issues, pull requests, and more.
- Users: Information about users, including their username, email address, name, location, followers, following, organizations, and more.
- Organizations: Information about organizations, including their name, description, members, repositories, teams, and more.
- Commits: Information about commits, including their SHA, author, committer, message, date, and more.
- Issues: Information about issues, including their title, description, labels, assignees, comments, and more.
- Pull requests: Information about pull requests, including their title, description, status, reviewers, comments, and more.
- Events: Information about events, including their type, actor, repository, date, and more.
Overall, the GitHub API provides a wealth of data that can be used to build powerful applications and tools for developers, businesses, and individuals.
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:
