Open-source data movement to
Open-source data movement from
Gutendex
The only open solution empowering data teams to meet growing business demands in the new AI era.
Set up a source connector to extract data from Airbyte
This can be any API tool, cloud data warehouse, database, data lake, file, or many other source types.
Configure the connection 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.
The Airbyte Open Data Movement Platform
Modernize your data infrastructure with Airbyte's high speed data replication. Move large volumes of data with best-in-class CDC methods and replicate large databases within minutes.
Why Airbyte?
Airbyte is the only unified data movement platform built on the open standard. It is uniquely positioned in terms of data sovereignty, connector extensibility, and support for AI workflows.
Create context for AI agents by leveraging Airbyte's 600+ connectors. Airbyte's pipelines transfer structured and unstructured data together for metadata preservation. With support for flexible destinations such as Iceberg, Airbyte is the ideal data movement solution for agentic application.
Flexible deployment options: self-hosted, cloud, and hybrid. Secure and compliant: ISO 27001, SOC 2, GDPR, HIPAA, data encryption, audit/monitoring, SSO, RBAC, and more. Centralized multi-tenant management with self-serve capabilities.
Trusted by the world's leading companies
Immediate ROI and productivity gains for your data teams.
"With our legacy framework, if one of the pipelines fails for one client, it will stop everything for the rest of our clients. But with Airbyte, things are run in parallel because of the platform’s distributed nature, which means that we can process multiple clients at the same time without impacting performance."
Raman Singh, Tech Lead at Symend
"The real ROI is in our ability to iterate quickly, especially at our increasing scale. At the end of the day, you want a tool like that to just work. We can forget about it and know that it's configured and it's connecting and it's working. That hands-free capability is a big appeal for the platform.”
Sean Carver, Director of Data at Petvisor
"For TUI Musement, Airbyte cut development time in half and enabled dynamic customer experiences."
Andre Exner, Director of Customer Hub and Common Analytics
"What's different from Stitch Data or Informatica is the way that we can configure Airbyte connections and Airbyte entities through code. That's a huge plus to us as data engineers, because we are used to checking code and being able to manage changes from Github."
Amy Zhao, Senior Manager of Data Engineering at Peloton
"Airbyte allows us to stay flexible while scaling from hundred-million to billion-dollar enterprise clients."
Franziska Ibscher, Product Manager at Drivepoint
FAQs
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.
Gutendex is a simple, self-hosted web API for serving book catalog information from Project Gutenberg, an online library of free ebooks.Gutendex. JSON web API for Project Gutenberg ebook metadata.Gutenberg can be a useful source of literature, but its large size makes it difficult to access and analyse it on a large scale. Gutendex downloads these files, stores their data in a database, and publishes the data in a simpler format. Gutendex uses Django to download catalog data and serve it in a simple JSON REST API.
This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:
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This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:
Airbyte
Fivetran
StitchData
Matillion
Talend Data Integration
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.
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.
Airbyte supports a growing list of sources, including API tools, cloud data warehouses, lakes, databases, and files, or even custom sources you can build.
Github
Unstructured
Gitlab
Unstructured
Google Drive
Unstructured
Microsoft OneDrive
Unstructured
Microsoft Sharepoint
Unstructured
Notion
Unstructured
S3
Unstructured
Slack
Unstructured
Apify Dataset
Unstructured
Azure Blog Storage
Unstructured