Scaling Airbyte: Challenges and Milestones on the Road to 1.0
Summary
Airbyte is one of the most prominent platforms for data movement. Over the past 4 years they have invested heavily in solutions for scaling the self-hosted and cloud operations, as well as the quality and stability of their connectors. As a result of that hard work, they have declared their commitment to the future of the platform with a 1.0 release. In this episode Michel Tricot shares the highlights of their journey and the exciting new capabilities that are coming next.
Announcements
Parting Question
Airbyte is one of the most prominent platforms for data movement. Over the past 4 years they have invested heavily in solutions for scaling the self-hosted and cloud operations, as well as the quality and stability of their connectors. As a result of that hard work, they have declared their commitment to the future of the platform with a 1.0 release. In this episode Michel Tricot shares the highlights of their journey and the exciting new capabilities that are coming next.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- Your host is Tobias Macey and today I'm interviewing Michel Tricot about the journey to the 1.0 launch of Airbyte and what that means for the project
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Airbyte is and the story behind it?
- What are some of the notable milestones that you have traversed on your path to the 1.0 release?
- The ecosystem has gone through some significant shifts since you first launched Airbyte. How have trends such as generative AI, the rise and fall of the "modern data stack", and the shifts in investment impacted your overall product and business strategies?
- What are some of the hard-won lessons that you have learned about the realities of data movement and integration?
- What are some of the most interesting/challenging/surprising edge cases or performance bottlenecks that you have had to address?
- What are the core architectural decisions that have proven to be effective?
- How has the architecture had to change as you progressed to the 1.0 release?
- A 1.0 version signals a degree of stability and commitment. Can you describe the decision process that you went through in committing to a 1.0 version?
- What are the most interesting, innovative, or unexpected ways that you have seen Airbyte used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Airbyte?
- When is Airbyte the wrong choice?
- What do you have planned for the future of Airbyte after the 1.0 launch?
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
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- Airbyte
- Airbyte Cloud
- Airbyte Connector Builder
- Singer Protocol
- Airbyte Protocol
- Airbyte CDK
- Modern Data Stack
- ELT
- Vector Database
- dbt
- Fivetran
- Meltano
- dlt
- Reverse ETL
- GraphRAG