Self Service Open Source Data Integration With AirByte
Summary
Data integration is a critical piece of every data pipeline, yet it is still far from being a solved problem. There are a number of managed platforms available, but the list of options for an open source system that supports a large variety of sources and destinations is still embarrasingly short. The team at Airbyte is adding a new entry to that list with the goal of making robust and easy to use data integration more accessible to teams who want or need to maintain full control of their data. In this episode co-founders John Lafleur and Michel Tricot share the story of how and why they created Airbyte, discuss the project’s design and architecture, and explain their vision of what an open soure data integration platform should offer. If you are struggling to maintain your extract and load pipelines or spending time on integrating with a new system when you would prefer to be working on other projects then this is definitely a conversation worth listening to.
Announcements
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- Your host is Tobias Macey and today I’m interviewing Michel Tricot and John Lafleur about Airbyte, an open source framework for building data integration pipelines.
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you start by explaining what Airbyte is and the story behind it?
- Businesses and data engineers have a variety of options for how to manage their data integration. How would you characterize the overall landscape and how does Airbyte distinguish itself in that space?
- How would you characterize your target users?
- How have those personas instructed the priorities and design of Airbyte?
- What do you see as the benefits and tradeoffs of a UI oriented data integration platform as compared to a code first approach?
- what are the complex/challenging elements of data integration that makes it such a slippery problem?
- motivation for creating open source ELT as a business
- Can you describe how the Airbyte platform is implemented?
- What was your motivation for choosing Java as the primary language?
- incidental complexity of forcing all connectors to be packaged as containers
- shortcomings of the Singer specification/motivation for creating a backwards incompatible interface
- perceived potential for community adoption of Airbyte specification
- tradeoffs of using JSON as interchange format vs. e.g. protobuf/gRPC/Avro/etc.
- information lost when converting records to JSON types/how to preserve that information (e.g. field constraints, valid enums, etc.)
- interfaces/extension points for integrating with other tools, e.g. Dagster
- abstraction layers for simplifying implementation of new connectors
- tradeoffs of storing all connectors in a monorepo with the Airbyte core
- impact of community adoption/contributions
- What is involved in setting up an Airbyte installation?
- What are the available axes for scaling an Airbyte deployment?
- challenges of setting up and maintaining CI environment for Airbyte
- How are you managing governance and long term sustainability of the project?
- What are some of the most interesting, unexpected, or innovative ways that you have seen Airbyte used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while building Airbyte?
- When is Airbyte the wrong choice?
- What do you have planned for the future of the project?
Contact Info
- Michel
- @MichelTricot on Twitter
- michel-tricot on GitHub
- John
- @JeanLafleur on Twitter
- johnlafleur on GitHub
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Links
- Airbyte
- Liveramp
- Fivetran
- Stitch Data
- Matillion
- DataCoral
- Singer
- Meltano
- Airflow
- Kotlin
- Docker
- Monorepo
- Airbyte Specification
- Great Expectations
- Dagster
- Prefect
- DBT
- Kubernetes
- Snowflake
- Redshift
- Presto
- Spark
- Parquet
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA