Self Service Open Source Data Integration With AirByte

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

Hello and welcome to the Data Engineering Podcast, the show about modern data management
When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days. Datafold helps Data teams gain visibility and confidence in the quality of their analytical data through data profiling, column-level lineage and intelligent anomaly detection. Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Go to dataengineeringpodcast.com/datafold today to start a 30-day trial of Datafold. Once you sign up and create an alert in Datafold for your company data, they will send you a cool water flask.
RudderStack’s smart customer data pipeline is warehouse-first. It builds your customer data warehouse and your identity graph on your data warehouse, with support for Snowflake, Google BigQuery, Amazon Redshift, and more. Their SDKs and plugins make event streaming easy, and their integrations with cloud applications like Salesforce and ZenDesk help you go beyond event streaming. With RudderStack you can use all of your customer data to answer more difficult questions and then send those insights to your whole customer data stack. Sign up free at dataengineeringpodcast.com/rudder today.
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
- LinkedIn
- @MichelTricot on Twitter
- michel-tricot on GitHub
John
- LinkedIn
- @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

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Support Data Engineering Podcast