Episode 75: Commoditizing Data Integration Pipelines with Michel Tricot

Datacast

Timestamps

• (01:58) Michel went over his education studying at EPITA — School of Engineering and Computer Science in France.
• (03:50) Michel mentioned his first US internship at Siemens Corporate Research as an R&D engineer.
• (05:48) Michel discussed the unique challenges of building systems to handle financial data through his engineering experience at FactSet Research Systems and Murex.
• (07:48) Michel talked about his move to San Francisco to work as a Senior Software Engineer at Rapleaf, focusing on scaling up data integration and data management pipelines.
• (10:40) Michel unpacked his work building the modern data stack at LiveRamp.
• (16:18) Michel shared valuable leadership and hiring lessons absorbed during his time as Liveramp’s Head of Data Integrations — fostering a strong culture of innovation and expanding the engineering organization significantly.
• (19:03) Michel dived into how to interview engineering talent for independence, autonomy, and communication.
• (20:56) Michel dissected the engineering architecture of the rideOS ride-hail platform (where he was a founding member and director of engineering).
• (26:03) Michel told the founding story of Airbyte, whose mission is to make data integration pipelines a commodity (+ the pivot that happened during Airbyte’s time at Y Combinator).
• (32:10) Michel explained the paint points with existing data integration practices and the vision that Airbyte is moving towards.
• (35:07) Michel unpacked the analogy of Airbyte’s approach to building a connector manufacturing plant, which is to think in onion layers.
• (39:13) Michel shared the challenges that are still hard for an open-source solution to address (Read his list of challenges that open-source and commercial software face to solve the data integration problem).
• (40:28) Michel discussed how to prioritize product roadmap while developing an open-source project.
• (41:59) Michel discussed pricing strategies for open-source projects (Airbyte’s business models entail both self-hosted and hosted solutions).
• (44:17) Michel revealed the hurdles that Airbyte has overcome to find the early committers for their open-source project.
• (47:53) Michel shared valuable hiring lessons learned at Airbyte.
• (50:16) Michel shared fundraising advice for founders seeking the right investors for their startups.
• (52:41) Closing segment.

Michel’s Contact Info

• Twitter
• LinkedIn
• GitHub

Mentioned Content

Airbyte (Docs | Community | GitHub | Twitter | LinkedIn)

• Handbook
• Recipes
• Community Call
• Office Hours
• Connector Contest

Blog Posts

• “The Hard Things About Pivoting” (July 2020)
• “How Can We Commoditize Data Integration Pipelines” (Sep 2020)
• “How to Build Thousands of Connectors” (Oct 2020)
• “The Deck We Used to Raise Our Seed with Accel in 13 Days” (March 2021)

People

• Jeremy Litz (Former CTO and Co-Founder of LiveRamp)
• Tristan Handy (CEO of dbtLabs and Editor of the Analytics Engineering Newsletter)

Book

• “High-Growth Handbook” (by Elad Gil)

Notes

My conversation with Michel was recorded back in April 2021. Since the podcast was recorded, a lot has happened at Airbyte! I'd recommend:

• Looking over the deck that they used to raise a $26M Series A led by Benchmark.
• Reading Michel's take on Airbyte's new OSS model and strategy to commoditize all data integration.
• Diving into Airbyte Cloud, a hosted service that takes all of the features of the open-source version and adds hosting and management, on top of a number of additional support options and enterprise features.
• Subscribing to Airbyte's newsletter called Weekly Bytes and exploring Airbyte Recipes.

About the show

Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY and the HOW”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.

Datacast is produced and edited by James Le. Get in touch with feedback or guest suggestions by emailing [e-mail].

Subscribe by searching for Datacast wherever you get podcasts or click one of the links below:

• Listen on Spotify
• Listen on Apple Podcasts
• Listen on Google Podcasts

If you’re new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.

About the show

Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY and the HOW”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.

Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [e-mail].

Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:

• Listen on Spotify
• Listen on Apple Podcasts
• Listen on Google Podcasts

If you’re new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.