Data Pipelines

What I Learned From DataOps Unleashed 2022

What I Learned From DataOps Unleashed 2022

Earlier this month, I attended the second iteration of DataOps Unleashed, a great event that examines the emergence of DataOps, CloudOps, AIOps, and other professionals coming together to share the latest trends and best practices for running, managing, and monitoring data pipelines and data-intensive analytics workloads.

In this long-form blog recap, I will dissect content from the session talks that I found most useful from attending the summit. These talks are from DataOps professionals at leading organizations detailing how they establish data predictability, increase reliability, and reduce costs with their data pipelines. If interested, you should also check out my recap of DataOps Unleashed 2021 last year.

Datacast Episode 75: Commoditizing Data Integration Pipelines with Michel Tricot

Datacast Episode 75: Commoditizing Data Integration Pipelines with Michel Tricot

Michel Tricot has been working in data engineering for 15 years. Originally from France, Michel came to the US in 2011 to join a small startup named LiveRamp. As the company grew, he became the Head of Integrations and Director of Engineering, where his team built and scaled over 1,000 data ingestion and distribution connectors to replicate hundreds of TB worth of data every day. 

After LiveRamp’s acquisition and later IPO (NYSE:RAMP), he wanted to return to an early-stage startup. So he joined rideOS as Director of Engineering, again deep in data engineering. While there, he realized that companies were always trying to solve the same problem repeatedly, which should be solved once and for all. 

This was when he decided to start a new company, and Airbyte was born.

What I Learned From Attending Tecton's apply() Conference

What I Learned From Attending Tecton's apply() Conference

Last week, I attended apply(), Tecton’s first-ever conference that brought together industry thought leaders and practitioners from over 30 organizations to share and discuss ML data engineering’s current and future state. The complexity of ML data engineering is the most significant barrier between most data teams and transforming their applications and user experiences with operational ML.


In this long-form blog recap, I will dissect content from 23 sessions and lightning talks that I found most useful from attending apply(). These talks cover everything from the rise of feature stores and the evolution of MLOps, to novel techniques and scalable platform design. Let’s dive in!

What I Learned From Attending DataOps Unleashed 2021

What I Learned From Attending DataOps Unleashed 2021

Last week, I attended DataOps Unleashed, a great event that examines the emergence of DataOps, CloudOps, AIOps, and other professionals coming together to aggregate conversations around the latest trends and best practices for running, managing, and monitoring data pipelines.

This long-form blog recap dissects content from the session talks that I found most useful from attending the summit. These talks are from DataOps professionals at leading organizations detailing how they establish data predictability, increase reliability, and create economic efficiencies with their data pipelines.