Streaming Architecture

Datacast Episode 121: High-Performance Processing Engine, Modern Data Streaming, and Propelling Minority in Tech with Alex Gallego

Datacast Episode 121: High-Performance Processing Engine, Modern Data Streaming, and Propelling Minority in Tech with Alex Gallego

Alexander Gallego is the founder and CEO of Redpanda Data, a high-performance, Apache Kafka-compatible data streaming platform for mission-critical workloads. He has spent his career immersed in deeply technical environments and is passionate about finding and building solutions to the challenges of modern data streaming.

Before Redpanda, Alex was a principal engineer at Akamai and the co-founder and CTO of Concord.io, a high-performance stream-processing engine acquired by Akamai in 2016. He has also engineered software at Factset Research Systems, Forex Capital Markets, and Yieldmo; and holds a bachelor’s degree in computer science and cryptography from NYU.

Datacast Episode 112: Distributed Systems Research, The Philosophy of Computational Complexity, and Modern Streaming Database with Arjun Narayan

Datacast Episode 112: Distributed Systems Research, The Philosophy of Computational Complexity, and Modern Streaming Database with Arjun Narayan

Arjun Narayan is the co-founder and CEO of Materialize. Materialize is a streaming database for real-time applications and analytics, built on top of a next-generation stream processor – Timely Dataflow. He was previously an engineer at Cockroach Labs and held a Ph.D. in Computer Science from the University of Pennsylvania.

What I Learned From Tecton's apply() 2022 Conference

What I Learned From Tecton's apply() 2022 Conference

Back in May, I attended apply(), Tecton’s second annual virtual event for data and ML teams to discuss the practical data engineering challenges faced when building ML for the real world. There were talks on best practice development patterns, tools of choice, and emerging architectures to successfully build and manage production ML applications.

This long-form article dissects content from 14 sessions and lightning talks that I found most useful from attending apply(). These talks cover 3 major areas: industry trends, production use cases, and open-source libraries. Let’s dive in!

What I Learned From Attending Tecton apply(meetup) 2021

What I Learned From Attending Tecton apply(meetup) 2021

Last month, I attended apply(), Tecton’s follow-up virtual event of their ML data engineering conference series. I’ve previously written a recap of their inaugural event, a whirlwind tour of wide-ranging topics such as feature stores, ML platforms, and research on data engineering. In this shorter post, I would like to share content from the main talks and lightning talks presented at the community meetup. Topics include ML systems research, ML observability, streaming architecture, and more.