Alex Martin
Head of Product
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Alex is Head of Product at Tinybird. He has been building data products at scale for almost a decade, with a focus on streaming analytics, implementing many Event Sourcing techniques in a challenging environment with great impact. Now, he's in Tinybird to help data teams ship faster.
Events Sourcing with Kafka at scale
In the digital age, businesses generate copious amounts of operational data from their day-to-day activities. Unfortunately, much of this data remains locked away in silos, limiting its potential to drive business value. Event Sourcing is an approach that can unlock this potential by capturing, storing, and analyzing operational events.
This talk will explore the concept of Event Sourcing and its ability to help businesses unlock the hidden value in their operational events. We will also examine the key challenges businesses face when implementing Event Sourcing, including data volume, complexity, and security. Additionally, we will highlight real-world examples of businesses that have successfully implemented these techniques to improve operational efficiency, reduce costs, and enhance the customer experience.
Whether you are a business leader, data engineer, or data scientist, this talk will provide valuable insights into how Event Sourcing can help you unlock the hidden value in your operational events and drive business success.
Scaling Kafka consumers with rendezvous hashing
Horizontally scaling Kafka consumers with rendezvous hashing: how we used this technique to reimagine and revamp our Kafka infrastructure so we could ingest events from hundreds of concurrent Kafka topics. This new approach significantly reduced our Kafka connections - and associated costs - while continuing to support the throughput and availability goals of our original architecture.
8+ years in data
I've spent the last 8+ years working with data, in one way or another: running ad-hoc analysis over massive amounts of data, building real-time internal applications, designing and building data warehouses and pipelines in the cloud, doing streaming processing and analysis, or building an analytics tracker at massive scale from scratch.
Today I want to share with you a few things I've learned along the way – what worked, what didn't, and some real-life examples to top it off.
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