Tim Yim’s Post

Don't miss out on our upcoming streamcast on Thursday, Oct 17th at 8 AM PDT. Discover the ins and outs of Redpanda's architecture, deployment choices, real-world applications, and top tips 💻 Read the details here: #Kafka #datastreaming #dataengineering

Don't miss out on our upcoming streamcast on Thursday, Oct 17th at 8 AM PDT. Discover the ins and outs of Redpanda's architecture, deployment choices, real-world applications, and top tips 💻 Read the details here: #Kafka #datastreaming #dataengineering

Don't miss out on our upcoming streamcast on Thursday, Oct 17th at 8 AM PDT. Discover the ins and outs of Redpanda's architecture, deployment choices, real-world applications, and top tips 💻 Read the details here: #Kafka #datastreaming #dataengineering

Join our virtual session on "Getting started with Redpanda" on Thursday, October 17th (8 AM PDT) to learn about Redpanda's architecture, deployment options, practical use cases, and best practices. 💻 Sign up below👇 #Kafka #datastreaming #dataengineering

Join our virtual session on "Getting started with Redpanda" on Thursday, October 17th (8 AM PDT) to learn about Redpanda's architecture, deployment options, practical use cases, and best practices. 💻 Sign up below👇 #Kafka #datastreaming #dataengineering

Join our virtual session on "Getting started with Redpanda" on Thursday, October 17th (8 AM PDT) to learn about Redpanda's architecture, deployment options, practical use cases, and best practices. 💻 Sign up below👇 #Kafka #datastreaming #dataengineering

Want to better understand Redpanda and learn how to get started? Our technical experts will dive into our architecture before laying out installation techniques, deployment options, example use cases and best practices for using Redpanda in our upcoming streamcast! 💡 Check it out 👀 #Kafka #datastreaming #dataengineering

Hi LinkedIn! 🚀 New Article Alert! 🚀 "Designing Event-Driven Systems: Video Streaming Use Case" In distributed systems, event-driven architectures power everything from real-time notifications to scalable video platforms like YouTube. Are you curious about how to design these systems? 🤔 In my latest article, I dive into : ✅ The core components of event-driven systems: Clocks, Triggers, Message Brokers, and more. ✅ How to design scalable architectures that handle real-time user interactions, such as liking videos or managing subscriptions. ✅ A video streaming use case—exploring how MeTube (a fictional platform) implements an event-driven pattern for Real-time subscription management, Dynamic video recommendations, and Event-based user notifications. 🌟 Whether you're a software engineer building your first system or an architect optimizing a distributed platform, this hands-on guide is packed with code snippets, diagrams, and best practices to get you started. 👉 Check it out here: https://lnkd.in/dNPbUk4x 💡 I'd love to hear your thoughts: What are your favorite tools or strategies for building event-driven systems? Let’s discuss this in the comments! #EventDrivenArchitecture #DistributedSystems #SoftwareEngineering #Microservices #ScalableDesign

Unapologetically Technical's newest episode is now live! In this episode of Unapologetically Technical, I interview Hubert Dulay the author of Streaming Data Mesh and Developer Advocate at StarTree. We talked about his early experience with web backends like CORBA and SOAP and how those prepared him for data work. He shares his advice for those with web development skills to transition into data and what it's like for a person leaving a company after a long tenure there. We discuss his time at Cloudera and Confluent and how the data industry has changed over time. We talk about his recent experience with Flink and StarTree. We go in-depth into streaming data mesh, covering what it is and how to create real-time data products. We discuss the ways teams can create joined or enriched data products across domains. We round out the discussion with some tips on how to get data mesh adoption. Watch the full episode here:

Great tutorial by Jim Dowling on scaling real-time feature engineering to 1M events/s with Hopsworks. It's all open source, so you can try it yourself. One thing that stood out: the team had to allocate over 1TB of memory and hundreds of CPU cores to get Flink to feed Hopsworks that kind of data volume! Thanks for the shout-out to Feldera, Jim 🙌! We can indeed do better than Flink here. With Feldera, you don't need a supercomputer to do real-time feature engineering at scale. As proof, we will implement the same queries using Feldera and publish our results in an upcoming blog 📊. Stay tuned!