Project Metamorphosis : dévoilement de la plateforme de streaming d'événements nouvelle génération. En savoir plus

Kafka Summit NYC is Almost Here – Don’t Miss the Streams Track!

Ever wondered what it’s like to run Kafka in production? What about building and deploying microservices that process streams of data in real-time, at large scale?  Or, maybe just the pitfalls to avoid in your own organization? The Streams Track at Kafka Summit NYC is for you. In just a few weeks, companies like Google, Uber, and AltspaceVR, among others, will be sharing lessons they’ve learned with stream processing solutions, and how they run them in production at scale.

We might be biased, but we think the streams track is one of the most exciting tracks at Kafka Summit, and we know our speakers are excited to share their thoughts and insights on the world of stream processing. In this track, we will explore new Kafka features that are exceptionally useful when building event-driven applications and microservices, and how companies use Kafka in their stream processing architectures to build great products and to understand and act upon everything their customers are doing in real-time.

Here are some of our can’t miss sessions:


Hanging Out With Your Past Self In VR: Time-Shifted Avatar Replication Using Kafka Streams
Greg Fodor, Director of Engineering, AltspaceVRIn this talk, we’ll show how AltspaceVR developed a Kafka Streams based solution to perform real time mirroring, capture, and playback of networked avatars in a shared VR environment. For intermediate to advanced Kafka Streams users, we’ll cover some common pitfalls, lessons learned, and design patterns that helped us create a streaming application that provides features our users call magic.
ben stopford The Data Dichotomy: Rethinking Data & Services with Streams
Ben Stopford, Engineer, ConfluentGénéralement, lorsque nous développons des applications orientées services, des microservices, des SOA, etc., nous utilisons REST ou un cadre RPC. Mais développer de telles applications devient compliqué au fur et à mesure qu'elle deviennent plus volumineuses et plus complexes, et partagent davantage de données. Nous pouvons faire remonter cette difficulté à une dichotomie qui sous-tend la façon dont les systèmes interagissent : les systèmes de données sont conçus pour exposer des données, afin de les rendre librement accessibles. Mais les services, à la place, se concentrent sur l'encapsulation. Limitant les données que chaque service expose. Ces deux forces sont inévitablement en concurrence au fur et à mesure que ces systèmes évoluent.Cette présentation se penchera sur une approche différente. Une approche où un log distribué peut être utilisé afin de stocker des données qui sont partagées entre des services. Ensuite, les processeurs de flux à états sont incorporés directement dans chaque service, fournissant des installations pour assembler et réagir aux flux partagés. Le résultat est une manière très différente d'élaborer une architecture et de concevoir des applications orientées services, mais avec certains avantages uniques au fur et à mesure que nous implémentons à grande échelle.
Scalable Real-time Complex Event Processing at Uber
Shuyi Chen, Senior Software Engineer, UberThe Marketplace data team at Uber has built a scalable complex event processing platform to solve many challenging real-time data needs for various Uber products. This platform has been in production for >1 year and supporting over 100 real-time data use cases with a team of 3. In this talk, we will share the detail of the design and our experience, and how we employ Kafka and Samza at scale.

It’s not too late to
register for Kafka Summit NYC!  Also, you may want to participate in our Kafka Summit Hackathon where we help the community learn how to build streaming applications with Apache Kafka. Hope to see you there!

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