When it comes time to choose a distributed messaging system, everyone knows the answer: Apache Kafka. But how about when you’re on the hook to choose a world-class, horizontally scalable stream data processing system? When you need not just publish and subscribe messaging, but also long-term storage, a flexible integration framework, and a means of deploying real-time stream processing applications at scale without having to integrate a number of different pieces of infrastructure yourself? The answer is still Apache Kafka.
In this talk, we’ll make a rapid-fire review of the breadth of Kafka as a streaming data platform. We’ll look at its internal architecture, including how it partitions messaging workloads in a fault-tolerant way. We’ll learn how it provides message durability. We’ll look at its approach to pub/sub messaging. We’ll even take a peek at how Kafka Connect provides code-free, scalable, fault-tolerant integration, and how the Streams API provides a complete framework for computation over all the streaming data in your cluster.
vikgamov
claudioed
tkhresk
ramimac
eiki
weaverryan
asahihidehiko
masakamayama
picardparis
line_developers
keio_smilab
yanzm
yoshiitaka
mraible
andyhume
eitanlees
sferik
sachag
hannesfritz
rasmusluckow
michaelherold
destraynor
jrom
chrislema
phodgson