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Rethinking Stream Processing with Apache Kafka: Applications vs. Clusters, Streams vs. Databases (Google DevFest Switzerland 2017)

My talk at Google DevFest Switzerland, Fribourg, Oct 2017. https://devfest.ch/schedule/day1?sessionId=118 Abstract: Modern businesses have data at their core, and this data is changing continuously. How can we harness this torrent of information in real-time? The answer is stream processing, and the technology that has since become the core platform for streaming data is Apache Kafka. Among the thousands of companies that use Kafka to transform and reshape their industries are the likes of Netflix, Uber, PayPal, and AirBnB, but also established players such as Goldman Sachs, Cisco, and Oracle. Unfortunately, today’s common architectures for real-time data processing at scale suffer from complexity: there are many technologies that need to be stitched and operated together, and each individual technology is often complex by itself. This has led to a strong discrepancy between how we, as engineers, would like to work vs. how we actually end up working in practice. In this session we talk about how Apache Kafka helps you to radically simplify your data architectures. We cover how you can now build normal applications to serve your real-time processing needs — rather than building clusters or similar special-purpose infrastructure — and still benefit from properties such as high scalability, distributed computing, and fault-tolerance, which are typically associated exclusively with cluster technologies. We discuss common use cases to realize that stream processing in practice often requires database-like functionality, and how Kafka allows you to bridge the worlds of streams and databases when implementing your own core business applications (inventory management for large retailers, patient monitoring in healthcare, fleet tracking in logistics, etc), for example in the form of event-driven, containerized microservices. We will also give a brief shout-out to the recently launched KSQL, a streaming SQL engine for Apache Kafka.

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Rethinking Stream Processing
with Apache Kafka:
Applications vs. Clusters,
Streams vs. Databases
Michael G. Noll, Confluent
@miguno
Google GDG DevFest Switzerland, October 28-29, 2017
2
0.11 Exactly-once
semantics
0.10 Data processing (Streams API)
0.9 Data integration (Connect API)
Intra-cluster
replication
0.8
2012 2014 2015 2016 2017
Cluster mirroring0.7
2013
Apache Kafka: birthed as a messaging system, now a streaming platform
3
4
5
6

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