A presentation at Confluent VUG by Robin Moffatt
Data integration in architectures built on static, update-in-place datastores inevitably end up with pathologically high degrees of coupling and poor scalability. This has been the standard practice for decades, as we attempt to build data pipelines on top of databases that do a poor job modelling the fundamental objects that drive our businesses and systems: events.
Events carry both notification and state, and form a powerful primitive on which to build systems for developers and data engineers alike. Developers benefit from the asynchronous communication that events enable between services, and data engineers benefit from the integration capabilities. Everyone gains from using the standards-based, scalable and resilient streaming platform.
In this talk, we’ll discuss the concepts of events, their relevance to both software engineers and data engineers and their ability to unify architectures in a powerful way. We’ll see how stream processing makes sense in both a microservices and ETL environment, and why analytics, data integration and ETL fit naturally into a streaming world. The talk will conclude with a hands-on demonstration of these concepts in practice using Apache Kafka and commentary on the design choices made.
Join this talk to learn:
The following resources were mentioned during the presentation or are useful additional information.
Fully Managed Apache Kafka, Schema Registry, ksqlDB, and Connectors.
Kafka 101 - introducing the concepts, the APIs, and the ecosystem
Free eBooks to download, including Kafka: The Definitive Guide.
Tutorials, videos, blogs, podcasts, and more - all for developers working with Apache Kafka and Confluent Platform
Huge list of connectors for Kafka Connect
A fun blog showing what you can do with ksqlDB and Kafka
Learn all about ksqlDB in this 45 minute talk & live demo
Learn all about Kafka Connect (including the connectors available with ksqlDB)
Apache Kafka and Confluent Platform in Action! Using live streams of rail movement data in all sorts of useful ways for analysis and applications.
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