Tim Berglund
ArchConf
Clearwater · December 10 - 13, 2018
VP Developer Relations at Confluent
Tim is a teacher, author, and technology leader with Confluent, where he serves as the Vice President of Developer Relations. He is a regular speaker at conferences and a presence on YouTube explaining complex technology topics in an accessible way. He tweets as @tlberglund, blogs every few years at http://timberglund.com. He has three grown children and two grandchildren, a fact about which he is rather excited.
Presentations
Kafka as a Platform: the Ecosystem from the Ground Up
Kafka has become a key data infrastructure technology, and we all have at least a vague sense that it is a messaging system, but what else is it? How can an overgrown message bus be getting this much buzz? Well, because Kafka is merely the center of a rich streaming data platform that invites detailed exploration.
Streaming Data with Apache Kafka
The toolset for building scalable data systems is maturing, having adapted well to our decades-old paradigm of update-in-place databases. We ingest events, we store them in high-volume OLTP databases, and we have new OLAP systems to analyze them at scale—even if the size of our operation requires us to grow to dozens or hundreds of servers in the distributed system. But something feels a little dated about the store-and-analyze paradigm, as if we are missing a new architectural insight that might more efficiently distribute the work of storing and computing the events that happen to our software. That new paradigm is stream processing.
Streaming Data with Apache Kafka
The toolset for building scalable data systems is maturing, having adapted well to our decades-old paradigm of update-in-place databases. We ingest events, we store them in high-volume OLTP databases, and we have new OLAP systems to analyze them at scale—even if the size of our operation requires us to grow to dozens or hundreds of servers in the distributed system. But something feels a little dated about the store-and-analyze paradigm, as if we are missing a new architectural insight that might more efficiently distribute the work of storing and computing the events that happen to our software. That new paradigm is stream processing.
Four Distributed Systems Architectural Patterns
Developers and architects are increasingly called upon to solve big problems, and we are able to draw on a world-class set of open source tools with which to solve them. Problems of scale are no longer consigned to the web’s largest companies, but are increasingly a part of ordinary enterprise development. At the risk of only a little hyperbole, we are all distributed systems engineers now.
Managing Schemas in Kafka
On the inside, Kafka is schemaless, but there is nothing schemaless about the worlds we live in. Our languages impose type systems, and the objects in our business domains have fixed sets of properties and semantics that must be obeyed. Pretending that we can operate without competent schema management does us no good at all.