Tim Berglund
From Confluent
Tim is a teacher, author, and technology leader with Confluent, where he serves as the Senior Director of Developer Experience. He can frequently be found at speaking at conferences in the United States and all over the world. He is the co-presenter of various O’Reilly training videos on topics ranging from Git to Distributed Systems, and is the author of Gradle Beyond the Basics. He tweets as @tlberglund, blogs very occasionally at http://timberglund.com, is the co-host of the http://devrelrad.io podcast, and lives in Littleton, CO, USA with the wife of his youth and their youngest child, the other two having mostly grown up.
Architecture
The Database Unbundled: Commit Logs in an Age of Microservices
Microservice architectures provide a robust challenge to the traditional centralized database we have come to understand. In this talk, we’ll explore the notion of unbundling that database, and putting a distributed commit log at the center of our information architecture. As events impinge on our system, we store them in a durable, immutable log (happily provided by Apache Kafka), allowing each microservice to create a derived view of the data according to the needs of its clients. Event-based integration avoids the now-well-known problems of RPC and database-based service integration, and allows the information architecture of the future to take advantage of the growing functionality of stream processing systems like Kafka, allowing us to create systems that can more easily adapt to the changing needs of the enterprise and provide the real-time results we are increasingly being asked to provide.
Server Side Java
Building Streaming Microservices with Apache Kafka
Streaming is all the rage in the data space, but can stream processing be used to build business systems? Do Streaming and Microservices actually have anything in common? These are the questions we’ll explore in this talk by looking at how real-world Streaming Microservices are actually built. We'll explore how services collaborate using events instead of traditional REST calls. We'll take this idea a step further using Kafka’s Streams API: embedding the ability to join and process data right inside our services. Finally we'll build a fictitious system, from the ground up, using these tools and techniques, bridging the sync-async divide to form bounded contexts separated by an asynchronous, event-driven core.
Big Data & AI
Processing Streaming Data with KSQL
Apache Kafka is a de facto standard streaming data processing platform, being widely deployed as a messaging system, and having a robust data integration framework (Kafka Connect) and stream processing API (Kafka Streams) to meet the needs that common attend real-time message processing. But there’s more!
Kafka now offers KSQL, a declarative, SQL-like stream processing language that lets you define powerful stream-processing applications easily. What once took some moderately sophisticated Java code can now be done at the command line with a familiar and eminently approachable syntax. Come to this talk for an overview of KSQL with live coding on live streaming data.