Bring your own laptop with a 64-bit CPU, at least 3 GB free RAM, and VirtualBox 4.3.x installed.
Please make sure to download and decompress the Virtual Machine image found at http://tiny.smokinghand.com/workshopkafka.7z BEFORE arriving onsite.
See how Kafka can help you harness the value of stream data in your organization! During this three-hour tutorial we’ll discuss what Kafka is, and its emerging critical role in the modern data management and distribution pipeline.
We’ll also discuss key architectural concepts and developer APIs. The tutorial includes hands-on labs where you’ll build an application that can publish data to Kafka, and subscribe to receive data from Kafka.
Here’s the high-level agenda for the tutorial:
This tutorial is ideal for application developers, extraction-transformation-load (ETL) developers, or data scientists who need to interact with Kafka clusters as a source of, or destination for, stream data.
Jesse Anderson is a data engineer, creative engineer, and managing director of the Big Data Institute. Jesse trains employees on big data—including cutting-edge technology like Apache Kafka, Apache Hadoop, and Apache Spark. He’s taught thousands of students at companies ranging from startups to Fortune 100 companies the skills to become data engineers. He’s widely regarded as an expert in the field and recognized for his novel teaching practices. Jesse is published by O’Reilly and Pragmatic Programmers and has been covered in such prestigious media outlets as the Wall Street Journal, CNN, BBC, NPR, Engadget, and Wired. You can learn more about Jesse at Jesse-Anderson.com.
Ewen Cheslack-Postava is an engineer at Confluent building a stream data platform based on Apache Kafka to help organizations reliably and robustly capture and leverage all their real-time data. Ewen received his PhD from Stanford University, where he developed Sirikata, an open source system for massive virtual environments. His dissertation defined a novel type of spatial query giving significantly improved visual fidelity and described a system for efficiently processing these queries at scale.
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