Tutorial: Capturing and processing streaming data with Apache Kafka
Tuesday June 9th 9am-12pm
Kafka provides high throughput, low latency pub/sub messaging and many large companies are quickly adopting it to handle their realtime and streaming data at large scale. But what can you use it for, and how do you get started? Come to Confluent’s tutorial conducted by our first engineer, Ewen Cheslack-Postava on June 9th at 9am to find out.
We’ll start out with an overview of Kafka starting from the basics. You’ll learn about Kafka’s unifying abstraction, a partitioned and replicated low-latency commit log. Then we’ll discuss concrete applications of Kafka across multiple domains so you can see how Kafka can work for your company.
With a solid understanding of Kafka fundamentals, you’ll develop an end-to-end application that performs anomaly detection on streaming data to see how quickly you can get up and running with Kafka. The implementation will be broken into two parts. First, you’ll take an existing front-end application and instrument it with a Kafka producer to store user activity events in Kafka. Second, you’ll build a distributed, fault tolerant service that detects and reports anomalies in the activity data.
By the end of the session, you’ll understand and be able to apply all the core functionality of Kafka.
And, the fun doesn’t stop there because you can still attend…
Thursday June 11th 1:40pm-2.30pm
If you are curious about how Kafka is adopted at large scale in production or if you are looking to learn how to adopt Kafka in practice, attend my talk at 1.40pm on June 11th.
Since we open sourced Kafka more than 4 years ago, it has been adopted very widely from web companies like Uber, Netflix, LinkedIn to more traditional enterprises like Cerner, Goldman Sachs and Cisco. These companies use Kafka in a variety of ways – as the infrastructure for ingesting high-volume log data into Hadoop, to collect operational metrics for monitoring and alerting applications, for low latency messaging use cases, and to power near realtime stream processing.
In this talk, you will learn how Kafka’s unique architecture allows it to be used both for real time processing and as a bus for feeding batch systems like Hadoop. You will also learn how Kafka is fundamentally changing the way data flows through an organization and presents new opportunities for processing data in real time that were not possible before. I will discuss how Kafka impacts the way data is integrated across a variety of data sources and systems.
Lastly, you can expect to learn how you can go about adopting Kafka in your company to leverage real-time data at scale in practice.