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Apache Kafka and Kafka Streams

  • February 7 · 6:00 PM
  • This location is shown only to members

Join us to learn more about Apache Kafka and Kafka Streams on February 7th from 6:00pm - 8:30pm, hosted by Object Partners.
1515 Central Avenue NE Suite 100, Minneapolis, MN 55413
There's some parking in the front and lots of parking in the rear. You'll need to walk to the front to get in if you park in the rear.

Please see the agenda and speaker information below. See you there!

Agenda
6:00pm: Doors open
6:00pm - 6:30pm: Networking (food and beverages provided by Object Partners)
6:30pm - 7:15pm: Presentation #1: John Holland, Object Partners
7:15pm - 8:00pm: Presentation #2: Jeremy Custenborder, Confluent
8:00pm - 8:30pm: Additional Q&A and Networking


First Talk

Speaker: John Holland, Chief Technologist, Object Partners

Bio: John Holland is the Chief Technologist of real-time data for Object Partners Inc. (OPI), a Minneapolis based consulting firm known for it's outstanding talent and ability to get things done. He has been consulting with them for the last 9 years crafting solutions for businesses, large to small. For the last few years he has been focused on real-time data systems.

Title: Simplifying Distributed Systems Using Apache Kafka

Abstract: With the rising popularity of micro service architectures and typical scaling patterns used at enterprises, distributed systems are becoming more common and complex. What was once a simple web server connected to a database now can entail multiple databases, caches and integrations to other services/systems. By using Apache Kafka one can take much of the integration complexity out of the system, reduce coupling between the different components, easily expand functionality without disruption and scale horizontally.
This presentation will cover patterns and concepts that can be used to achieve all of the above. There will be a quick overview of how Apache Kafka works, it’s differences from other messaging brokers and why that’s important. I’ll speak about the good, the bad and what was missed during my experience working with large distributed systems.
Second Talk

Speaker: Jeremy Custenborder,

Bio: Jeremy Custenborder is a Systems Engineer for Confluent, the company driving commercial Kafka support. He has consulted with big data systems since 2010 for various companies across the US. He currently lives in Austin, TX where he hides in the air conditioning for a large portion of the year.

Title: Introducing Kafka Streams

Abstract: Modern businesses have data at their core, and this data is changing continuously. How can we harness this torrent of information in real-time? The answer is stream processing, and the technology that has since become the core platform for streaming data is Apache Kafka. Among the thousands of companies that use Kafka to transform and reshape their industries are the likes of Netflix, Uber, PayPal, and AirBnB, but also established players such as Goldman Sachs, Cisco, and Oracle.
Unfortunately, today’s common architectures for real-time data processing at scale suffer from complexity: there are many technologies that need to be stitched and operated together, and each individual technology is often complex by itself. This has led to a strong discrepancy between how we, as engineers, would like to work vs. how we actually end up working in practice.
In this session we talk about how Apache Kafka helps you to radically simplify your data processing architectures. We cover how you can now build normal applications to serve your real-time processing needs — rather than building clusters or similar special-purpose infrastructure — and still benefit from properties such as high scalability, distributed computing, and fault-tolerance, which are typically associated exclusively with cluster technologies. Notably, we introduce Kafka’s Streams API, its abstractions for streams and tables, and its recently introduced Interactive Queries functionality. As we will see, Kafka makes such architectures equally viable for small, medium, and large scale use cases.

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