Lies Enterprise Architects Tell

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Event

Lies Enterprise Architects Tell

5 March 2019

London

Added 31-Jul-2019

Join us for our next London Apache Kafka meetup on March 5th from 6:00pm. The address, agenda and speaker information can be found below. See you there!

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Agenda:
6:00pm: Doors open
6:00pm - 6:30pm: Pizza, Drinks and Networking
6:30pm - 7:15pm: Gwen Shapira, Confluent
7:15pm - 7:45pm: Additional Q&A & Networking

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Speaker:
Gwen Shapira

Bio:
Gwen Shapira is a principal data architect at Confluent, where she helps customers achieve success with their Apache Kafka implementation. She has 15 years of experience working with code and customers to build scalable data architectures, integrating relational and big data technologies. Gwen currently specializes in building real-time reliable data-processing pipelines using Apache Kafka. Gwen is an Oracle Ace Director, the coauthor of Hadoop Application Architectures, and a frequent presenter at industry conferences. She is also a committer on Apache Kafka and Apache Sqoop. When Gwen isn’t coding or building data pipelines, you can find her pedaling her bike, exploring the roads and trails of California and beyond.

Title:
Lies Enterprise Architects Tell

Abstract: Lets face it - we are all liars. We often lie unintentionally and most of all - we lie to ourselves. I’ve spent the last 10 years working with enterprise architects intent on modernizing their data infrastructure, and I’ve heard many “facts” that turned out to be… less than perfectly accurate. Self-deception about state of the industry, our requirements and our capabilities can lead us to make bad choices, which leads us to build bad architectures and often leads to bad business outcomes.
If you say or hear phrases like “we have big data”, “we don’t have big data”, “this business app must be real-time” and “hybrid-cloud doesn’t exist” - you may work for an organization that can use a bit of reality check. In this talk, Gwen Shapira, principal data architect at Confluent, will share common enterprise architecture myths that did not survive contact with reality and offer some advice on how to design good data architecture given our inherent capacity for self-deception.

 

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