“…I see more and more data flowing around and you need to apply and leverage it to add business value and be innovative in your business”

Part 1 of 2 

Part 1 of 2 - In this sensational streaming episode of the Beyond Buildings podcast, we get to talk to the rockstar that is Kai Waehner who is a Technology Evangelist and Big Data Analyst, helping Fortune 500 companies get started, get going, and succeeding in becoming cloud-native. He’s been working with data integration his whole life and has first hand seen the journey Big Data has taken during the last couple of decades. We get to learn more about how to combine slow data and fast data in the same platform, what the industrial IoT scene looks like, Railway Connectivity, and we go above and beyond where most buildings are today. Will there be a need to transfer millions of messages per second in the real estate industry? If so, how can it be done?

This is an episode where we might get a glimpse towards the future modern data strategies for Real Estate Portfolios, that are part of a Smart City context. We get to find out the relationship between open architectures and existing proprietary PLC and much much more. What an exceptional episode thanks to Kai, and I strongly recommend reading up on Streaming Platforms, Apache Kafka, and think about what the future demands from your business!

I’ve been following Kai Waehner for a couple of years now and I really like Kafka as an open-source technology as well as Confluent with what they are bringing to the table.

Going above and beyond real estate is important and it’s fascinating to know more about why will be hybrid cloud strategies in automotive and manufacturing, and that everything is moving towards distributed intelligence.

What do you think? Are there any similarities between real estate, industry 4.0, and the importance of local control?

Links:

Nicolas Waern, Podcast Creator/Smart World Architect

WINNIIO - Digital Consultancy

Kai Waehner Global Technology Advisor - Technology Evangelist/Field CTO at Confluent

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