Conference Program


Architecting Production IoT Analytics

12:20 PM - 1:00 PM PDT, June 18, 2020 | Track 1

What works in production is the only technology criteria that matters. When you look at technology and data engineering choices, even in companies with wildly different Internet of Things use cases, you see something surprising: Successful production Internet of Things architectures show a remarkable number of similarities.
Machine Learning analyses on Internet of Things data has broad applications in a variety of industries from smart buildings to smart farming, from network optimization for telecoms to preventative maintenance on expensive medical machines or factory robots. Among other things, every successful IoT architecture includes a distributed streaming pub/sub backbone.
Join us as we drill into the data architectures in a selection of companies like Philips, Anritsu, and Optimal+. Each company, regardless of industry or use case, has one thing in common: highly successful IoT analytics programs in large scale enterprise production deployments.
Come study IoT architectures that work, discuss what drove the decisions behind them, what decisions make sense across broad streaming machine learning use cases, and why.

Learn to
- Judge large scale IoT technology choices critically and objectively
- Avoid some of the traps that have cost other companies time and money and caused so many implementations to fail
- Choose an architecture that will help ensure AI and ML projects make it into production where they have a real impact

Use Case

Paige Roberts

In 23 years in the data management industry, I’ve worked as an engineer, a trainer, a support technician, a technical writer, a marketer, a product manager, and a consultant.I’ve built data engineering pipelines and architectures, documented and tested open source analytics implementations, spun up Hadoop clusters, picked the brains of stars in data analytics and engineering, worked with a lot of different industries, and questioned a lot of assumptions.Now, I promote understanding of Vertica, distributed data processing, open source, high scale data engineering, and how the analytics revolution is changing the world.