Architecting production IoT analytics
Who is this presentation for?Data engineers, data architects, developers
Analyzing IoT 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. When you look at technology and data engineering choices, even in companies with wildly different use cases and requirements, you see something surprising: successful production IoT architectures show a remarkable number of similarities.
Paige Roberts drills into the data architectures in a selection of companies like Philips, Anritsu, and OptimalPlus. Each company, regardless of industry or use case, has one thing in common: highly successful IoT analytics programs in large-scale enterprise production deployments. By comparing the architectures of these companies, you’ll see the commonalities and gain a deep understanding of why certain architectural choices make sense in a variety of IoT applications.
- General knowledge of the data and infrastructure requirements of large-scale analytics projects
What you'll learn
- Learn how 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, insulate your company from some of the impact of rapid change in data management technology, learn from other companies' mistakes and successes so you don't have to reinvent the wheel, and choose an architecture that will help ensure AI and ML projects make it into production where they have real impact
Paige Roberts is an open source relations manager at Vertica, where she promotes understanding of Vertica, MPP data processing, open source, and how the analytics revolution is changing the world. In two decades in the data management industry, she’s worked as an engineer, a trainer, a marketer, a product manager, and a consultant.
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