Designing And Deploying IoT Analytics For Industrial Applications At Vopak
Data Engineering Podcast
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The Challenges of Machine Learning Platforms
Migration was something that we were not expecting from the beginning to be so challenging, you know. The operato part of it, i was not expecting, or, like example, fixing data, right? We focus a lot of in investing the data, understanding the opic way, partical, almot best pactical works and all the industrial side ofthe things work.
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Summary
Industrial applications are one of the primary adopters of Internet of Things (IoT) technologies, with business critical operations being informed by data collected across a fleet of sensors. Vopak is a business that manages storage and distribution of a variety of liquids that are critical to the modern world, and they have recently launched a new platform to gain more utility from their industrial sensors. In this episode Mário Pereira shares the system design that he and his team have developed for collecting and managing the collection and analysis of sensor data, and how they have split the data processing and business logic responsibilities between physical terminals and edge locations, and centralized storage and compute.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I’m interviewing Mário Pereira about building a data management system for globally distributed IoT sensors at Vopak
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Vopak is and what kinds of information you rely on to power the business?
- What kinds of sensors and edge devices are you using?
- What kinds of consistency or variance do you have between sensors across your locations?
- How much computing power and storage space do you place at the edge?
- What level of pre-processing/filtering is being done at the edge and how do you decide what information needs to be centralized?
- What are some examples of decision-making that happens at the edge?
- Can you describe the platform architecture that you have built for collecting and processing sensor data?
- What was your process for selecting and evaluating the various components?
- How much tolerance do you have for missed messages/dropped data?
- How long are your data retention periods and what are the factors that influence that policy?
- What are some statistics related to the volume, variety, and velocity of your data?
- What are the end-to-end latency requirements for different segments of your data?
- What kinds of analysis are you performing on the collected data?
- What are some of the potential ramifications of failures in your system? (e.g. spills, explosions, spoilage, contamination, revenue loss, etc.)
- What are some of the scaling issues that you have experienced as you brought your system online?
- How have you been managing the decision making prior to implementing these technology solutions?
- What are the new capabilities and business processes that are enabled by this new platform?
- What are the most interesting, innovative, or unexpected ways that you have seen your data capabilities applied?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on building an IoT collection and aggregation platform at global scale?
- What do you have planned for the future of your IoT system?
Contact Info
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
- Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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Links
- Vopak
- Swinging Door Compression Algorithm
- IoT Greengrass
- OPCUA IoT protocol
- MongoDB
- AWS Kinesis
- AWS Batch
- AWS IoT Sitewise Edge
- Boston Dynamics
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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