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Portfolio Architecture Examples: Edge Collection

This article is a continuation of a series of posts about our project named Portfolio Architectures. A previous post, Portfolio Architecture Examples: Healthcare Collection, begins with a project overview, introduction, and examples of tooling and workshops available for the project.  You may want to refer back to that post to gain insight into the background of Portfolio Architectures before reading further.  

Edge Collection

The collection featured today focuses on edge computing architectures. There are currently five architectures in this collection, and we'll provide a short overview of each, leaving the in-depth exploration as an exercise for the reader.

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Apache Kafka Landscape for Automotive and Manufacturing

Before the Covid pandemic, I had the pleasure of visiting "Motor City" Detroit in November 2019. I met with several automotive companies, suppliers, startups, and cloud providers to discuss use cases and architectures around Apache Kafka. A lot has happened. Since then, I have also met several OEMs and suppliers in Europe and Asia. As I finally go back to Detroit this January 2022 to meet customers again, I thought it would be a good time to update the status quo of event streaming and Apache Kafka in the automotive and manufacturing industry.

Today, in 2022, Apache Kafka is the central nervous system of many applications in various areas related to the automotive and manufacturing industry for processing analytical and transactional data in motion across edge, hybrid, and multi-cloud deployments. This article explores the automotive event streaming landscape, including connected vehicles, smart manufacturing, supply chain optimization, aftersales, mobility services, and innovative new business models.

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Microsoft Cloud for Retail: Architect Perspective

2020 was the year in which we experienced disruptive changes at a pace and a scale that we could never have imagined. COVID-19 caused disruptions in product supply and demand, in the labor pool, and consumer spending. But it also allowed many sectors to embrace digitalization like never before.

Retail is 31% of the world’s GDP, and that data is the demand signal for the world. In 2020, the retail industry faced challenges but also opportunities. First, the retail sector accelerated the already underway transition from physical retail to e-commerce quickly.

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Microsoft Cloud for Manufacturing: An Architecture Perspective

Manufacturers around the globe are becoming more agile and adaptable. Post-COVID 2020 has been a year that we will not soon forget. 

This interference has led to the high demand for innovation, fast delivery, and better user experience. Filled with unimaginable change caused by the pandemic, the manufacturing industry witnessed a perfect storm, a significant disruption in terms of business continuity, operational visibility, remote work, employee safety, and the list goes on. However, businesses have responded, adapted, and are recovering.  

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Apache Kafka for Industrial IoT and Manufacturing 4.0

This post explores use cases and architectures for processing data in motion with Apache Kafka in Industrial IoT (IIoT) across verticals such as automotive, energy, steel manufacturing, oil&gas, cybersecurity, shipping, logistics. Use cases include predictive maintenance, quality assurance, track and track, real-time locating system (RTLS), asset tracking, customer 360, and more. Examples include BMW, Bosch, Baader, Intel, Porsche, and Devon.

Why Kafka Is a Key Piece of the Evolution for Industrial IoT and Manufacturing

Industrial IoT was a mess of monolithic and proprietary technologies in the last decades. Modbus, Siemens S7, SCADA, and similar "concepts" controlled the industry. Vendors locked in enterprises by intentionally building incompatible products without open interfaces. These systems still run on Windows XP or similar non-supported outdated operating systems and without security in mind.

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Apache Kafka and MQTT (Part 3 of 5) – Manufacturing 4.0 and Industrial IoT

Apache Kafka and MQTT are a perfect combination for many Industrial IoT use cases. This blog series covers the pros and cons of both technologies. Various use cases across industries, including connected vehicles, manufacturing, mobility services, and smart city are explored. The examples use different architectures, including lightweight edge scenarios, hybrid integrations, and serverless cloud solutions. This post is part three: Manufacturing, Industrial IoT, and Industry 4.0.

Apache Kafka + MQTT Blog Series

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5 Tech Trends Driving Growth in Automotive Manufacturing

Introduction

New and emerging technologies are playing a major role in the future of automotive manufacturing. As tech like smart robotics and the Industrial Internet of Things (IIoT) transforms automaking at large, information technology is becoming an increasingly important driver of growth in the industry.

At the same time, shifting consumer preferences and the growing importance of sustainability in the industry are also having a significant impact on manufacturing processes.

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Augmented Reality Demo With Apache Kafka and Machine Learning

Augmented Reality (AR) and Virtual Reality (VR) get traction across industries far beyond gaming. Retail, manufacturing, transportation, healthcare, and other verticals leverage it more and more. This blog post explores a retail demo that integrates a cutting-edge augmented reality mobile shopping experience with the backend systems via the event streaming platform Apache Kafka.

Augmented Reality (AR) and Virtual Reality (VR)

Augmented reality (AR) is an interactive experience of a real-world environment where the objects in the real world are enhanced by computer-generated perceptual information. AR is a system that fulfills three basic features: a combination of real and virtual worlds, real-time interaction, and accurate 3D registration of virtual and real objects.

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Smart Factory with Apache Kafka and 5G Campus Networks

The Fourth Industrial Revolution (also known as Industry 4.0) is the ongoing automation of traditional manufacturing and industrial practices using modern smart technology. Event Streaming with Apache Kafka plays a key role in processing massive volumes of data in real-time in a reliable, scalable, and flexible way of integrating with various legacy and modern data sources and sinks. This blog post explores Apache Kafka's relationship to modern telco infrastructures that leverage private 5G campus networks for Industrial IoT (IIoT) and edge computing.

Event Streaming with Kafka at the Disconnected Edge

Apache Kafka is the new black at the edge.

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A Rapid Overview of ISA-88 and How It Aligns With ISA-95 and IIoT Platforms

ISA-88 is a long-standing standard for managing batch processes, while ISA-95 is focused on defining the progressive complexity of information that is expected to be available at each layer.

Often, interconnect between the two standard definitions and further the evolving IIOT platform definitions cause some degree of confusion as manufacturing firms grapple with defining and shaping their IT strategies and new tech/platform/apps adoption frameworks.

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IoT Architectures for Digital Twin With Apache Kafka

A digital twin is a virtual representation of something else. This can be a physical thing, process or service. This post covers the benefits and IoT architectures of a Digital Twin in various industries and its relation to Apache Kafka, IoT frameworks and Machine Learning. Kafka is often used as a central event streaming platform to build a scalable and reliable digital twin and digital thread for real-time streaming sensor data.

I already blogged about this topic recently in detail: Apache Kafka as Digital Twin for Open, Scalable, Reliable Industrial IoT (IIoT). Hence that post covers the relation to Event Streaming and why people choose Apache Kafka to build an open, scalable and reliable digital twin infrastructure.

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Modernizing IT Infrastructure for Manufacturing Organizations With Hyperconvergence: Part 2

You can find Part 1 here.

Hyperconvergence is a term that is gaining rapid interest across the manufacturing industry due to the undeniable benefits it has delivered to IT professionals seeking to modernize their data center, or as is a popular buzzword today ― "transform." Today, in particular, the manufacturing industry is looking to hyperconvergence for the potential benefits it can provide to its emerging and growing use of IoT and its growing need for edge computing systems.

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Deep Learning for Manufacturing: Overview and Applications

Deep learning

Introduction to Deep Learning for Manufacturing

Before getting into the details of deep learning for manufacturing, it’s good to step back and view a brief history. Concepts, original thinking, and physical inventions have been shaping the world economy and manufacturing industry since the beginning of the modern era, i.e. early 18th century.

Ideas of economies-of-scale by the likes of Adam Smith and John Stuart Mill, the first industrial revolution and steam-powered machines, electrification of factories and the second industrial revolution, and the introduction of the assembly line method by Henry Ford are just some of the prime examples of how the search for high efficiency and enhanced productivity have always been at the heart of manufacturing.

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Here’s Why Full-Stack Development Is Critical for IoT

When it comes to IoT development, full-stack engineers are likely the way to go.

The Internet of Things (IoT) is in the process of completely revolutionizing our daily routines, interactions with appliances, electronics, and even how we transport ourselves.

Companies like Philips, Xiaomi, Belkin, etc. have jumped into making IoT-capable smart devices, including devices like light bulbs, switches, air purifiers, and even general household appliances like Internet-enabled refrigerators and washing machines.