DZone IoT Zone

From Polling to PubSub: Building an Asynchronous OPC UA Stack in Python

Harshith Narasimhan Srivatsa — Fri, 03 Jul 2026 18:00:00 GMT

Industrial control systems are generating more data than ever before, but the Python tooling used to process this telemetry often encounters severe performance constraints. Traditional OPC UA libraries are built around synchronous, polling-based Client and Server architectures. When industrial networks scale to thousands of sensors broadcasting high-frequency data, these synchronous Python implementations choke. To handle this modern many-to-many topology, developers need a native Publisher and Subscriber solution that does not block the execution thread while waiting for network packets.

For Python developers unfamiliar with industrial protocols, OPC UA PubSub (IEC 62541-14) is a standard that decouples data producers from consumers by allowing devices to broadcast telemetry via stateless middleware like UDP Multicast. For industrial engineers new to Python concurrency, asyncio is a standard library that uses an event loop to handle thousands of simultaneous network operations concurrently without the heavy overhead of traditional threading.

Solving Data Traffic Jams in Your Network

Sascha Neumeier — Mon, 22 Jun 2026 16:00:00 GMT

Stop, start. Stop, start. Nothing brings data flows to a grinding halt (or raises an admin’s blood pressure) quite like network congestion.

The unwanted, unexpected extra step in an information request or response operation chain is a telltale sign that something’s changed or isn’t working in your infrastructure. And heavier traffic is more than just an inconvenience – it’s a multifaceted problem with knock-on business effects that falls upon admins to identify and fix.

Parallel Kafka Batch Processing With Kotlin Coroutines in Spring Boot

Erkin Karanlık — Tue, 16 Jun 2026 19:00:00 GMT

Managing high-volume message traffic in distributed architectures is crucial. Efficient use of database and CPU resources is also very important. There are structures that allow us to receive messages in batches. The default Spring Kafka "BatchMessageListener" structure addresses this need. However, the processing of these messages often goes through a sequential bottleneck.

This article will discuss the structure and usage of Kotlin Coroutines in detail. We will examine how to maximize Kafka message processing performance using Structured Concurrency principles and Resource Throttling techniques.

Bringing Intelligence Closer to the Source: Why Real-Time Processing is the Heart of Edge AI

Jitendra Bafna — Tue, 26 May 2026 17:00:10 GMT

Artificial Intelligence is rapidly becoming a part of everyday devices — smartphones, cars, cameras, and even home appliances. Traditionally, these systems rely on cloud servers to send, process, and analyze data before making decisions, which increases latency and delays responses. However, many applications require instant decision-making, where even a slight delay can be critical. In such scenarios, relying on network connectivity is not always practical, and decisions need to be made locally on the device itself.

This has led to a growing shift toward running intelligence directly on devices, making real-time local processing more important than ever. In this article, we’ll explore why this shift matters and how it is shaping the future of modern intelligent systems.

Building Enterprise-Grade Real-Time IoT Dashboards with Vue 3, MQTT, and Kafka

Venkata Sandeep Dhullipalla — Tue, 26 May 2026 15:00:15 GMT

The convergence of IoT, real-time data streaming, and modern frontend frameworks is redefining how engineers build enterprise monitoring systems. Over the course of designing and leading the Device IoT Platform — an enterprise-grade solution for real-time monitoring, configuration, and diagnostics of thousands of distributed network devices — I encountered and solved a core architectural challenge: how do you build a frontend dashboard that can handle hundreds of concurrent device telemetry streams without sacrificing performance, maintainability, or user experience?

This article shares the architectural patterns, technology decisions, and hard-won lessons from that journey — covering the full stack from MQTT ingestion to Vue 3 reactivity to Kafka-backed event processing.

Scaling Cloud Data Automation: A Practical Guide to Open Table Formats

Sandeep Batchu — Mon, 25 May 2026 20:00:00 GMT

When we talk about data analytics the way we set up our tables is really important. This is because it can make a difference, in how well our systems work and how fast they can grow.

Data analytics and Open Table Formats go hand in hand. Open Table Formats are a part of how we build our data systems today. They make it easy to work with systems. Get more out of our data.

Edge Computing in Utility IoT: Two Architecture Patterns That Actually Work

Yevheniia Mala — Fri, 22 May 2026 12:00:00 GMT

When centralized control architectures were designed, power flowed from large generation plants down to passive consumers, utilities managed hundreds of large assets, data volumes were modest, and connectivity was reliable at substations.

Few of these assumptions hold today. Power flows in both directions as rooftop solar and battery storage inject back into the distribution network. Utilities now coordinate millions of small, variable, distributed assets instead of hundreds of large ones. Data volumes have multiplied by orders of magnitude as smart meters, sensors, and distributed energy resource (DER) controllers generate continuous streams.

The Network Attach Problem Nobody Warns You About

SESHA KIRAN GONABOYINA — Thu, 14 May 2026 20:00:00 GMT

We have been here before.

When NB-IoT went nationwide at a major U.S. operator in 2018, enterprise teams discovered that activating large device fleets simultaneously did things to the network that nobody in the procurement conversation had mentioned. The spec sheets were silent on it. The vendor demos didn't surface it. It showed up on activation day, at scale, in production.

Ten Years of Beam: From Google's Dataflow Paper to 4 Trillion Events at LinkedIn

Abgar Simonean — Thu, 14 May 2026 13:00:01 GMT

In August 2015, a team of engineers at Google published a paper with a title so long it barely fits on a conference slide: "The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing." The opening line was:

We as a field must stop trying to groom unbounded datasets into finite pools of information that eventually become complete.

Beyond Caching: Content Delivery Networks

Ammar Husain — Mon, 27 Apr 2026 16:00:00 GMT

Consider a user in Australia browsing their social media feed to catch up with friends in Europe and America. The media shared by friends takes a considerable time to load despite the user having a reasonably fast internet connection — while the same content loads instantly for those browsing from within Europe.

Consider another user in America trying to watch a live concert in Europe on their device. The broadcast is interrupted briefly but frequently. However, for the European audience, the broadcast is seamless.

Data Processing for Real Estate: Enabling Smart Analysis and Decision-Making

Peter Leo — Tue, 21 Apr 2026 15:00:00 GMT

Do you think real estate success still depends on gut feelings and market hunches? Those days are over. Data analysis has become the lifeline of modern real estate operations and has changed how property valuations, market trends, and investment decisions work.

Administrators in real estate firms now deal with diverse sets of information from various sources, including property records, market transactions, demographic changes, economic indicators, and customer interactions. These datasets remain unused without appropriate data processing techniques. Valuable insights remain concealed under heaps of unstructured data.

Swift: The Complete Guide to Error Handling in the Network Layer

Pavel Andreev — Mon, 20 Apr 2026 15:00:00 GMT

In my previous article, we explored how to construct a robust, abstract network layer using Clean Architecture. The response was fantastic, but I received a recurring piece of feedback: the error handling was a bit too thin for a real-world production environment.

Categorizing HTTP Status Codes

To provide a more granular and descriptive way of handling network events, I decided to categorize HTTP status codes into specific enums. This approach ensures that our logic is both type-safe and highly readable. By referencing the MDN Web Docs, I mapped out each response category to its own structure.

Part II: The Network That Doesn't Exist: Zero Trust, Service Meshes, and the Slow Death of Perimeter Security

Igboanugo David Ugochukwu — Fri, 17 Apr 2026 12:00:18 GMT

The conversation that reordered my understanding of enterprise network security happened in a conference room in London in early 2019. The CISO of a mid-size financial services firm — precise, methodical, someone whose threat modeling I trusted — was describing her organization's response to a pen test finding. The testers had gotten onto one internal server through a phishing email. From that single initial access point, within seventy-two hours, they had lateral movement access to fourteen other systems, including two that handled customer account data.

The perimeter had been intact throughout. The firewall logs showed nothing anomalous crossing the network boundary. Everything that happened after the initial email was internal traffic, authenticated by the fact that it came from inside the network. There was no enforcement, no verification, nothing that asked whether this particular server had any business talking to those other fourteen.

Spark on AmpereOne® M Arm Processors Reference Architecture

RamaKrishna Nishtala — Mon, 06 Apr 2026 20:23:05 GMT

Introduction

Arm technology now powers a broad spectrum of on-premises and cloud server workloads. Building on Ampere Computing's previous reference architecture, which demonstrated that Apache Spark on Ampere Altra – 128C (Ampere Altra 128 Cores) processors delivers superior performance per rack, lower power consumption, and optimized CapEx and OpEx, this paper evaluates and extends that analysis to showcase Spark performance on the latest generation of AmpereOne® M processors.

Scope and Audience

This document describes the process of setting up, tuning, and evaluating Spark performance using a testbed powered by AmpereOne® M processors. It includes a comparative analysis of the performance benefits of the 12-channel AmpereOne® M processors relative to their predecessors, specifically Ampere Altra – 128C processors. Additionally, the paper examines the Spark performance improvements achieved by using a 64KB page-size kernel over standard 4KB page-size kernels.

Hadoop on AmpereOne Reference Architecture

RamaKrishna Nishtala — Fri, 03 Apr 2026 22:25:18 GMT

Ampere processors with Arm architecture deliver superior power efficiency and cost advantages compared to traditional x86 architecture. Hadoop, with its core components and broader ecosystem, is fully compatible with Arm-based platforms.

Ampere Computing has previously published a comprehensive reference architecture demonstrating Hadoop deployments on Ampere® Altra® M processors. This paper builds on that foundation and extends the analysis by highlighting Hadoop performance on the next generation of AmpereOne® M processor.

Stop Leap-Second AI Drift in IoT Streams With PySpark

Ram Ghadiyaram — Fri, 27 Mar 2026 14:30:00 GMT

Fintech and Enterprise platforms ingest massive volumes of timestamped data (big data) from IoT devices such as payment terminals, wearables, and mobile apps. Accurate timing is essential for fraud detection, risk scoring, and customer analytics. Yet a subtle irregularity called the leap second can corrupt timestamps and trigger AI drift, gradually degrading model performance in production.

In this article, I will attempt to explain clearly what drift types are and how they can be prevented, based on my research paper. Details can be found here. Let's start.

How Piezoelectric Energy Harvesting Is Solving the Battery Waste Crisis in Industrial IoT

Emily Newton — Wed, 18 Mar 2026 18:00:01 GMT

High-temperature energy harvesting exposes the hidden cost of batteries across Industrial Internet of Things (IIoT) deployments, especially in environments where heat and access constraints shorten battery life and raise maintenance risk. Fit-and-forget architectures matter in hazardous and remote locations. Battery replacement introduces downtime and unpredictable operating costs that scale with fleet size, while thermal extremes further reduce cell reliability.

Energy harvesting and self-powered sensors emerge as engineering-driven solutions that align with long-term system availability and life-cycle performance. Battery-less IIoT designs become a practical response to operational constraints rather than a sustainability narrative.

DataFlow — An Open-Source Data Preparation System Accelerating LLM Training

Nan Xiang — Wed, 11 Mar 2026 17:00:00 GMT

Competition among large language models (LLMs) has intensified significantly over the past two years, with many believing that their core competitiveness lies in algorithms. However, this is not the case. The current open-source ecosystem has made mainstream architectures increasingly transparent — model structures such as Llama, GPT, and Gemma can all be publicly reproduced, and the competitive edge at the algorithmic level is rapidly eroding. The real competitive barrier actually exists at a more fundamental level — data.

Data is the sole source of knowledge for LLMs, and data quality determines a model's "emotional intelligence" and "intelligence quotient." This means the development of LLMs has largely relied on large-scale, high-quality training data. However, most mainstream training datasets and their processing workflows remain undisclosed, and the scale and quality of publicly available data resources are still limited. This poses significant challenges for the community in building and optimizing training data for LLMs.

Breaking the Vendor Lock in Network Automation: A Pure Python Architecture

Dippu Kumar Singh — Mon, 16 Feb 2026 18:00:00 GMT

In the world of Infrastructure as Code (IaC), servers are a solved problem. We spin up thousands of VMs with a single script. But the network layer? That often remains a manual bottleneck.

The reason is the “Multi-Vendor Trap.” Enterprise networks are rarely homogeneous. They are a patchwork of routers, switches, and load balancers from different vendors (Cisco, Juniper, F5), each with its own proprietary CLI syntax. This fragmentation makes standard automation difficult, leading to long lead times (often weeks) just to open a VLAN or update a firewall rule.

A Pattern for Intelligent Ticket Routing in ITSM

Dippu Kumar Singh — Tue, 10 Feb 2026 17:00:00 GMT

In the world of IT Service Management (ITSM), the Service Desk often acts as a human router. A ticket comes in, a coordinator reads it, checks a spreadsheet to see who is on shift, remembers who is good at databases versus networking, and then assigns the ticket.

This process is slow, subjective, and prone to cherry-picking (where engineers grab easy tickets and ignore hard ones). It creates a bottleneck that increases Mean Time to Resolution (MTTR).