DZone IoT Zone

Best Practices for Handling Bad Data in Stream Processing Platforms

Gautam Goswami — Wed, 02 Sep 2026 17:00:00 GMT

Today, stream processing platforms facilitate the real-time analysis of data flowing continuously from Internet of Things (IOT) devices, financial transactions, web applications and servers at banks, manufacturing equipment, logistical systems in warehouses and ships, as well as customer activities with conversational agents on web portals. Streaming frameworks like Apache Kafka, Apache Flink, Apache Spark Structured Streaming, and stream databases are empowering business folks to process millions of events in real time.

But what your streaming platform is worth depends exclusively on the quality of data fed into it. An event that is malformed, a duplicate message, any missing field, or an invalid timestamp can lead to incorrect analytics generation, false alert triggers, bursts of alerts, and even application crashes. Batch processing allows for data to be cleaned before execution, but stream-processing requires that validation and corrections occur while the data is flowing. Thus, establishing a strong data quality strategy is a core necessity of any event-driven architecture.

Securing Branch Networks With Firewalls, VPNs, IDS/IPS, and Identity-Based Access

Kamal chand Narra — Wed, 05 Aug 2026 16:00:00 GMT

Branch networks no longer behave like quiet extensions of a single headquarters LAN. They terminate local user traffic, break out directly to the internet for SaaS, maintain persistent connections back to core systems, and increasingly host devices that are operationally important even when central resources are unavailable. NIST notes that the enterprise network landscape has shifted because of cloud services, geographic dispersion, and changes in application design, while zero trust guidance emphasizes that network location is no longer the primary signal of trust.

In practice, that means a branch cannot be secured by treating the site-to-site tunnel as a blanket trust boundary. The branch edge has to make explicit policy decisions about which flows are allowed, which flows are encrypted, which flows are inspected, and which identities are entitled to touch which resources.

Why Do Some Proxies Work Fine for Search But Fail Once You Start Filtering Results?

xiyun chen — Tue, 21 Jul 2026 16:00:00 GMT

Automated web scraping, market intelligence data gathering, and large-scale search engine extraction platforms frequently hit an invisible wall. A collection of proxy IPs might execute initial search queries flawlessly, yielding a standard 200 OK status code and complete HTML payloads. However, the exact same backend application might immediately throw 403 Forbidden errors, encounter endless CAPTCHAs, or receive empty JSON responses the millisecond it applies structural filters — such as sorting by price, filtering by date range, or toggling deep category facets.

To the application engineer, this behavior feels contradictory. If a network endpoint successfully authenticates, circumvents initial perimeter defenses, and extracts data from a root search page, why does a simple query parameter modifier trigger an immediate failure?

Architecting Autonomous Network Ecosystems: From Reactive Monitoring to Agentic AI Orchestration

Daniel Oh — Wed, 15 Jul 2026 15:00:00 GMT

Agentic AI systems represent a paradigm shift in network operations, facilitating the transition from traditional reactive monitoring to fully autonomous management frameworks. For global infrastructure leaders, these specialized AI agents serve as persistent digital engineers, providing round-the-clock expertise across deployment, maintenance, and complex troubleshooting lifecycles.

The following blueprint delineates the strategic application of agentic AI within a global enterprise network operations environment.

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.