# Consumer Published articles for Consumer. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## aulumu Introduces Nine iPhone 18 Pro Cases with Aramid-TPU Protection and Glow-in-the-Dark Designs DevFeed: [aulumu Introduces Nine iPhone 18 Pro Cases with Aramid-TPU Protection and Glow-in-the-Dark Designs]() Original publisher: [Read original article]() Author: Sarang Sheth Published: 2026-09-15T23:30:05Z Content type: article Language: en Sources: [Yanko Design]() Topics: [iphone]() Tags: [accessories](), [accessories-deals-product-design-aulumu-smartphone-case](), [aulumu](), [consumer](), [deals](), [design](), [iphone](), [product-design](), [products](), [protection](), [smartphone-case](), [tech]() ### AI overview The article examines aulumu's A18 collection of nine iPhone 18 Pro cases, highlighting different materials and design approaches. It focuses on the A18 Armor Shockproof Case, which combines 1500D aramid fiber with TPU for a lightweight build with added flexibility and cushioning. ### Source excerpt aulumu's iPhone 18 Pro Cases Survive Drops, Glow In The Dark, And Auto Cool Your Phone Picking a phone case is one of the most low-stakes high-stakes decisions in consumer tech. The stakes are low because it's a $40 purchase. The... ## HP ZBook Ultra G3a 16 Preview: 192GB of Unified Memory Aims for the Top of the Local AI Laptop Leaderboard DevFeed: [HP ZBook Ultra G3a 16 Preview: 192GB of Unified Memory Aims for the Top of the Local AI Laptop Leaderboard]() Original publisher: [Read original article]() Author: Brian Beeler Published: 2026-09-15T23:15:57Z Content type: article Language: en Sources: [StorageReview.com]() Topics: [Local AI](), [Hardware](), [GPU](), [cpu](), [Large Language Model]() Tags: [consumer](), [cpu](), [deepseek](), [dgx-spark](), [gemma](), [gpu](), [hardware](), [local-ai](), [models](), [ollama](), [windows](), [workstation]() ### AI overview StorageReview previews HP's pre-production ZBook Ultra G3a 16, a local AI laptop with 192GB of unified memory and up to 160GB assignable to its integrated GPU. The article examines its hardware and planned testing while noting that shipping-hardware benchmarks are not yet available. ### Source excerpt HP's ZBook Ultra G1a 14 holds the Best for Large Models spot on our Best Laptops for Local AI leaderboard because its 128GB of unified memory, 96GB of it assignable to the GPU, loaded models no discrete-GPU laptop could touch. The new HP ZBook Ultra G3a 16 raises that pool to 192GB with up to The post HP ZBook Ultra G3a 16 Preview: 192GB of Unified Memory Aims for the Top of the Local AI Laptop Leaderboard appeared first on StorageReview.com. ## MSI XpertStation WS300 Thermals: Why a 1,300W GB300 Doesn't Throttle on a Desk DevFeed: [MSI XpertStation WS300 Thermals: Why a 1,300W GB300 Doesn't Throttle on a Desk]() Original publisher: [Read original article]() Author: Brian Beeler Published: 2026-09-14T19:41:35Z Content type: article Language: en Sources: [StorageReview.com]() Topics: [Blackwell](), [cpu](), [GPU](), [Grace CPU](), [Nvidia](), [DGX Station]() Tags: [blackwell](), [consumer](), [dgx-station](), [gpu](), [grace-cpu](), [heat](), [memory](), [nvidia](), [performance](), [review](), [workstation]() ### AI overview The article explains why MSI's XpertStation WS300 can sustain a 1,300W GB300 Grace Blackwell Ultra Superchip without throttling on a desk. It attributes the thermal stability to cold plates covering the major heat-producing components, dual 360mm radiators, multiple fans, and a cooling loop rated above the system's nominal CPU and GPU load. ### Source excerpt The most common question we got about the MSI XpertStation WS300 after our review coalesces around one key theme. The GB300 Grace Blackwell Ultra Superchip is a 1,300W part that normally lives in a liquid-cooled rack, so what happens to thermals when you put it in a tower? The concern is fair: a GB300 system The post MSI XpertStation WS300 Thermals: Why a 1,300W GB300 Doesn't Throttle on a Desk appeared first on StorageReview.com. ## Message Queue vs Event Bus vs Broker DevFeed: [Message Queue vs Event Bus vs Broker]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-09-10T12:22:39Z Content type: article Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [systems](), [Latency]() Tags: [architecture](), [consumer](), [fraud-detection](), [latency](), [messages](), [notifications](), [payment](), [queue]() ### AI overview This article distinguishes message queues, event buses, and brokers by the meaning of the message. It explains that queues assign work to consumers, support retries and dead-letter queues, smooth traffic spikes, and can develop growing backlogs and higher latency under sustained overload. ### Source excerpt They often show up in the same architecture diagram, which makes them look interchangeable. They're not. ## HP ZGX Fury Is Now Orderable: GB300 Superchip, 748GB Unified Memory, and a Red Hat AI Factory Plan for the Edge DevFeed: [HP ZGX Fury Is Now Orderable: GB300 Superchip, 748GB Unified Memory, and a Red Hat AI Factory Plan for the Edge]() Original publisher: [Read original article]() Author: Brian Beeler Published: 2026-09-09T19:35:12Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [Artificial Intelligence](), [Inference](), [Blackwell](), [GPU](), [Nvidia](), [DGX Station](), [Grace CPU](), [Hardware](), [Fine-tuning](), [Availability](), [NVLink](), [NCCL]() Tags: [10gbe](), [ai](), [ai-factory](), [availability](), [blackwell](), [connectx](), [consumer](), [dgx-station](), [fine-tuning](), [gpu](), [grace-cpu](), [hardware](), [inference](), [nvidia](), [nvlink](), [nvme](), [red-hat](), [usb](), [workstation]() ### AI overview HP's ZGX Fury AI station is available to order with a GB300 Grace Blackwell Ultra Desktop Superchip, 748GB of unified memory, and up to 20 petaFLOPS of FP4 compute. HP positions it as a shared inference system for departments, factory floors, and branch offices, supported by a collaboration with Red Hat and NVIDIA to run Red Hat AI Factory with NVIDIA. ### Source excerpt HP's ZGX Fury AI station is now available to order, and HP paired the availability news with a collaboration with Red Hat and NVIDIA to put Red Hat AI Factory with NVIDIA on top of it. The ZGX Fury is HP's take on NVIDIA's DGX Station design, built around the GB300 Grace Blackwell Ultra Desktop The post HP ZGX Fury Is Now Orderable: GB300 Superchip, 748GB Unified Memory, and a Red Hat AI Factory Plan for the Edge appeared first on StorageReview.com. ## DGX Spark Windows Clue: This Week's Firmware Adds a Windows Boot Certificate as NVIDIA Details October RTX Spark PCs DevFeed: [DGX Spark Windows Clue: This Week's Firmware Adds a Windows Boot Certificate as NVIDIA Details October RTX Spark PCs]() Original publisher: [Read original article]() Author: Brian Beeler Published: 2026-09-05T00:25:04Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [DGX Spark](), [Windows](), [boot](), [Blackwell](), [Arm]() Tags: [arm](), [blackwell](), [boot](), [consumer](), [dgx-spark](), [secure-boot](), [update](), [windows](), [workstation]() ### AI overview A firmware update for a DGX Spark cluster added the Windows UEFI CA certificate, suggesting groundwork for Windows support on the platform. The article explains the certificate's role in UEFI Secure Boot and notes that Windows on this hardware would still require drivers, an installer, and broader platform support. NVIDIA has announced October Windows PCs using the same GB10-class silicon under the RTX Spark name. ### Source excerpt NVIDIA used IFA in Berlin to put Windows at the center of its local AI story. The company's September 3 post covers a new Windows Agent framework for agents that run in the background under OS control, one-click Windows setups for OpenClaw and Nous Research's Hermes Agent, a beta of an inference router called NVIDIA The post DGX Spark Windows Clue: This Week's Firmware Adds a Windows Boot Certificate as NVIDIA Details October RTX Spark PCs appeared first on StorageReview.com. ## AMD Reveals Threadripper Halo Station: 96 Cores and up to 576GB of HBM3E Aimed Straight at DGX Station DevFeed: [AMD Reveals Threadripper Halo Station: 96 Cores and up to 576GB of HBM3E Aimed Straight at DGX Station]() Original publisher: [Read original article]() Author: Brian Beeler Published: 2026-09-04T16:08:49Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [DGX Station](), [GPU](), [Nvidia](), [cpu](), [AI Models](), [Inference](), [Arm](), [toolchain]() Tags: [2026](), [accelerators](), [ai](), [ai-models](), [arm](), [consumer](), [cpu](), [dgx-station](), [gpu](), [toolchain](), [workstation]() ### AI overview AMD revealed the Threadripper Halo Station, a liquid-cooled workstation combining a 96-core Threadripper PRO CPU with two Instinct MI350P accelerators. AMD positions it as an answer to NVIDIA's GB300-based DGX Station, with up to four accelerators, 576GB of HBM3E, 2TB of DDR5 memory, and support for AI models exceeding one trillion parameters. The article contrasts AMD's expandable, discrete design with NVIDIA's unified-memory architecture. ### Source excerpt AMD used its IFA 2026 opening keynote to reveal the Threadripper Halo Station, a liquid-cooled workstation pairing a 96-core Threadripper PRO with a pair of Instinct MI350P accelerators, and the pitch could not be more direct: this is AMD's answer to NVIDIA's GB300 DGX Station. "This is the most powerful workstation in the world," said The post AMD Reveals Threadripper Halo Station: 96 Cores and up to 576GB of HBM3E Aimed Straight at DGX Station appeared first on StorageReview.com. ## Dell Pro 14 Premium Review: A 2.55-Pound Magnesium Flagship With Tandem OLED DevFeed: [Dell Pro 14 Premium Review: A 2.55-Pound Magnesium Flagship With Tandem OLED]() Original publisher: [Read original article]() Author: Dylan Bryan Published: 2026-09-02T00:35:43Z Content type: article Language: en Sources: [StorageReview.com]() Topics: [dell](), [Security](), [cpu](), [intel](), [GPU](), [On-device AI]() Tags: [consumer](), [dell](), [display](), [enterprise](), [gpu](), [intel](), [memory](), [mobile](), [on-device-ai](), [review](), [security](), [storage](), [workstation]() ### AI overview This review examines the Dell Pro 14 Premium, a lightweight 14-inch commercial notebook designed for highly mobile professionals. It emphasizes portability, recycled magnesium construction, enterprise security and manageability, replaceable battery serviceability, Tandem OLED display quality, and on-device AI capabilities. Its integrated graphics and processor options are suited to office productivity and collaboration rather than GPU rendering or CAD workloads. ### Source excerpt The Dell Pro 14 Premium is what Dell calls its ultimate commercial notebook: a 14-inch, 2.54-pound magnesium flagship aimed at highly mobile professionals rather than workstation users. It sits above the Dell Pro and Dell Pro Plus lines, and the pitch is craftsmanship and portability with enterprise security and manageability underneath, not raw compute. The The post Dell Pro 14 Premium Review: A 2.55-Pound Magnesium Flagship With Tandem OLED appeared first on StorageReview.com. ## Small Models Have Arrived DevFeed: [Small Models Have Arrived]() Original publisher: [Read original article]() Author: Calvin French-Owen Published: 2026-08-26T12:00:00Z Content type: opinion Language: en Sources: [Calvin French-Owen]() Topics: [Artificial Intelligence](), [Inference](), [Users]() Tags: [ai](), [business](), [consumer](), [cost](), [fast](), [models]() ### AI overview The article argues that fast, inexpensive, capable small models have improved enough to make consumer AI applications more viable. It also suggests these models can handle much of the responsive, incremental work that businesses perform, while reducing inference costs. ### Source excerpt The fast/cheap/good-enough models have quietly gotten good. That unlocks consumer AI, and most of the "token spewer" work that actually runs a business. ## Where Kafka Fits in Modern Systems DevFeed: [Where Kafka Fits in Modern Systems]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-08-25T12:02:09Z Content type: tutorial Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [Kafka](), [Event-Streaming](), [systems](), [distributed-systems](), [data]() Tags: [architecture](), [consumer](), [distributed-systems](), [durability](), [event-streaming](), [kafka](), [parallelism](), [partition](), [pipelines](), [real-time](), [systems]() ### AI overview This article explains when Apache Kafka is appropriate for distributed systems. It presents Kafka as a durable event log whose replayability, consumer fan-out, and partition-based parallelism support real-time data pipelines and shared event-driven architectures, while warning that Kafka can add unnecessary operational complexity for simpler asynchronous workflows. ### Source excerpt When should you actually use Kafka? And when is it just unnecessary complexity? ## Why Asia Is Ahead on Stablecoins, According to Reap's Daren Guo DevFeed: [Why Asia Is Ahead on Stablecoins, According to Reap's Daren Guo]() Original publisher: [Read original article]() Author: Amira Valliani Published: 2026-08-12T12:57:00Z Content type: article Language: en Sources: [Solana News Feed]() Topics: [Solana](), [systems]() Tags: [2025](), [b2b](), [blockchain](), [blockchain-technology](), [consumer](), [cross-border](), [cross-border-payments](), [crypto-news](), [cryptocurrency](), [defi](), [finance](), [global](), [infrastructure](), [nfts](), [payments](), [podcasts](), [remittances](), [report](), [solana](), [solana-ecosystem](), [southeast-asia](), [speed](), [stablecoins](), [volume](), [web3]() ### AI overview Reap co-founder Daren Guo explains why Asia's cross-border payment infrastructure is well suited to stablecoins and describes Reap's stablecoin card business, including its primary treasury-management move to Solana in 2025. ### Source excerpt Reap moves roughly $6B a year in stablecoin card volume after building cross-border payments infrastructure from Asia. ## Xiaomi Smart Storage NAS Revealed DevFeed: [Xiaomi Smart Storage NAS Revealed]() Original publisher: [Read original article]() Author: Rob Andrews Published: 2026-08-03T16:00:11Z Content type: article Language: en Sources: [NAS Compares]() Topics: [Hardware](), [Specifications](), [QNAP](), [Synology]() Tags: [2-bay-nas](), [capacity](), [china](), [consumer](), [crowdfunding](), [hardware](), [home-nas](), [hyperos](), [launch](), [leak](), [nas](), [nas-2026](), [nas-news](), [network-attached-storage-nas](), [news](), [news-2026](), [private-cloud](), [product](), [qnap](), [qnap-2026](), [qnap-alternative](), [qnap-news](), [realtek-rtd1619b](), [specifications](), [storage](), [synology](), [synology-2026](), [synology-alternative](), [synology-news](), [uncategorised](), [xiaomi](), [xiaomi-nas](), [xiaomi-smart-storage](), [xiaomi-youpin]() ### AI overview Xiaomi Smart Storage is a two-bay consumer NAS launched in China through Xiaomi Youpin crowdfunding. The article describes its family-focused storage use cases, bundled-drive pricing, and leaked engineering-sample hardware specifications, while distinguishing official details from unconfirmed specifications. ### Source excerpt Xiaomi has now officially moved into the home NAS market with Xiaomi Smart Storage, a two-bay consumer storage box that is being launched in China through Xiaomi Youpin crowdfunding. The product is being positioned less like a traditional enthusiast NAS and more like a family storage appliance for phone backup, photo management, files, and home [...] ## Ten Ways Consumer and Prosumer NAS Products Are Changing DevFeed: [Ten Ways Consumer and Prosumer NAS Products Are Changing]() Original publisher: [Read original article]() Author: Rob Andrews Published: 2026-07-31T16:00:41Z Content type: article Language: en Sources: [NAS Compares]() Topics: [Network Attached Storage (NAS)](), [Hardware](), [Software]() Tags: [10gbe-paywall](), [1gbe-nas-bottleneck](), [anti-consumer-hardware](), [challenges](), [closed-storage-appliances](), [consumer](), [debian-nas-os](), [design](), [disguised-product-refreshes](), [diy-nas](), [dram-less-ssd-nas](), [dram-less-ssds](), [drive-locking](), [dsm-7-3](), [emmc-boot-drives](), [emmc-boot-storage](), [enterprise-hdd-squeeze](), [fixed-os-drives](), [hard-drive-market](), [hardware](), [hardware-enshittification](), [hdd-compatibility-lists](), [hyperscaler-hdd-allocation](), [legacy-socs](), [lpddr5-nas](), [m-2-bandwidth-throttling](), [minisforum-nas](), [multi-gigabit-networking](), [multi-gigabit-premium](), [nand-density-bottlenecks](), [nas](), [nas-boot-drive-failure](), [network-attached-storage-nas](), [software](), [uncategorised]() ### AI overview The article examines ten ways the consumer and prosumer Network Attached Storage (NAS) market has changed over the past 18 to 24 months, focusing on design priorities, hardware architecture, and software accessibility. It acknowledges component supply constraints and manufacturing priorities as contributing engineering pressures while presenting broader concerns about recent market changes. ### Source excerpt 10 WAYS that NAS is Getting Screwed Over the past 18 to 24 months, the consumer and prosumer Network Attached Storage (NAS) market has undergone a noticeable shift in design priorities, hardware architecture, and software accessibility. While broader industry pressures--ranging from component supply constraints to shifting manufacturing priorities--have introduced genuine engineering challenges, many recent market [...] ## Open rails for agentic commerce at Open Source Summit North America 2026 DevFeed: [Open rails for agentic commerce at Open Source Summit North America 2026]() Original publisher: [Read original article]() Author: Google Open Source (noreply@blogger.com) Published: 2026-06-16T23:16:03Z Content type: opinion Language: en Sources: [Google Open Source Blog]() Topics: [Artificial Intelligence](), [Open Source](), [systems](), [Protocol (disambiguation)]() Tags: [2026](), [agentic](), [agentic-commerce](), [ai](), [ai-agents](), [business](), [capabilities](), [checkout](), [consumer](), [e-commerce](), [features](), [fragmentation](), [integration](), [interoperability](), [open-source](), [open-standards](), [payment](), [platforms](), [shopping](), [standard](), [ucp](), [universal-commerce-protocol](), [workflows]() ### AI overview An article from Open Source Summit North America 2026 explains why agentic commerce may require shared, open rules and integrations. It presents Universal Commerce Protocol (UCP) as an open standard intended to let agents, businesses, consumer surfaces, and payment providers work together across shopping, checkout, fulfillment, and post-purchase flows. ### Source excerpt by Anurag Sinha, Universal Commerce Protocol (UCP) At Open Source Summit North America 2026, I shared why agentic commerce needs open rails. As AI agents become more capable, the shopping journey is shifting from "show me" to "help me." Instead of browsing, comparing, clicking, and checking out step by step, people can increasingly ask an agent to help them decide what to buy and, in some cases, complete the purchase. Industry forecasts suggest agentic shopping could account for roughly 10% to 25% of U.S. e-commerce by 2030 (Bain), which points to a meaningful shift in how digital commerce will work. Watch the full keynote here. Why shared rules matter That shift also exposes a challenge. Commerce is still highly fragmented. Different businesses, payment providers, and platforms operate with their own rules, workflows, and business logic. Every new surface adds more integration work. Every bespoke connection creates more complexity. And that fragmentation makes it harder for AI systems to understand and perform commerce actions consistently across businesses. A shared language lowers that barrier for everyone. A common language for agentic commerce That is the problem Universal Commerce Protocol (UCP) is designed to solve. We launched the Universal Commerce Protocol, or UCP, with industry leaders to establish an open standard for agentic commerce, built to work across the shopping journey. UCP creates a common language for agents and systems to operate together across consumer surfaces, businesses, and payment providers, so the ecosystem does not need a different bespoke integration for every new agent or platform. Just as importantly, UCP is designed for the real world. Every business has its own way of selling. Checkout, fulfillment, loyalty, policy logic, shipping, and post-purchase flows can vary widely between a local shop, a marketplace, and a large retailer. UCP is built to support that reality. A layered architecture for a shared commerce language UCP uses a ## Cloud Topics: the Metastore DevFeed: [Cloud Topics: the Metastore]() Original publisher: [Read original article]() Author: Andrew Wong Published: 2026-06-11T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Kafka](), [Cloud](), [rocksdb](), [data]() Tags: [architecture](), [cloud](), [consumer](), [engineering](), [kafka](), [learn](), [mapping](), [memory](), [object-storage](), [retention](), [rocksdb](), [scale](), [serialization](), [storage]() ### AI overview This article explains Redpanda's metastore for Cloud Topics. The metastore maps Apache Kafka offsets to byte ranges in L1 objects stored in object storage, while tracking metadata such as leader-term boundaries and compaction state. Redpanda built a general-purpose key-value store, inspired by LevelDB and RocksDB and implemented as an LSM tree, to scale metadata independently of memory, local disk, and metadata formats. ### Source excerpt Learn how Redpanda's metastore powers Cloud Topics, from offset lookups and whole cluster restore to cross-region read replicas, and why it's built to be a foundational primitive for the future. ## EU Digital Services Tax: What SaaS Founders Need to Know in 2026 DevFeed: [EU Digital Services Tax: What SaaS Founders Need to Know in 2026]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-04-13T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Software as a service](), [Shared Responsibility Model](), [Binance](), [API](), [Cloud]() Tags: [b2b](), [compliance](), [consumer](), [digital-products](), [eu](), [how-to](), [merchant-of-record](), [saas](), [subscription](), [tax](), [vat]() ### AI overview This guide explains how EU VAT applies to SaaS and other electronically supplied digital services. It covers customer location, B2B reverse charge rules, B2C collection obligations, and the role of a Merchant of Record in handling compliance complexity. ### Source excerpt Understand the EU digital services tax landscape, VAT obligations for digital products, and how to stay compliant when selling SaaS to European customers. ## Event-Driven Architecture with Apache Kafka DevFeed: [Event-Driven Architecture with Apache Kafka]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-03-16T00:00:00Z Content type: tutorial Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [event driven](), [Kafka](), [Apache-Kafka](), [Scalability](), [Resilience](), [streaming-data-processing](), [Latency](), [systems]() Tags: [apache-kafka](), [architecture](), [asynchronous](), [consumer](), [distributed-systems](), [event-driven-architecture](), [event-sourcing](), [kafka](), [microservices](), [partitions](), [real-time](), [resilience](), [scalability](), [schemas](), [stream-processing]() ### AI overview This tutorial explains event-driven architecture and how Apache Kafka supports asynchronous, real-time data processing. It covers producers, consumers, immutable events, event sourcing, scalability, resilience, stream-processing pipelines, and challenges such as ordering, debugging, and eventual consistency. ### Source excerpt Event-driven architecture replaces synchronous point-to-point calls with immutable events on a durable log, so producers and consumers scale and fail independently. Kafka provides that log: topics sharded into ordered append-only partitions, replicated across brokers, with consumer groups sharing partitions to scale read throughput. ## OfferUp scammers are out in force: Here's what you should know DevFeed: [OfferUp scammers are out in force: Here's what you should know]() Original publisher: [Read original article]() Author: Phil Muncaster Published: 2026-02-04T10:00:00Z Content type: article Language: en Sources: [WeLiveSecurity]() Topics: [App](), [account takeover](), [Authentication](), [Mobile]() Tags: [account-takeover](), [app](), [authentication](), [consumer](), [fraud](), [marketplace-app](), [mobile](), [phishing](), [scams]() ### AI overview This article describes common scams targeting OfferUp buyers and sellers, including counterfeit items, payment fraud, account takeover attempts, empty-box listings, and phishing links. It explains warning signs and how scammers exploit off-platform payments or verification codes. ### Source excerpt The mobile marketplace app has a growing number of users, but not all of them are genuine. Watch out for these common scams. ## Redpanda 25.3 delivers near-instant disaster recovery and more DevFeed: [Redpanda 25.3 delivers near-instant disaster recovery and more]() Original publisher: [Read original article]() Author: Matt Schumpert Published: 2025-11-06T00:00:00Z Content type: release Language: en Sources: [Redpanda]() Topics: [Disaster Recovery](), [Resilience](), [Kafka](), [Streaming](), [Availability](), [configuration](), [Cloud](), [API](), [systems]() Tags: [cloud](), [clusters](), [configuration](), [consumer](), [disaster-recovery](), [high-availability](), [integration](), [kafka](), [process](), [production](), [real-time](), [recovery](), [release](), [resilience](), [server](), [stream-processing](), [streaming](), [systems](), [update]() ### AI overview The article previews Redpanda 25.3, focusing on Shadowing, a disaster recovery and business continuity feature for streaming workloads. Shadowing creates a byte-for-byte, offset-preserving hot standby of a production cluster in another region, including topics, configurations, consumer offsets, ACLs, and schemas. It is designed to provide recovery point and recovery time objectives measured in seconds, with setup through configuration or Redpanda Console and replication using the standard Kafka API. ### Source excerpt Check out how the latest Redpanda release is providing the resilience, efficiency, and interoperability that modern enterprises need for their streaming workloads. ## Building a Scalable Job Consumer System in Go DevFeed: [Building a Scalable Job Consumer System in Go]() Original publisher: [Read original article]() Author: ansu jain Published: 2024-10-16T06:48:50Z Content type: tutorial Language: en Sources: [Mindorks - Medium]() Topics: [Go Language](), [Amazon Simple Queue Service (SQS)](), [Concurrency](), [Orchestration]() Tags: [consumer](), [go](), [goroutines](), [message-queue](), [orchestration](), [parallel](), [sqs](), [workers]() ### AI overview This tutorial presents a scalable job consumer system in Go. It describes interfaces for queue consumption, job management, job handling, and configuration, with parallel workers processing messages from queues such as AWS SQS. ### Source excerpt In this article, I will walk through the design of a scalable and extensible job consumer system using Go. This system allows the flexible registration of different jobs and the parallel consumption of messages from a message queue. I'll focus on how this system can be used to handle real-world scenarios like payment processing, order proccesing etc where different jobs consume messages from a queue (e.g., AWS SQS) and execute business logic based on the type of message received. Key Components of the Design IMessageConsumer - A generic interface for interacting with message queues (e.g., SQS). IJobManager - An interface that coordinates jobs and workers, responsible for consuming messages and distributing them to registered jobs. IJobHandler - An interface representing individual jobs. Each job implements specific business logic. IQueueConfig - Provides configurations for the queue (e.g., queue name, region, broker type). IJobConfig - Provides job-level configurations (e.g., number of consumers, timeouts). Core Concepts1. IMessageConsumer This interface abstracts queue operations, exposing methods for dequeuing and deleting messages. It's implemented by specific brokers (such as SQS in this case). Here's a basic structure: type IMessageConsumer interface { Dequeue(noOfMessages int64) ([]Message, error) Delete(messageIdentifier string) error } Dequeue: Fetches messages from the queue. Delete: Deletes a message after it has been successfully processed. 2. IJobManager This is where the system's core orchestration happens. The JobManager manages a pool of registered jobs and starts multiple workers to process messages concurrently. It uses a Consume method to begin processing and a RegisterJob method to register jobs into the system. type IJobManager interface { Consume(ctx context.Context) RegisterJob(job IJobHandler) } RegisterJob: Adds a job to the manager's job list. Consume: Starts the workers that process messages from the queue in parallel for each registered jo ## Smartme Analytics builds complete consumer insights with Tinybird DevFeed: [Smartme Analytics builds complete consumer insights with Tinybird]() Original publisher: [Read original article]() Author: Tinybird Published: 2024-06-25T00:00:00Z Content type: article Language: en Sources: [Tinybird]() Topics: [data](), [real-time](), [Software]() Tags: [analytics](), [consumer](), [customer-stories](), [data](), [real-time](), [software]() ### AI overview Smartme Analytics builds Consumer 360 software using real-time, zero-party data and analytics powered by Tinybird. ### Source excerpt Read how the leading consumer behavior platform builds comprehensive Consumer 360 software with real-time, zero-party data and analytics powered by Tinybird. ## Python Kafka Basics DevFeed: [Python Kafka Basics]() Original publisher: [Read original article]() Author: Fuzzygroup Published: 2020-04-16T00:00:00Z Content type: tutorial Language: en Sources: [Scott Johnson]() Topics: [Kafka](), [Python]() Tags: [bootstrap](), [consumer](), [json](), [kafka](), [lambda](), [process](), [python]() ### AI overview A brief introduction to using Kafka with Python. It explains that a Kafka consumer returns Consumer Records and shows how to access the message payload through the record's .value attribute, with a basic consumer example. ### Source excerpt This is a starting point blog post where different things I learn about using Python Kafka will be documented. Getting the Value out of a Consumer Record The output of a Python Kafka call is a Consumer Record which is a "set of named tuples (blah blah blah)" where what you actually want is the value tuple. This can be gotten with the .value attribute: #!/usr/bin/env python from kafka import KafkaConsumer consumer = KafkaConsumer( 'dimon_tcpdump', group_id='zhg_group', value_deserializer=lambda m: json.loads(m.decode('utf-8')), bootstrap_servers='192.168.100.9:9092' ) for msg in consumer: # this is what you actually want; print msg.value # process msg here See Also Stack Overflow ## Understanding flatMap and merge in RxJava DevFeed: [Understanding flatMap and merge in RxJava]() Original publisher: [Read original article]() Author: David Karnok (noreply@blogger.com) Published: 2016-02-24T16:22:00Z Content type: tutorial Language: en Sources: [Akarnokd - Advanced RxJava]() Topics: [Programming](), [Functional programming]() Tags: [backpressure](), [callback](), [consumer](), [express](), [functional](), [map](), [merge](), [protocol](), [rxjava](), [stream]() ### AI overview This introductory article explains the outer and inner properties of flatMap, including how it transforms values into Observables whose outputs may vary in time, location, and count. It compares flatMap with merge, describing flatMap as an upstream value-to-Observable operation and merge as an operation over an Observable of Observables. It also discusses backpressure, serialization, and their relationship in RxJava 1.x. ### Source excerpt Introduction In this blog post, I begin to explain the outer and inner properties of the most used, misunderstood and at the same time, one of the most complex operator there is: flatMap. FlatMap is most useful because it let's you replace simple values with something that can change the output in terms of time, location and value count. FlatMap is misunderstood because it is introduced late, not enough time is spent demonstrating it and often surrounded with functional programming technoblabble. Finally, it's complex because it has to coordinate backpressure of a single consumer and request from multiple sources, and we usually don't know which of them will respond with actual items. Maybe all of them. FlatMap has a companion operator: merge. Merge lets you flatten a sequence of Observables into a single stream of values while ensuring the contract of the Observer, namely, the requirement of non-concurrent invocation of the onXXX methods and the conformance to the onNext* (onError|onCompleted)? protocol. This is necessary because although the individual Observables you merge do conform the same protocol individually, they get mixed in time, location and numbers when you listen to them all at once. Of course, flatMap has to do the same so why are there two operators? The answer is convenience and usage pattern. FlatMap is an in-sequence operator that reacts to values from the upstream by generating an Observable, through a callback function, that is internally subscribed to, coordinated and serialized in respect to any previous or subsequent Observables generated through the same callback function. Merge, on the other hand works on a two-dimensional sequence: an Observable of Observables. There is no function involved here but the operator has to subscribe all of those inner Observables emitted by the outer Observable. The fun thing is, you can express them with the other: Func1> f = ... source.flatMap(f) == Observable.merge(source.map(f)) Observabl ## Architecture behind our new Search and Explore experience DevFeed: [Architecture behind our new Search and Explore experience]() Original publisher: [Read original article]() Published: 2012-12-04T00:00:00Z Content type: article Language: en Sources: [SoundCloud Backstage Blog]() Topics: [App](), [real-time](), [User Experience](), [Network](), [Replication](), [Database]() Tags: [apache](), [app](), [architecture](), [consumer](), [data](), [database](), [experiment](), [explore](), [feature](), [features](), [infrastructure](), [models](), [network](), [new-feature](), [recovery](), [replication](), [scale](), [search](), [search-and-discovery](), [speed](), [user-experience]() ### AI overview SoundCloud describes the architecture behind its new Search and Explore experience. The article explains that the earlier Apache Solr search implementation became difficult to keep real-time, re-index, recover, scale, and modify as the product and data grew. The supplied text introduces an overhaul intended to improve relevance, scalability, experimentation, and feature delivery, but does not include the details of the new architecture. ### Source excerpt Search is front-and-center in the new SoundCloud, key to the consumer experience. We've made the search box one of the first things you see...