标签：#Data

Every data strategy document written this decade contains some version of the same sentence: we need a single place to access all our data. The sentence is right. The trouble starts on the next page, because there are two fundamentally different ways to build that single place, and the industry has spent years arguing about them as if they were rivals. Road one is consolidation: bring the data together. Land everything in one governed store, in this era an open lakehouse, Apache Iceberg tables on object storage, and point every consumer at it. Road two is federation: leave the data where it lives and bring the access together instead. A query engine that speaks to your databases, warehouses, lakes, and applications in place, presenting one surface over many sources, with no copies made. I work at Dremio, a company whose platform is built on the conviction that this is a false choice, that the right architecture uses both roads with judgment, and I will declare that bias now and then earn it with an honest treatment. Because the truth practitioners live is messier than either camp's marketing: federation without a lakehouse hits performance and scale ceilings, a lakehouse without federation spends years and fortunes migrating the long tail, and the teams that win treat the two as phases and partners rather than competitors. So this article is the full playbook. What federation and the lakehouse each actually are, mechanically. The honest strengths and limits of each, including the failure modes their advocates gloss over. A concrete decision framework for when each one carries a workload. The lifecycle pattern that connects them, federate first, promote deliberately. And the unified architectures, mine included, that put both behind one governed door, which matters more than ever now that the consumers walking through that door increasingly are AI agents. Why Unify at All: The Cost of the Status Quo Before the two roads, the destination deserves a paragraph, because

series: Prediksi Survival Klub Debutan Kenapa Project Ini? Setiap musim, klub yang promosi ke liga top (Premier League, La Liga, dst.) menghadapi risiko besar: sekitar 2 dari 3 klub yang naik biasanya kembali terdegradasi di musim pertama mereka. Saya penasaran — bisakah performa di beberapa laga awal musim memberi sinyal dini soal peluang klub tersebut bertahan? Ini jadi project portofolio pertama saya sebagai data scientist yang baru mulai (0-1 tahun pengalaman). Saya sengaja pilih topik yang saya suka (sepak bola) supaya prosesnya tetap enjoyable, bukan cuma "tutorial project" generik. Rencana Project Pertanyaan utama: Berdasarkan performa 8 laga pertama musim debut, seberapa besar peluang klub promosi bertahan hingga musim berikutnya (tidak degradasi)? Data yang dipakai: football-data.co.uk — data hasil pertandingan tiap musim sejak 1993/1994 Wikipedia (halaman musim liga) — daftar klub promosi & klasemen akhir musim Tech stack: pandas , requests untuk data collection scikit-learn untuk modeling (mulai dari Logistic Regression sebagai baseline) imbalanced-learn untuk handle class imbalance Streamlit + Plotly untuk dashboard interaktif Deploy ke Streamlit Community Cloud Timeline (Build in Public) Saya bikin timeline ini publik supaya ada tekanan yang sehat untuk benar-benar menyelesaikannya, bukan cuma jadi ide yang menguap: Checkpoint Target Tanggal Yang Harus Selesai Part 1 (post ini) 11 Juli 2026 Kickoff, rencana, environment siap Part 2 15 Juli 2026 Dataset jadi, push ke GitHub Part 3 17 Juli 2026 EDA selesai, insight awal Part 4 24 Juli 2026 Model final dipilih + evaluasi Part 5 31 Juli 2026 Dashboard live di Streamlit Cloud Part 6 (final) 8 Agustus 2026 Project selesai, recap lengkap Tantangan yang Sudah Saya Antisipasi Data leakage — fitur harus dihitung dari laga awal musim saja, bukan seluruh musim, biar model beneran memprediksi bukan "menyontek" hasil akhir Dataset kecil — kemungkinan hanya ~60-100 sampel klub, jadi saya mulai dari model sederhana (Lo

By the team at MailTester Ninja — a real-time email verification API that stores nothing. Everyone says "IPv6 is here." For the web, mostly true. For email , it is a mirage. We resolved the MX records of 50,000 of the most-linked domains and checked whether any of their mail servers publish an AAAA record, meaning they can actually receive over IPv6. No sending, no personal data, just DNS. 55.2% of mail-enabled domains have at least one IPv6-capable MX. That sounds healthy. It is not, because two companies carry almost the whole number: Email provider IPv6 MX Other / self-hosted ██░░░░░░░░ 18.4% Google Workspace / Gmail ██████████ 100% Microsoft 365 / Outlook █████████░ 91.3% Proofpoint ░░░░░░░░░░ 0.6% Mimecast ░░░░░░░░░░ 0% Tencent QQ ░░░░░░░░░░ 4.2% Namecheap ░░░░░░░░░░ 0.2% Cisco IronPort ░░░░░░░░░░ 4.5% Zoho ░░░░░░░░░░ 0% Barracuda ░░░░░░░░░░ 0% Google ( 100% ) and Microsoft ( 91.3% ) run IPv6 on nearly every inbox. Remove those two, the providers that already anchor most of the world's mail, and IPv6 email adoption falls from 55.2% to 12.9% . The enterprise security gateways that gate corporate mail, such as Proofpoint, Mimecast and Barracuda, are effectively not on IPv6 at all. Why it matters for deliverability. IPv6-only sending is a dead end. It reaches Gmail and Outlook and little else. Dual-stack is not optional. IPv4 is still the backbone of email, and that is where blocklists, FCrDNS and IP reputation are mature. The takeaway: IPv6 email is not adopted. Google and Microsoft adopted it for you. Plan your sending for an IPv4 world with two big IPv6 exceptions. Check any domain yourself — our free deliverability analyzer shows a domain's MX / SPF / DMARC in one click (no signup, nothing stored). Need to confirm whether a specific mailbox actually exists and is deliverable? That is exactly what MailTester Ninja's email verifier does in real time — and we store no data. Source: MailTester Ninja's open Email Infrastructure Index — a live DNS scan of 50,000 of

Adapted from an appendix of my MS thesis. Markov Chain Monte Carlo Almost as soon as computers were invented, they were used for simulation. Markov chain Monte Carlo (MCMC) was invested as Los Alamos, Metropolis et al (1953) simulated a liquid in equilibrium with its gas phase. Their tour de force was the realization that they did not need to simulate the exact dynamics, they only needed to simulate some Markov chain with the same equilibrium distribution. The Metropolis algorithm was widely used by chemists and physicists, but was not widely known among statisticians until after 1990. Hastings (1970) generalized the Metropolis algorithm, and simulations following his scheme are said to use the Metropolis-Hastings (MH) algorithm [1]. A special case of the MH algorithm was introduced by Geman et al (1984) discussing optimization to find the posterior mode rather than simulation. Algorithms following their scheme are said to use the Gibbs sampler. It took some time for the spatial statistics community to understand that the Gibbs sampler simulated the posterior distribution, thus enabling full Bayesian inference of all kinds. Gelfand et al (1990) made the wider Bayesian community aware of the Gibbs sampler, and then it was rapidly realized that most Bayesian inference could be done using MCMC, whereas very little could be done without MCMC. Green (1995) generalized the MH algorithm as much as it could be generalized [1]. Theoretical Foundations A sequence X 1 ​ , X 2 ​ , … of random elements of some set is a Markov chain if the conditional distribution of X n + 1 ​ given X 1 ​ , … , X n ​ depends on X n ​ only. The set in which the X i ​ take values is called the state space of the Markov chain. A Markov chain has stationary transition probabilities if the conditional distribution of X n + 1 ​ given X n ​ does not depend on n . This is the main kind of Markov chain of interest in MCMC. The joint distribution of a Markov chain is determined by the following [1]. The ma

Let's be real: our personal medical history is a mess. It’s a chaotic mix of PDF lab results, grainy scans of prescriptions, and cryptic Electronic Medical Records (EMR) scattered across different hospital portals. If you’ve ever tried to remember exactly when a specific symptom started or how your cholesterol has trended over the last decade, you know the "search" struggle is real. In this guide, we are moving beyond simple folders. We are architecting a Personal Health Knowledge Base using a modern Vector Database and RAG (Retrieval-Augmented Generation) pipeline. We’ll leverage Qdrant for high-performance similarity search, Unstructured.io for complex document parsing, and Sentence-Transformers to turn 10 years of medical jargon into searchable embeddings. By the end of this post, you'll have a system capable of cross-year symptom correlation and instant medical history retrieval. The Architecture: From Pixels to Insights 🏗️ The biggest challenge with medical records isn't storage; it's ingestion . Medical PDFs are notoriously difficult to parse because they often contain nested tables and checkboxes. Our pipeline handles this by isolating the layout before embedding. graph TD A[Raw Medical Data: PDFs, Scans, EMRs] --> B[Unstructured.io: Partitioning & OCR] B --> C[Text Chunking & Cleaning] C --> D[Sentence-Transformers: Vector Embedding] D --> E[(Qdrant Vector DB)] F[User Query: 'Show me my blood sugar trends since 2015'] --> G[FastAPI Interface] G --> H[Query Embedding] H --> I[Vector Search in Qdrant] I --> J[Contextual Results + LLM Synthesis] J --> K[Actionable Health Insight] Prerequisites 🛠️ To follow along, you'll need: Python 3.9+ Unstructured.io : For the heavy lifting of PDF/Image parsing. Qdrant : Our vector engine (run it via Docker: docker run -p 6333:6333 qdrant/qdrant ). Sentence-Transformers : To generate local embeddings without sending sensitive data to the cloud. FastAPI : To wrap it all in a slick API. Step 1: Parsing the Chaos with Unstructu

It's Friday evening. An endpoint that normally answers in 200 milliseconds is suddenly taking eight seconds. You open Grafana. Every graph is green. CPU is calm, memory is fine, the disk isn't full. By every dashboard you have, the database is healthy. It is not healthy. This is the failure mode monitoring is worst at: the server is unmistakably alive , so nothing alerts, while inside the database something is slowly rotting. A table has bloated. An index nobody uses is dragging down every INSERT . A forgotten transaction is sitting open, holding a lock and quietly making everything worse. None of it crashes. It just degrades, a little at a time, until one Friday evening it tips over. The good news is that Postgres will tell you all of this — you just have to ask. The queries below run on bare PostgreSQL (13 or newer; one version note along the way), need no agent and no paid monitoring, and use an extension in exactly one place where it genuinely earns it. Open psql and check your own database as you read. 1. The cheapest signal: dead rows Start here, because it costs nothing and catches the most. Postgres never deletes a row in place. An UPDATE or DELETE leaves behind a dead tuple — an old version of the row — and autovacuum cleans those up later. Until it does (or if it can't keep up), the dead rows sit in the table, taking space and forcing every scan to page past them. The fastest look is pg_stat_user_tables , always available, no extension: SELECT schemaname , relname AS table , n_live_tup , n_dead_tup , round ( n_dead_tup * 100 . 0 / nullif ( n_live_tup + n_dead_tup , 0 ), 1 ) AS dead_ratio , last_autovacuum FROM pg_stat_user_tables WHERE n_dead_tup > 0 ORDER BY n_dead_tup DESC LIMIT 20 ; A dead_ratio above ~20% on a large table is worth investigating. And watch for a table where the ratio is high and last_autovacuum is empty — that means autovacuum has never successfully run on it, which is its own red flag (we'll see why in section 5; the whole story conver

Reddit looks simple and hides two genuinely hard problems. Comments nest arbitrarily deep, and a naive tree structure makes loading a busy thread slow. The front page reorders itself constantly, so ranking cannot just count votes or old posts would never leave. Both problems have well-known answers, and both are good lessons in choosing the right model. The core problem A comment thread is a tree. Each comment can reply to any other, so depth is unbounded. If you store only "this comment's parent id" and then try to load a whole thread, you walk the tree one level at a time, one query per level, which gets slow for deep or wide threads. Loading a popular post with thousands of nested comments should not take thousands of queries. Ranking is the second problem. If the front page sorted by raw vote count, the highest-voted post of all time would sit at the top forever. If it sorted by newest, quality would drown in noise. You need a score that blends how good a post is with how fresh it is, so good new posts can climb and old ones fade even if they were once popular. Key design decisions Store the parent pointer, but do not traverse at read time. The simple model is a parent_id per comment, which is easy to write but expensive to read as a tree. To load a thread cheaply, fetch all comments for the post in one query, then assemble the tree in application memory. One read, in-memory tree building. This works because a single post's comments, while numerous, fit in memory to assemble. Consider a path or closure model for deep trees. For very deep threads, some systems store a materialized path on each comment, an encoded ancestor chain, so you can fetch an entire subtree with a single prefix query and sort by the path to get correct display order. Another option is a closure table that records every ancestor-descendant pair, which makes subtree queries direct at the cost of extra write work. The right choice depends on how deep threads get and how often you read subtrees

TL;DR Retailers process thousands of inventory transactions every second across physical stores, eCommerce platforms, warehouses, suppliers, and fulfillment centers. Yet many inventory systems still rely on scheduled synchronization, causing stock levels to become outdated within minutes. The result is overselling, delayed replenishment, inaccurate inventory visibility, and avoidable stockouts. Apache Kafka enables real-time inventory management by treating every inventory movement as an event that is streamed the moment it occurs. Sales, returns, warehouse transfers, supplier deliveries, and IoT sensor updates are continuously processed to maintain a consistent inventory view across all retail systems. This event-driven approach helps retailers improve inventory accuracy, automate replenishment, detect stockouts before they occur, and respond to changing demand in near real time. In this guide, you'll learn how Apache Kafka powers real-time inventory management, explore a production-ready reference architecture, understand how inventory events are processed across retail systems, and discover implementation best practices for building scalable, resilient inventory streaming applications. Introduction Retail inventory management has evolved far beyond tracking products on warehouse shelves. Today's retailers operate across physical stores, eCommerce platforms, online marketplaces, distribution centers, and supplier networks, where inventory levels change continuously throughout the day. Every sale, return, warehouse transfer, supplier delivery, and inventory adjustment impacts product availability, making accurate inventory visibility essential for delivering a seamless customer experience. However, many retailers still rely on scheduled synchronization between Point-of-Sale (POS) systems, Warehouse Management Systems (WMS), Enterprise Resource Planning (ERP) platforms, and online storefronts. While these systems perform different functions, they all depend on accur

Original Japanese article : Iceberg REST Catalogを直接叩いて、Glue Data CatalogとS3 Tablesの違いを理解する Introduction I'm Aki, an AWS Community Builder ( @jitepengin ). Most of the time, when working with Iceberg tables, we reach for PyIceberg or Spark. I'm no exception, and honestly there were parts of the PyIceberg configuration — rest.sigv4-enabled , rest.signing-name , warehouse — that I understood only vaguely. Iceberg defines a standard called the Iceberg REST Catalog Open API specification , and AWS implements it through two separate endpoints: The AWS Glue Iceberg REST endpoint ( https://glue.<region>.amazonaws.com/iceberg ) The Amazon S3 Tables Iceberg REST endpoint ( https://s3tables.<region>.amazonaws.com/iceberg ) If two implementations follow the same spec, sending the same requests to both and comparing the results should reveal what's actually different between them. In this article, I'll bypass clients like PyIceberg entirely and hit the REST API directly to explore the differences between the two endpoints. To state the conclusion up front: Even though both implement the same Iceberg REST Catalog specification, Glue is designed as an "entry point to multiple catalogs," while S3 Tables is designed as an "entry point to a single table bucket." That difference is visible just by looking at the URL paths. I previously wrote about the relationship between S3 Tables and Glue Data Catalog in another article — worth a read alongside this one: Does Amazon S3 Tables Replace AWS Glue Data Catalog? Understanding Their Relationship What Is the Iceberg REST Catalog? The Iceberg REST Catalog is a specification that standardizes Iceberg catalog operations as an HTTP API. It's published as an OpenAPI definition (YAML), and any catalog that conforms to it can be accessed the same way from clients such as PyIceberg, Spark, and Trino. The key points of the spec are: URL paths follow a pattern like GET /v1/{prefix}/namespaces , where {prefix} is a free-form segment Clients first call

I run a small AI trends site, and three weeks ago I started doing something simple: every day, snapshot the star count of every repo that crosses my GitHub trending scan for AI. No judgment, no curation, just append-only rows in a database. 611 repos and 2,671 data points later (June 19 to July 10), the picture of what's actually growing looks pretty different from what my feeds told me was hot. Here's what the data says. Before publishing this I re-checked every number below against GitHub's live API. Star counts drift by the hour, so treat them as of July 10. The top 10 risers, by raw stars gained Repo Gained Window From → To calesthio/OpenMontage +30,253 21 days 5,899 → 36,152 DeusData/codebase-memory-mcp +20,483 19 days 7,516 → 27,999 mattpocock/skills +19,053 15 days 137,485 → 156,538 obra/superpowers +16,887 20 days 232,908 → 249,795 NousResearch/hermes-agent +14,896 21 days 197,297 → 212,193 Panniantong/Agent-Reach +14,334 14 days 34,780 → 49,114 usestrix/strix +13,243 12 days 26,363 → 39,606 addyosmani/agent-skills +12,685 21 days 63,156 → 75,841 asgeirtj/system_prompts_leaks +11,720 21 days 43,415 → 55,135 msitarzewski/agency-agents +11,055 10 days 118,241 → 129,296 Windows differ because I only hold snapshots for the days a repo appeared in my scan; each row states its own real window. Three things in this data genuinely surprised me. 1. "Skills" are eating agent frameworks Four of the top ten are not agent frameworks. They are collections of packaged expertise that plug into an existing agent: obra/superpowers (still compounding at roughly 840 stars a day on a 250k base), mattpocock/skills, addyosmani/agent-skills, msitarzewski/agency-agents. A year ago this table would have been full of new frameworks. Now the framework layer looks settled and the growth is in what you load INTO the agent. The moat moved from orchestration code to encoded judgment. 2. The sharpest climbs are applications, not infrastructure The steepest sustained climb from a newcomer in

Ingeniería de Datos aplicada a la Biodescodificación: Presentando Bio-Mapping Engine 🧬 ¿Es posible aplicar ingeniería de datos de alta fidelidad a campos de conocimiento no estructurados? La respuesta es un rotundo sí. Hoy quiero presentarles Bio-Mapping Engine , un framework diseñado para resolver un problema clásico de la extracción de información: convertir literatura densa y desorganizada en una base de conocimientos semántica, estructurada y totalmente navegable. El Problema: El caos de la información no estructurada En campos como la Biodescodificación , la información suele residir en libros o archivos PDF donde los conceptos (síntomas, emociones, zonas anatómicas) están entrelazados de forma narrativa. Para un investigador o un desarrollador de herramientas de salud alternativa, extraer relaciones precisas entre un síntoma físico y su conflicto emocional mediante métodos tradicionales es una tarea manual, lenta y extremadamente propensa a errores. La Solución: Bio-Mapping Engine Bio-Mapping Engine no es un simple scraper . Es un motor de segmentación semántica y mapeo topológico. Su propósito es transformar un PDF bruto en un grafo de conocimiento estructurado en formato JSON, permitiendo realizar consultas multidimensionales con precisión quirúrgica. 🚀 Características Principales Segmentación Semántica Avanzada: Implementa un parsing topológico que distingue inteligentemente entre encabezados de síntomas, contenido emocional y el "ruido" estructural (como índices o números de página). Mapeo Relacional Multidimensional: Realiza una extracción de alta fidelidad a través de tres vectores fundamentales: Síntomas Canónicos: Estandarización de la nomenclatura de síntomas y condiciones. Jerarquía Anatómica: Mapeo inteligente que escala desde Sistemas $\rightarrow$ Regiones $\rightarrow$ Órganos. Arquetipos Emocionales: Extracción estructurada de modelos mentales y conflictos (ej. "Causa probable" , "Bloqueo emocional" ). Consultas Multi-Eje (CLI): Una potente inte

When Neon became the official database partner of DEV Community, I was already a user. But the partnership made me look closer at why I chose Neon — and whether those reasons apply to other AI developers. They do. Here's why Neon is the ideal database for AI applications in 2026. The Problem: AI Apps Have Unique Database Needs AI applications have database requirements that traditional web apps don't: High write volume — every AI request generates logs, metrics, and cost data Variable load — traffic spikes when a model goes viral, then drops to zero Schema evolution — you're constantly adding models, routing rules, and analytics tables Dev/prod parity — you need to test routing changes against real production data Edge compatibility — AI APIs need sub-100ms response times globally Traditional PostgreSQL (RDS, Aurora) struggles with all five. Neon was built for them. Feature 1: Database Branching (The Game-Changer) This is Neon's killer feature. It works like git branch but for your entire database: # Create a branch from production neon branches create --parent main --name test-deepseek-v31 # Get a connection string for the branch neon connection-string test-deepseek-v31 # → postgresql://...@ep-test-deepseek...neon.tech/neondb # Run migrations on the branch npx prisma db push --url $BRANCH_URL # Test your new routing algorithm against REAL data # (the branch is a copy-on-write clone of production) # When tests pass, merge neon branches merge test-deepseek-v31 Why This Matters for AI Apps When I added DeepSeek V3.1 to my model pool, I needed to test: Would the new model break existing routing rules? Would the cost calculations be correct? Would the latency meet my SLA? With traditional PostgreSQL, testing against real data meant either: Copying production to a staging DB (hours, $$) Testing with synthetic data (unreliable) With Neon branching, I branched, tested in 30 seconds, and merged. Zero downtime, zero risk. Feature 2: Scale-to-Zero (Cost Optimization) Neon's c

I recently commented on Jonathan Lewis’s blog, Savepoint Funny , where I compared how PostgreSQL handles uniqueness differently: “PostgreSQL resolves uniqueness through heap tuple visibility". This deserves a more detailed explanation. In Oracle, unique indexes store unique entries because the B-tree key is the index key, preventing duplicates. Non-unique indexes add the ROWID to ensure that all entries are physically unique, even when indexed column values are duplicated. In PostgreSQL, all indexes, even unique ones, created explicitly by CREATE UNIQUE INDEX or implicitly to enforce a unique constraint, behave like non-unique indexes by appending the TID (tuple ID, similar to Oracle's ROWID) to the index key. This indicates that the index itself doesn't guarantee physical uniqueness, allowing multiple entries to have identical logical keys but point to different heap tuples. The actual uniqueness verification occurs at the heap level, not within the index entries. Initially, this might seem unusual—a unique index that permits duplicates. However, PostgreSQL requires this because of its MVCC system. MVCC allows duplicate entries to coexist in an index, since they can represent different versions of the same logical row. Still, PostgreSQL must guarantee that no MVCC snapshot views two rows with the same index key. Oracle doesn't face this issue because its MVCC implementation also versions index blocks, allowing a single index version to maintain unique keys. Let’s show that. Page inspect In PostgreSQL, the heap contains the table data, and index entries point to heap tuples. Visibility depends on the heap header, especially the transaction information. Index scans often visit the heap pages to check visibility, except for index-only scans, which use the heap's visibility maps as an optimization. B-tree indexes can store entries for multiple versions of the same logical row, including versions that are no longer visible to current snapshots. To ensure uniqueness, the

Raj Ummadisetty and Ken Kurzweil share Netflix's architectural pivot to CloudStream, a repeatable capture, conversion, and deployment framework. They discuss shifting key-value abstractions from stateless to stateful to move terabytes of bulk data safely. Software architects will learn to exploit data access patterns, use "Pathfinder" prototypes, and maintain a 99% faster rollout. By Rajasekhar Ummadisetty, Ken Kurzweil

Thousands of new fossil-fuel power sources are quietly firing up across the state to power the AI boom, thanks to a regulatory loophole, leaving residents feeling blindsided.

Google shipped AlloyDB AI functions GA with a proxy model architecture that trains a lightweight local model from LLM outputs, then runs queries at database speed without external calls. Smart batching delivers 2,400x throughput improvement. The proxy model reaches 100,000 rows per second in preview, but benchmark numbers apply only to ai.if in internal testing. By Steef-Jan Wiggers

On July 8, 2026 I looked up a barcode that does not exist. Eight zeros. I sent them to Open Food Facts, the largest open nutrition database on the web, and it answered HTTP 200. Green light. Then I read the body: "status":0 , "status_verbose":"no code or invalid code" . A success code wrapped around a total miss. Ten seconds of trusting the status line and I would have written that empty result into a calorie tracker as if it were food. That is the whole post. The list of APIs is the easy part. The hard part is that a keyless food API hands you a clean 200 and a wrong answer, and it does it a slightly different way on almost every endpoint. A free food API here means a public nutrition, ingredient, or recipe endpoint that returns JSON with no API key, no signup, and no card. Not a CSV dump, not a partner form, not a portal from 2012. A real REST call you can paste into a terminal right now. I found eight that clear that bar, plus three worth knowing that quietly lean on a shared key. I re-verified every one with a live curl on July 8, 2026 (real HTTP code, real body, trimmed but never paraphrased). If you build calorie trackers, meal planners, grocery tools, or an AI agent that answers "how much sugar is in this," these are the lookups you reach for. Every one of them can lie to you with a 200. Here is the uncomfortable finding before the list. Keyless nutrition data in 2026 is mostly one project. Open Food Facts and its sibling databases (Pet Food, Products, Beauty, Prices) are six of the eight entries below: five distinct databases on one shared engine, with Open Food Facts itself showing up twice because it fails two different ways. Only two entries, Fruityvice and Wger, are independent, and Wger re-imports its data from Open Food Facts anyway. That concentration is not a weakness of the roundup. It is the point. Because it is one engine, the data-quality traps below are systemic, not one-offs. Learn them once and they repeat across the whole family. Let me be st

The cyberattack targeting a U.S. insurance giant is the largest known breach of driver's license numbers so far in 2026.

Itamar Friedman discusses how architects and engineering leaders can break through the AI productivity ceiling using adaptive multi-agent systems. He shares insights on moving past simple autocomplete to resilient workflows by integrating autonomous testing, intelligent code review, and robust arbitration. Learn how to govern agent communication and build a context-driven SDLC that scales. By Itamar Friedman

Most "Debezium vs managed tool" articles get the question wrong. They frame it as a product bake-off, feature grid included, and declare a winner. But if you've actually run change data capture in production, you know the real decision isn't which tool captures a transaction log better. They mostly read the same logs the same way. The real decision is who operates everything that sits around the capture, and whether that work is a good use of your team's time. That's a build-vs-buy question, not a product question. This post is a framework for answering it for your own situation. First, let's kill an outdated assumption A lot of Debezium criticism floating around is two or three years stale, and if you repeat it in 2026 you'll get corrected fast. So let's set the record straight before we compare anything. Debezium is no longer just “the thing you run with Kafka Connect.” In recent Debezium 3.x releases, the project has become much more flexible than the old tutorials suggest. Today, you have several deployment options: Kafka Connect , the classic setup, which gives you the Kafka ecosystem, distributed fault tolerance, durable schema history, and access to Kafka Connect sink connectors. Debezium Server , a standalone application that streams changes to systems like Amazon Kinesis, Google Cloud Pub/Sub, Apache Pulsar, Redis Streams, or NATS JetStream without requiring Kafka. Debezium Management Platform , which builds on Debezium Server and the Debezium Operator to provide a higher-level way to configure and manage CDC pipelines in Kubernetes-style environments. Embedded usage , where you run Debezium Engine inside your own application. Recent Debezium releases also added framework support such as the Quarkus extension. A few more things are worth knowing so the comparison is fair: Kafka 4.x runs in KRaft mode, and ZooKeeper mode has been removed. “You need to babysit ZooKeeper” is no longer true for a modern Kafka deployment. Debezium's default remains at-least-once