# data-modeling Published articles for data-modeling. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How to build a service marketplace with provider profiles in Webflow Cloud DevFeed: [How to build a service marketplace with provider profiles in Webflow Cloud]() Original publisher: [Read original article]() Author: Ismail Ajagbe Published: 2026-09-12T00:00:00Z Content type: tutorial Language: en Sources: [Webflow Blog]() Topics: [data-architecture](), [Database](), [SQLite](), [Next.js](), [Node.js](), [Authentication](), [Content Management System]() Tags: [data-modeling](), [database](), [guide](), [guides](), [how-to](), [next-js](), [node-js](), [sqlite](), [webflow]() ### AI overview A guide to building the discovery side of a service marketplace on Webflow Cloud. It explains why provider profiles should be modeled as database rows rather than CMS content when they require owner editing, flexible filtering, and approval before publication. ### Source excerpt Learn how to build the discovery half of a service marketplace on Webflow Cloud. ## Data Engineering Weekly #286 DevFeed: [Data Engineering Weekly #286]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-09-07T00:18:06Z Content type: article Language: en Sources: [Data Engineering Weekly]() Topics: [data-engineering](), [AI, ML & Data Engineering](), [agent observability](), [Large Language Model](), [data-modeling](), [Monitoring](), [Scalability](), [semantic-layer](), [parquet]() Tags: [ai](), [ai-agents](), [data-engineering](), [data-modeling](), [llm](), [monitoring](), [newsletter](), [observability](), [parquet](), [scalability](), [semantic-layer](), [weekly]() ### AI overview Data Engineering Weekly #286 is a curated newsletter covering data platform fundamentals, mathematics for machine learning, agentic machine learning at Instacart, Netflix's lifecycle for LLM-as-a-Judge systems, semantic layers and data modeling for AI analytics, and Apache Pinot scalability. ### Source excerpt The Weekly Data Engineering Newsletter ## How we knew COVID was over (and what our models had to unlearn) DevFeed: [How we knew COVID was over (and what our models had to unlearn)]() Original publisher: [Read original article]() Author: Harrison Katz Published: 2026-08-19T17:01:03Z Content type: article Language: en Sources: [The Airbnb Tech Blog - Medium]() Topics: [Data Science](), [data](), [datasets](), [Process]() Tags: [company](), [data](), [data-modeling](), [data-science](), [machine-learning](), [models](), [process](), [technology]() ### AI overview An Airbnb forecasting data science team explains how it responds when production forecasts drift, distinguishing between refitting a model with newer data, respecifying its structure, and holding it unchanged. The article emphasizes diagnosing the source of persistent bias and managing the risks of model updates that influence company decisions. ### Source excerpt When we retrain, when we rebuild, and when we leave a model alone. By: Harrison Katz A forecast that carries weight The Forecasting Data Science team at Airbnb produces many of the forecasts the rest of the company plans around: demand, bookings, cancellations, and a range of finer cuts by market and segment, refreshed continuously across thousands of markets. The targets differ, and the models differ, but they have one thing in common: Other teams build on top of them. This means a forecast that is casually wrong is not a clean miss, as it might be in an academic setting. That's because a small bias does not stay small once a lot of decisions are riding on it. So when one of those forecasts starts to drift, what to do about it is not really a methods question. It is a risk question, and an easy one to get wrong, which we have from time to time. One of these forecasts had been missing, compared to what actually happened after the forecast was released, in the same direction for a couple of quarters. This bias persisted after several routine refreshes. The usual solution would be to fully retrain the model: pull in the recent data, refit the model again, and ship. But we wanted to understand the source of the bias, rather than simply hoping an update would eliminate it. If you're interested in other posts on this topic, you can learn more about how COVID impacted Airbnb's financial models or how we dealt with disruption to our models during the pandemic. This post is about the discipline that came out of both: how we now decide whether a struggling forecast needs new data, a new model, or no changes at all. One word, three decisions The easy mistake is treating the choice to "retrain" a model as a single action. It is three separate actions -- refitting, respecifying, or holding -- and none of them is particularly similar to the others. Refitting is the cheaper option. Same model, same structure, same features, updated with newer data. This is what most people mean by ## A framework for sequential decisions in daily life and beyond DevFeed: [A framework for sequential decisions in daily life and beyond]() Original publisher: [Read original article]() Author: Nisarg Suthar Published: 2026-08-04T07:16:01Z Content type: article Language: en Sources: [Data Science at Microsoft]() Topics: [decision-making]() Tags: [data-modeling](), [data-science](), [decision-making](), [model](), [operations-research](), [sequential-decision](), [structured](), [supply-chain]() ### AI overview The article introduces a Universal Modeling Framework proposed by Warren Powell for reasoning about sequential decisions under uncertainty and incomplete information. It explains how mathematical modeling can clarify decision problems and support better choices across areas such as finance, healthcare, data-center infrastructure, and supply chains. ### Source excerpt Photo by Sophia Kunkel on Unsplash Decision-making occupies a significant portion of our mental uptime. Whether we work in finance, energy, transportation, healthcare, e-commerce, foreign policy, or global supply chains, we are constantly required to make choices in the presence of uncertainty and incomplete information. As new information arrives, decisions must be revised, refined, and sometimes completely reconsidered. Every decision carries consequences -- some rewarding, others costly. The ability to consistently make better choices is often a defining factor behind successful outcomes. Yet effective decision-making remains notoriously difficult. Describing a problem as "mind-bogglingly complex" is really just a by-product of a failure to think about the problem in a structured way.-- Dr. Warren Powell This article introduces a Universal Modeling Framework for reasoning about sequential decision problems proposed by Dr. Warren Powell, an operations researcher at Princeton University. The same framework can be applied across a remarkably diverse set of problems: buying or selling financial assets, evaluating a new user experience, selecting candidate drugs for clinical trials, investing in data-center infrastructure, or managing large-scale supply chains. Here we shall undertake an approach that focusses on identifying the core elements of a decision-making process. Central to our approach is the creation of a simple mathematical model that eliminates the ambiguity of describing problems in language. Modeling is an art, guided by a mathematical framework, and results in a well-defined problem that we can put on a computer to solve. Even when the ultimate goal is not to automate the decision, the act of modeling itself often leads to deeper understanding and better choices. A dynamic model for sequential ecisions A sequential decision process can be represented as follows: Where: Sₜ is the state variable capturing our state of knowledge at time t. For example, inve ## Modeling Device Capabilities for Analytics DevFeed: [Modeling Device Capabilities for Analytics]() Original publisher: [Read original article]() Author: Netflix Technology Blog Published: 2026-07-31T16:01:02Z Content type: article Language: en Sources: [Netflix](), [Netflix TechBlog - Medium]() Topics: [Netflix](), [data](), [data-processing](), [datasets](), [Hardware](), [cpu](), [Streaming](), [Software]() Tags: [analytics](), [cloud-gaming](), [cpu](), [data](), [data-engineering](), [data-modeling](), [datasets](), [devices](), [hardware](), [netflix](), [streaming]() ### AI overview Netflix describes a device-capability data model for analytics across a diverse ecosystem of streaming devices. Cumulative and histogram tables capture device capabilities, active device counts, software versions, and feature support, helping teams measure feature reach and make more granular enablement decisions. ### Source excerpt by Aarti Laddha, Richard Diaz-Cool, Rishika Idnani, Venkatesh Selveraj Netflix supports a vast and evolving set of features and content types, ranging from 4K streaming and immersive audio to live streaming and cloud gaming, across a diverse ecosystem of devices. However, not all devices are created equal. Hardware limitations such as available RAM, CPU cores, display capabilities, or platform support mean that some features cannot be supported on certain device models. To ensure the best possible user experience, we rely on a deep understanding of device capabilities. We have invested in building a comprehensive device capability data model and integrating feature flags from internal systems, paving the way for smarter, more granular feature management across our global device landscape. This approach helps us identify bottlenecks in feature penetration and accelerates the pace of innovation. We have designed our data storage and modeling strategies to efficiently support analytics at scale. We use a cumulative table to process information about the device's capabilities. This table is structured to efficiently capture the latest state of each device and its associated capabilities (like Screen resolutions, Video Profiles Supported, Surround Sound, RAM size etc) making it ideal for analytics and reporting use cases. { "Screen Height": ["720"], "Screen Width": ["1280"], "Video Profiles": [ "playready", "hevc", ], } For aggregate analytics, we leverage a histogram table that captures active device counts over the past 28 days, broken down by device model and software version. This table also records the number of devices supporting specific capabilities, enabling detailed distribution analysis. One use case for this histogram data is to analyze the distribution of external display capabilities attached to streaming sticks. For example, the histogram below shows that out of total X number of devices, all supported the HD profile (playready), while only 20% devices sup ## Data Modeling's Relevance in Modern Data Engineering DevFeed: [Data Modeling's Relevance in Modern Data Engineering]() Original publisher: [Read original article]() Author: Daniel Beach Published: 2026-07-27T12:20:21Z Content type: opinion Language: en Sources: [Data Engineering Central]() Topics: [data-modeling](), [data](), [Artificial Intelligence]() Tags: [ai](), [article](), [data-modeling](), [opinions]() ### AI overview This opinion article examines whether data modeling remains relevant in modern data work. It presents differing views, notes the longstanding role of relational modeling, and questions why the topic has recently been treated as potentially obsolete. ### Source excerpt was it never alive? ## Postgres AI Workshop DevFeed: [Postgres AI Workshop]() Original publisher: [Read original article]() Published: 2026-07-25T16:00:00Z Content type: article Language: en Sources: [Bruce Momjian: Postgres Blog]() Topics: [Artificial Intelligence](), [Claude Code](), [Command-line interface](), [Claude](), [Testing](), [benchmarking](), [Amazon Web Services](), [browser]() Tags: [ai](), [aws](), [benchmarking](), [browser](), [business](), [claude](), [claude-code](), [code](), [command-line](), [conference](), [data-modeling](), [presentation](), [release-notes](), [testing]() ### AI overview A firsthand account of an AWS-organized AI workshop for Postgres committers and core team members. It covers setting up Claude and Claude Code from the command line, using AI for testing, benchmarking, bug reports, email-thread analysis, patch review, and workflow automation. The author found Claude Code more useful than Claude in a web browser for identifying errors and inconsistencies in content. ### Source excerpt AWS was kind enough to organize an AI workshop this week in Pittsburgh for their employees, and the Postgres committers and core team. The first day covered the basics of setting up Claude and specifically how to use it from the command line to analyze files and automate workflows. We also discussed how AI-automated workflows can do testing, benchmarking, and create proof-of-concept patches to explore ideas that were previously too complex or time consuming to consider. On the second day, we did hands-on setup of Claude Code, and discussed how we can modify and add things to our source tree to improve AI usage. We also discussed how to improve our workflow in analyzing email threads and reviewing patches. On the final day, we picked various bug reports and used AI to improve email thread analysis and patch review. I found the event very relaxing, like a tech retreat, and hope we can do something like it again. During the event, I learned that Claude Code from the command line is much more powerful than using Claude via a web browser. It found many grammatic and typographic errors in my blogs and slides, we well as inconsistencies across my website. While I doubt anyone will notice the improvements, the process did improve the quality of my content. ## What Matters Most for NoSQL Migrations DevFeed: [What Matters Most for NoSQL Migrations]() Original publisher: [Read original article]() Author: Cynthia Dunlop Published: 2026-07-21T13:44:46Z Content type: tutorial Language: en Sources: [ScyllaDB]() Topics: [NoSQL](), [Database Migration](), [migration](), [data-architecture](), [data](), [App]() Tags: [apps](), [community](), [complexity](), [data](), [data-modeling](), [database](), [database-migration](), [databases](), [how-to](), [migration](), [offline](), [performance](), [scale](), [validation](), [what-matters]() ### AI overview This article presents practical guidance for planning, executing, and de-risking NoSQL database migrations. It compares online and offline approaches and emphasizes schema migration, data movement, and validation, with attention to performance, scale, complexity, and disruption. ### Source excerpt How to prioritize the things that matter most for planning, executing and de-risking your NoSQL database migration ## ClickHouse Schema Design and Data Modeling DevFeed: [ClickHouse Schema Design and Data Modeling]() Original publisher: [Read original article]() Author: Agus Syafaat Published: 2026-07-17T12:48:53Z Content type: article Language: en Sources: [SeveralNines]() Topics: [clickhouse](), [data-modeling](), [distributed-systems](), [Database]() Tags: [clickhouse](), [cta](), [data-modeling](), [database-general](), [distributed](), [distribution](), [monitoring](), [production](), [schema](), [schema-design]() ### AI overview This article explains how to design and maintain ClickHouse schemas in production. It covers distributed and local tables, shards and replicas, shard-key selection, operational concerns such as slow queries and oversized partitions, monitoring, and schema evolution. ### Source excerpt Sometimes, we see ClickHouse queries that should normally complete in milliseconds take several seconds to finish or worse, time out entirely. When that happens, there is a good chance that the schema is the real culprit. The problem is often not the query itself, nor is it a hardware bottleneck. Instead, it can stem from [...] The post ClickHouse Schema Design and Data Modeling appeared first on Severalnines. ## Announcing leetdata.ai -- A Practice Ground for Data Engineers DevFeed: [Announcing leetdata.ai -- A Practice Ground for Data Engineers]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-07-02T08:27:41Z Content type: release Language: en Sources: [Data Engineering Weekly]() Topics: [AI, ML & Data Engineering](), [data-modeling](), [Artificial Intelligence](), [Orchestration]() Tags: [ai](), [code](), [data-engineering](), [data-modeling]() ### AI overview Data Engineering Weekly announces leetdata.ai, a practice platform for data engineers featuring real problems, data modeling challenges, mock design interviews, and a leaderboard. The platform is open to everyone and aims to address the lack of dedicated data-engineering interview practice. ### Source excerpt Software engineers got a gym. Data engineers got production. It's time to fix that. ## ScyllaDB Customer Experience Spotlight: Faisal Saeed DevFeed: [ScyllaDB Customer Experience Spotlight: Faisal Saeed]() Original publisher: [Read original article]() Author: Cynthia Dunlop Published: 2026-06-11T12:01:56Z Content type: article Language: en Sources: [ScyllaDB]() Topics: [ScyllaDB Cloud](), [data-architecture](), [migration](), [Automation](), [Database](), [on-prem](), [MariaDB]() Tags: [app](), [automation](), [blog](), [cloud](), [community](), [customer](), [cx-profiles](), [data-modeling](), [database](), [demo](), [india](), [migration](), [on-prem](), [scale](), [scylladb](), [scylladb-cloud](), [testing](), [tool]() ### AI overview Faisal Saeed, Principal Customer Engineer at ScyllaDB, describes his work supporting existing customers, evaluating deployments, data modeling, and migrations to ScyllaDB Enterprise or ScyllaDB Cloud. He also discusses the ScyllaDB Automation Framework, which automates cluster operations, workload execution, and stress testing, and highlights ScyllaDB Cloud and a large-scale customer use case in India. ### Source excerpt Meet Faisal Saeed, Principal Customer Engineer on the Customer Experience team here at ScyllaDB. ## Scaling beyond one: How Airbnb evolved its data architecture for a multi-product world DevFeed: [Scaling beyond one: How Airbnb evolved its data architecture for a multi-product world]() Original publisher: [Read original article]() Author: Patrick Lam Published: 2026-06-09T17:01:02Z Content type: article Language: en Sources: [The Airbnb Tech Blog - Medium]() Topics: [data-architecture](), [Data Infrastructure](), [data-modeling](), [data]() Tags: [analytics](), [architecture](), [data](), [data-architecture](), [data-engineering](), [data-infrastructure](), [data-modeling](), [engineering](), [offline](), [post](), [technology]() ### AI overview Airbnb's data and analytics engineering teams evolved a decade-old offline data warehouse to support Homes, Experiences, and Services. The article examines the trade-offs between separate product-specific data models and a unified monolithic model while describing the need for a consistent, flexible, and scalable data foundation. ### Source excerpt How Airbnb's data engineers and analytics engineers built a consistent and flexible data modeling framework to support the expansion into Homes, Experiences, and Services. By: Patrick Lam, Namrata Lamba, Jamie Stober With the May 2025 Summer Release, Airbnb redesigned its app, relaunched Experiences, and debuted Services, pushing us beyond our traditional Homes focus. For the data teams, this meant rapidly evolving a decade-old infrastructure to integrate two brand-new product pillars. Our data engineers and analytics engineers rose to the challenge by building a consistent and flexible framework to serve as a robust and scalable data foundation for the next decade of growth. But getting there wasn't straightforward. This fundamental shift surfaced a critical question for our data organization: How do you evolve your offline data architecture to support new product lines without introducing disorder in vital analytics services? We knew the approach we took would have long-lasting implications. A fragmented strategy risked creating data silos, inconsistent analytics, and a tangled web of technical debt that would likely slow down future innovation. In this post, we'll take you behind the scenes to share key decisions that we made, the framework that emerged, and the lessons that helped reshape our offline data warehouse for the future. Note that we focus specifically on our offline data warehouse (the analytics-oriented data infrastructure owned by our data engineers and analytics engineers) rather than the online data systems that serve the app directly, as the two domains have fundamentally different requirements, constraints, and design philosophies that warrant separate treatment. The core dilemma: separate vs. monolithic The first and most critical question was how to structure offline data for the new, three-product world, with Homes, a refreshed Experiences product, and the new Services offering. This involved a trade-off between two main approaches: Separate ## When history fails you, borrow from geography DevFeed: [When history fails you, borrow from geography]() Original publisher: [Read original article]() Author: Harrison Katz Published: 2026-06-02T17:01:04Z Content type: article Language: en Sources: [The Airbnb Tech Blog - Medium]() Topics: [data](), [datasets]() Tags: [ai](), [building](), [data](), [data-modeling](), [data-science](), [machine-learning](), [post](), [technology](), [travel-industry]() ### AI overview Airbnb describes a forecasting approach for the uneven post-COVID travel recovery, using sequential signals from other geographies and propagating prior information to produce corridor-level forecasts when local post-shock data was scarce. ### Source excerpt How Airbnb used sequential geographic recovery signals and prior propagation to generate reliable corridor-level forecasts when local data was scarce. By: Harrison Katz The problem with unprecedented shocks Almost every forecasting system is built on the same implicit assumption: the future will resemble the past. You train on historical data, you validate on holdout periods, and you trust that past patterns will at least roughly indicate future performance. When this assumption breaks, the model does not gracefully degrade; it fails confidently. It produces precise, well-calibrated intervals around the wrong answer. The acute phase of COVID, from early to late 2020, was a clear illustration of this, and we wrote about it in a previous post. But the more interesting forecasting problem was not the shutdown. It was everything that came after. The period from late 2020 through 2022 was not a single coherent regime. It was a sequence of overlapping, asynchronous changes: vaccine rollouts that reached some markets months before others, border reopenings that followed their own country-level timelines, reclosures triggered by new variants that hit different corridors (a pairing of the traveler's origin city and destination city) at different moments. Demand was not recovering uniformly. It was rebounding unevenly across every corner of the world, in ways that had no historical precedent and no single governing pattern. The standard response to a shock is to wait for each affected market to accumulate its own post-shock data and retrain locally. But Covid was among the biggest shocks the travel industry has faced in decades. With markets worldwide reopening and reclosing on staggered schedules, waiting for markets to settle meant forecasting blind for months at a time, across all markets, just when timely projections were most needed, in the circumstances. So we started building something different. When we could not simply look backward in time for relevant examples, we ## How to Design Nested Documents for a Blogging App DevFeed: [How to Design Nested Documents for a Blogging App]() Original publisher: [Read original article]() Author: Nic Raboy Published: 2026-05-22T12:00:00Z Content type: tutorial Language: en Sources: [Nic Raboy]() Topics: [MongoDB](), [NoSQL](), [data-modeling](), [Content Management System]() Tags: [cms](), [data-modeling](), [database](), [how-to](), [mongodb](), [nosql]() ### AI overview A tutorial on designing nested documents for a blogging application in MongoDB. It examines document modeling choices for authors, blog posts, and comments, and explains why modeling these relationships as separate documents may not provide the best MongoDB experience. ### Source excerpt So you want to build your own content management system (CMS), also sometimes known as a blog? This is a classic example when learning how to use a database, whether it be a relational database manage... The post How to Design Nested Documents for a Blogging App appeared first on DataCamp. ## Building Agentic GraphRAG Systems DevFeed: [Building Agentic GraphRAG Systems]() Original publisher: [Read original article]() Author: Paul Iusztin Published: 2026-05-05T05:01:08Z Content type: tutorial Language: en Sources: [Decoding ML]() Topics: [Retrieval Augmented Generation (RAG)](), [data-modeling](), [MCP Server](), [AI Agent](), [context window]() Tags: [ai-agent](), [context-window](), [data-modeling](), [knowledge-graphs](), [langchain](), [latency](), [mcp-server](), [mongodb](), [rag]() ### AI overview The article explains agentic GraphRAG as a data-modeling problem involving knowledge graphs, ontologies, append-only data models, extraction modes, and hybrid retrieval. It describes exposing the resulting unified memory layer through an MCP server for AI agents. ### Source excerpt From knowledge graphs and ontologies to a unified memory as an MCP server for your AI agent. ## Top 10 best practices tips for ClickHouse DevFeed: [Top 10 best practices tips for ClickHouse]() Original publisher: [Read original article]() Author: Yonatan Dolan Published: 2026-03-26T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Optimization](), [data-modeling](), [Databases](), [Compression](), [Sorting](), [dataset](), [Monitoring](), [real-time](), [Open Source]() Tags: [analytics](), [architecture](), [benchmarks](), [clickhouse](), [compression](), [data](), [data-modeling](), [database](), [efficiency](), [observability](), [open-source](), [optimization](), [performance](), [popular](), [real-time](), [reduce](), [sorting](), [techniques]() ### AI overview A practical guide to ten ClickHouse best practices for improving query performance, compression, storage efficiency, and analytical workloads. It covers primary-key and sorting-key design, data types, schema and data modeling, table engines, materialized views, query and join optimization, and monitoring, with benchmark examples based on a 150-million-row dataset. ### Source excerpt Ten best practices for getting the most out of ClickHouse, from primary key design and data types to materialized views, ReplacingMergeTree, and join optimization -- illustrated with benchmarks on a 150M row dataset. ## How Data Scientists Create Impact in Complex Billing Systems DevFeed: [How Data Scientists Create Impact in Complex Billing Systems]() Original publisher: [Read original article]() Author: Madison Kohls Published: 2026-02-18T05:00:00Z Content type: opinion Language: en Sources: [Figma Blog]() Topics: [Data Science](), [Complex Systems](), [data-modeling](), [Instrumentation]() Tags: [complex-systems](), [data-modeling](), [data-science](), [instrumentation](), [systems]() ### AI overview The article argues that impactful data science is not limited to experiments, optimization, forecasting, or inferential modeling. In Figma's Billing infrastructure, it emphasizes modeling event lifecycles, reconciling data across systems, instrumentation, and building tools that make complex system behavior observable and verifiable. ### Source excerpt Not all impactful data science work involves experiments or optimization. Sometimes it's about making complex systems legible, correct, and safe to operate. ## The 2025 AI + Data Engineering Roadmap DevFeed: [The 2025 AI + Data Engineering Roadmap]() Original publisher: [Read original article]() Author: Zach Wilson Published: 2025-10-17T22:35:45Z Content type: tutorial Language: en Sources: [DataExpert.io Newsletter]() Topics: [data-engineering](), [Python](), [SQL](), [Artificial Intelligence](), [airflow](), [BigQuery](), [Orchestration](), [Apache Spark](), [data-modeling](), [Data Quality](), [Embeddings](), [Retrieval Augmented Generation (RAG)]() Tags: [2025](), [ai](), [airflow](), [bigquery](), [count](), [course](), [data-engineering](), [data-modeling](), [data-quality](), [embeddings](), [engineering](), [framer](), [newsletter](), [orchestration](), [python](), [rag](), [real-time](), [right-join](), [spark](), [sql]() ### AI overview A 2025 roadmap for entering data engineering, covering foundational SQL and Python skills, distributed computing, orchestration, data modeling, data quality, AI and data integrations, portfolio projects, and personal branding. ### Source excerpt Getting a data engineering job is complicated. ## The Modern Data Toolbox DevFeed: [The Modern Data Toolbox]() Original publisher: [Read original article]() Author: Doximity Published: 2025-08-18T00:36:00Z Content type: article Language: en Sources: [Doximity]() Topics: [data](), [Large Language Model](), [Machine learning](), [data-modeling](), [Statistics](), [Machine Learning, Security Attacks]() Tags: [data](), [data-modeling](), [datasets](), [fraud](), [fraud-detection](), [large-language-models-llms](), [machine-learning](), [statistics]() ### AI overview The article explains how to choose among large language models, machine learning, and statistical methods based on data characteristics, goals, scale, and explainability requirements. It argues that complex data problems often benefit from hybrid systems that combine these approaches, illustrating the idea with a multi-layered fraud detection system for payment processing. ### Source excerpt Matching the Tool to the Task A Quick Recap In a previous article, we focused on the strengths of Large Language Models (LLMs), traditional Machine Learning (ML), and statistical methods and recommended 4 key questions to help you choose the right tool for a data solution. Your Data: Is it structured or unstructured? Bounded or unbounded? Your Goal: Do you need prediction, generation, or inference? Your Data Volume: Are you working with massive datasets or limited samples? Your Need for Transparency: Is deep explainability or strict repeatability a requirement? The key takeaway was that LLMs excel at understanding and generating unstructured, unbounded language; ML models are the gold standard for prediction on structured data; and statistics are invaluable for inference and causality, especially with limited data. However, the most complex and valuable real-world problems rarely fit neatly into one box. What if you need to understand unstructured customer feedback and use it to accurately predict churn? This is where hybrid approaches come in, combining the capabilities of each tool to create a system that is greater than the sum of its parts. Below, we present a few examples showcasing how working with hybrid data approaches helps unlock greater value. Hybrid Data Solutions In our experience, the most effective data solutions often emerge from combining multiple data modeling approaches. Rather than viewing LLMs, ML, and statistics as competitors, we recommend considering them as complementary parts of your broader data toolbox. 1. A Multi-Layered Fraud Detection System built using ML, LLM and Statistics Let's consider a high-stakes and regulated environment of a payments processing system. The primary challenge is to detect and block fraudulent transactions in real-time without incorrectly declining legitimate purchases. In addition, the decision-making process should be transparent and auditable. The analytics workhorse of such a system will be a real-time trans ## Data Modeling for Java Developers: Structuring With PostgreSQL and MongoDB DevFeed: [Data Modeling for Java Developers: Structuring With PostgreSQL and MongoDB]() Original publisher: [Read original article]() Author: Aasawari Sahasrabuddhe Published: 2025-04-27T12:00:00Z Content type: tutorial Language: en Sources: [Nic Raboy]() Topics: [data-modeling](), [Java](), [MongoDB](), [PostgreSQL](), [Databases](), [Application Development]() Tags: [acid](), [application-development](), [data-modeling](), [java](), [mongodb](), [postgres](), [postgresql](), [tutorial](), [vs]() ### AI overview This tutorial explains data modeling for Java developers by comparing PostgreSQL's relational approach with MongoDB's document-oriented model. It covers database relationships, including many-to-many relationships, and discusses trade-offs such as rigid schemas, ACID compliance, and scalability. ### Source excerpt Application and system designs have always been considered the most essential step in application development. All the later steps and technologies to be used depend on how the system has been designe... The post Data Modeling for Java Developers: Structuring With PostgreSQL and MongoDB appeared first on DEV. ## A quick introduction to data modeling in real world applications DevFeed: [A quick introduction to data modeling in real world applications]() Original publisher: [Read original article]() Author: Pranshu Raj Published: 2025-04-13T15:22:00Z Content type: tutorial Language: en Sources: [Pranshu Raj - blog on backend systems, performance and sidequests]() Topics: [data-modeling](), [data-architecture](), [Databases](), [MongoDB]() Tags: [data-modeling](), [data-modelling](), [database](), [mongodb](), [relationships](), [schema]() ### AI overview An introductory tutorial on data modeling: identifying relevant entities, attributes, relationships, application requirements, and workload before designing a schema. It contrasts relational tables with document-database collections using Postgres and MongoDB as examples. ### Source excerpt What data modeling is, why it's so useful, how can we do it effectively to get the best results for our use case. ## Prompt to Production: Accelerate Development with v0 & Neon Postgres DevFeed: [Prompt to Production: Accelerate Development with v0 & Neon Postgres]() Original publisher: [Read original article]() Author: Ryan Vogel Published: 2025-03-31T18:27:40Z Content type: tutorial Language: en Sources: [Blog -- Neon Docs]() Topics: [AI-assisted coding](), [backend-development]() Tags: [api](), [code-generation](), [community](), [data-modeling](), [database](), [frontend](), [postgres](), [product](), [vercel](), [workflow]() ### AI overview A tutorial on using detailed prompts with Vercel v0 and Neon Postgres to prototype and deploy a full-stack time-clock application. It emphasizes reviewing generated database plans and iterating to keep the data model in scope. ### Source excerpt Vercel's v0 recently introduced integrations for Neon, Supabase, and Upstash. With these integrations, you can easily add persistent storage and deploy full-stack applications in minutes. However, detailed prompting is essential to guide v0 effectively. Since its initial launch,... ## Postgres to ClickHouse: Data Modeling Tips V2 DevFeed: [Postgres to ClickHouse: Data Modeling Tips V2]() Original publisher: [Read original article]() Author: Lionel Palacin & Sai Srirampur Published: 2025-03-06T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [data-modeling](), [PostgreSQL](), [Replication](), [Databases](), [Stack Overflow](), [Python]() Tags: [best-practices](), [blog-post](), [clickhouse](), [data-modeling](), [deep-dive](), [github](), [how-to](), [learn](), [performance](), [postgres](), [python](), [replication]() ### AI overview This advanced article explains how to replicate PostgreSQL data into ClickHouse for real-time analytics using CDC, ClickPipes, or PeerDB. It covers data modeling and query-performance practices, including deduplication, ordering keys, JOIN optimization, materialized views, and denormalization, with examples based on a StackOverflow dataset. ### Source excerpt Dive into how Postgres-to-ClickHouse replication works, and learn best practices for data deduplication, custom ordering keys, optimizing JOINs, denormalization, and more. ## Scaling Scientific Data: Migrating Benchling's Schema Model for Performance at Scale DevFeed: [Scaling Scientific Data: Migrating Benchling's Schema Model for Performance at Scale]() Original publisher: [Read original article]() Author: Melody Ding Published: 2024-12-04T15:01:06Z Content type: article Language: en Sources: [Benchling]() Topics: [data](), [Structured-data](), [data-processing](), [Database](), [PostgreSQL]() Tags: [applications](), [automated](), [benchling](), [collection](), [core](), [customer](), [customers](), [data](), [data-modeling](), [database-optimization](), [model](), [performance](), [postresql](), [product](), [scalable-architecture](), [scale](), [schema](), [science](), [software-engineering](), [speed](), [storage](), [structure](), [tests]() ### AI overview Benchling describes migrating its Schema Model to a more compact structure to improve data-ingestion performance as scientific data volumes grow. The phased transition aimed to balance speed and flexibility while avoiding PostgreSQL scaling limitations, including the need for sharding. ### Source excerpt Benchling is a unified platform for scientific data. It allows scientists to collaborate on complex science, automate work, and power AI. Customers store large volumes of data on our platform, leveraging it across many applications both within Benchling and in their own infrastructure. It's critical that customer data is accessible in a performant and scalable way. In this article, we'll explore a recent shift in how we store and retrieve customer data. By migrating to a more compact structure, we've tackled key performance challenges associated with increased data volumes. This transition has required a careful balance between speed and flexibility, as well as a phased approach that minimized disruption for users. Benchling Schemas At the core of Benchling's system is Schemas, a product that allows both Benchling internal teams and customers to configure the various shapes of data, defining fields, attributes, and constraints that entities must follow. These data structures represent entities like equipment, storage, biological molecules, workflows, tasks, lab notes, and recorded results from scientific tests. Schemas reside in what we refer to as the definition layer. An example schema for defining the data structure of a molecule Each instance of a schema, referred to as a schematizable item, represents the actual data input by scientists. We call this the instance layer. These items are populated with field values conforming to the schema's defined fields. As Benchling's user base grows and the amount of schematized data ingested into the platform increases every year, optimizing the storage of field values has become crucial. Relationship between actual instances of a molecule and its defined schemaThe Challenge: Scale and Performance Historically, Benchling saw a shift from manual data upload by scientists to integrations with lab equipment, leading to automated data collection. This significantly increased the speed and volume of data ingestion. Assay results [Next page]()