# data-modeling Data modeling is the foundational practice of creating abstract representations of an organization's data structures, including conceptual, logical, and physical models. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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 ## 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? ## 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. ## In 2026 The Data Fundamentals Matter More Than Ever DevFeed: [In 2026 The Data Fundamentals Matter More Than Ever]() Original publisher: [Read original article]() Author: SeattleDataGuy Published: 2026-06-13T22:51:35Z Content type: opinion Language: en Sources: [SeattleDataGuy's Newsletter]() Topics: [data-engineering](), [data-modeling](), [Data Science](), [Python](), [SQL](), [Artificial Intelligence]() Tags: [2026](), [data](), [data-engineering](), [data-science](), [developer](), [fundamentals](), [python](), [sql]() ### AI overview This opinion article argues that data fundamentals remain important in 2026 despite changing technology trends and job titles. It identifies messy data and weak data foundations as persistent bottlenecks, and emphasizes SQL, Python, data modeling, and related engineering skills. ### Source excerpt Otherwise we are headed towards a massive data mess ## 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 ## 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 ## How Pinecone writes documentation DevFeed: [How Pinecone writes documentation]() Original publisher: [Read original article]() Author: Tiffany Chen Published: 2025-07-25T00:00:00Z Content type: article Language: en Sources: [Mintlify Blog]() Topics: [Documentation](), [Information Architecture](), [Claude Code](), [data-modeling]() Tags: [architecture](), [best-practices](), [claude-code](), [developer](), [documentation](), [onboarding](), [technical-writing]() ### AI overview An interview with Pinecone documentation lead Jesse Seldess about restructuring documentation around user goals, improving information architecture, using Claude Code for content reviews, and hiring technical writers who collaborate across teams. ### Source excerpt Jesse Seldess runs documentation at Pinecone, where he's shaping how developers learn and succeed with one of the leading vector database platforms. Previously, he's scaled developer education from the ground up at renowned organizations like Cockroach Labs. ## 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. ## 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. ## How we model our data warehouse DevFeed: [How we model our data warehouse]() Original publisher: [Read original article]() Author: Jack Colsey Published: 2024-11-08T16:31:00Z Content type: article Language: en Sources: [The incident.io Blog]() Topics: [data-architecture](), [data-modeling](), [data]() Tags: [blog](), [blog-post](), [data](), [data-modeling](), [incident](), [incident-channel](), [incident-management](), [incident-response](), [model](), [outage](), [post-mortem](), [salesforce](), [slack-incident](), [stripe]() ### AI overview This blog post explains incident.io's data warehouse design and data-modeling principles. It describes a layered structure based on dbt's approach, using staging, intermediate, and marts layers, with guidance on renaming columns, casting data types, joining tables, performing reusable calculations, and organizing customer-facing and internal data models. It also discusses how these principles support flexible but consistent modeling in a BI tool and how intermediate calculations can improve pipeline performance. ### Source excerpt Curious about the inner workings of our data warehouse? We've shared a lot about our data stack, but this time we're diving into the design principles behind our warehouse. This blog breaks down how we structure our data, from staging to marts layers, and how we use it all in our BI tool. It's a quick look into how we keep things flexible, efficient, and built to scale. ## Announcing! Everything Data Connect Series DevFeed: [Announcing! Everything Data Connect Series]() Original publisher: [Read original article]() Author: Andrea Wu Published: 2024-10-22T00:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Cloud](), [PostgreSQL](), [data-modeling](), [Database](), [App](), [SDKs]() Tags: [blog-post](), [cloud](), [data-modeling](), [databases](), [firebase](), [postgresql](), [sdks](), [sql-connect](), [updates]() ### AI overview Firebase announces the "Everything Data Connect" blog series, which will cover Firebase Data Connect, including data modeling, queries, mutations, end-to-end setups, and generated SDKs. Data Connect connects Firebase apps to PostgreSQL databases hosted on Cloud SQL and is available in public preview. ### Source excerpt News, tutorials, and updates from the Firebase team. ## The Complex Data Models Behind Shopify's Tax Insights Feature DevFeed: [The Complex Data Models Behind Shopify's Tax Insights Feature]() Original publisher: [Read original article]() Author: Siraj Ali Published: 2023-02-08T15:00:04Z Content type: article Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [Shopify](), [data-architecture](), [data-modeling](), [Apache Spark](), [Google Cloud Platform (GCP)](), [Databases](), [Back end](), [SQL](), [Cloud]() Tags: [backend](), [cloud-storage](), [data](), [data-architecture](), [data-modeling](), [databases](), [google-cloud](), [shopify](), [spark](), [sql]() ### AI overview This article explains the data architecture behind Shopify Tax's Tax Insights feature. It covers requirements gathering, SQL-based data-model prototyping, dynamically changing data, Spark processing jobs, data warehousing in Google Cloud Storage, and publishing insights for display in the merchant application. ### Source excerpt The intensive data work behind Shopify's Tax Insights feature required building functionality to handle dynamically changing information ## What is a Full Stack Data Scientist? DevFeed: [What is a Full Stack Data Scientist?]() Original publisher: [Read original article]() Author: Micayla Wood Published: 2022-09-15T13:35:00Z Content type: article Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [Data Science](), [data-modeling](), [product analytics](), [engineering-culture]() Tags: [data-modeling](), [data-science](), [engineering](), [machine-learning](), [pipeline](), [product-analytics](), [production](), [shopify](), [streaming]() ### AI overview Shopify defines a full stack data scientist as someone who takes ownership of a data science project across its entire lifecycle, from discovery and analysis through data acquisition, modeling, pipeline development, and production delivery. The article emphasizes communication with stakeholders, engineering practices, product analytics, and proactively using data to solve business problems. ### Source excerpt At Shopify, we've embraced full stack data science, so we chatted with our data scientists to share what it means to be a full stack data scientist. ## How Do You Know What ActiveRecord Table Has a user\_id Attribute? DevFeed: [How Do You Know What ActiveRecord Table Has a user\_id Attribute?]() Original publisher: [Read original article]() Author: Fuzzygroup Published: 2019-12-28T00:00:00Z Content type: tutorial Language: en Sources: [Scott Johnson]() Topics: [Development](), [data-modeling](), [business logic]() Tags: [active-record](), [activerecord](), [business-logic](), [class](), [classes](), [console](), [development](), [id](), [jumpstart](), [metaprogramming](), [migration](), [migrations](), [rails](), [relationships](), [schema](), [table]() ### AI overview This article explains how to identify application classes whose ActiveRecord-backed tables have a user_id attribute. It introduces a DataObject class with a .has_user_id method that can be run in the console, helping the author add belongs_to user relationships without repeatedly checking the schema. ### Source excerpt Even though I'm a firm, firm believer in agile, I've recently been experimenting with a throwback to waterfall style development and it very quickly left me with a fully featured data structure of tables and relationships modeled as example data. What I did is very rapidly write a series of migrations and then populate them to represent a sample "installation". My goal with this approach was to play to my strengths - data modeling - and avoid getting tied down in user interface stuff (my weakness). This was a very, very interesting approach and what I found was that I got much further along the lines of the "guts" of an application simply because I never got discouraged by: Oh Shite - I know this should look good but I'm too much of a hoser to make it look good; I guess I'll put it aside and go watch TV The downside to this is that I never bothered setting up the normal associations that you do when you write a migration; I simply thought about this application in terms of the example data: the users who would be using the system the data objects that they would create how the data objects would interact with each other One of the things that I noticed when I started filling in the basics like "belongs_to :user" was that I kept constantly jumping between the class I was working on and the schema file. Finally it hit me - what I needed was a method that I could execute in the console that would tell me what classes had a user_id attribute. And so I wrote a class called DataObject (for an ActiveRecord class which stores data) and a method .has_user_id. What I was looking for was output that looked like this: > DataObject.has_user_id Initiative Yes - has a user_id field KeyResultOwner Yes - has a user_id field KeyResult Yes - has a user_id field ObjectiveOwner Yes - has a user_id field ObjectiveType Objective Yes - has a user_id field OkrTeamMember Yes - has a user_id field OkrTeam Yes - has a user_id field OrganizationGroup Organization Quarter ResponsibilityRole Stat ## FOSDEM 2019: Data Modeling, Normalization, and Denormalisation DevFeed: [FOSDEM 2019: Data Modeling, Normalization, and Denormalisation]() Original publisher: [Read original article]() Published: 2019-02-02T22:00:00Z Content type: article Language: en Sources: [Dimitri Fontaine]() Topics: [PostgreSQL](), [data-modeling](), [FOSDEM](), [data-architecture](), [data](), [Open Source]() Tags: [data-modeling](), [database](), [fosdem](), [open-source](), [postgresql](), [schema]() ### AI overview A FOSDEM 2019 talk about practical database schema modeling with PostgreSQL. It covers normalization examples, anti-patterns, denormalization techniques, advanced data types, and the trade-offs involved in applying these approaches. ### Source excerpt As a developer using PostgreSQL one of the most important tasks you have to deal with is modeling the database schema for your application. In order to achieve a solid design, it's important to understand how the schema is then going to be used as well as the trade-offs it involves. As Fred Brooks said: "Show me your flowcharts and conceal your tables, and I shall continue to be mystified. Show me your tables, and I won't usually need your flowcharts; they'll be obvious." In this talk we're going to see practical normalisation examples and their benefits, and also review some anti-patterns and their typical PostgreSQL solutions, including Denormalization techniques thanks to advanced Data Types. ## Database Modelization Anti-Patterns DevFeed: [Database Modelization Anti-Patterns]() Original publisher: [Read original article]() Author: Dimitri Fontaine PostgreSQL Major Contributor; Author Published: 2018-03-08T17:00:43Z Content type: article Language: en Sources: [Dimitri Fontaine]() Topics: [data-modeling](), [PostgreSQL](), [SQL](), [Database]() Tags: [data-modeling](), [database](), [postgresql](), [sql]() ### AI overview This article examines database modeling and normalization by discussing three anti-patterns: entity-attribute-value designs, storing multiple values in one column, and using UUIDs in some situations. It focuses on the practical problems these designs can create, including typos, weak typing, extra processing, and more complex SQL queries. ### Source excerpt Next week we see two awesome PostgreSQL conferences in Europe, back to back, with a day in between just so that people may attend both! In chronological order we have first Nordic pgDay in Oslo where I will have the pleasure to talk about Data Modeling, Normalization and Denormalization. Then we have pgday.paris with an awesome schedule and a strong focus on the needs of application developers! ## Simple Data Modeling with a Test Data Set DevFeed: [Simple Data Modeling with a Test Data Set]() Original publisher: [Read original article]() Author: Dimitri Fontaine PostgreSQL Major Contributor; Author Published: 2017-11-27T15:23:44Z Content type: tutorial Language: en Sources: [Dimitri Fontaine]() Topics: [data-modeling](), [PostgreSQL](), [test data](), [SQL]() Tags: [data-modeling](), [how-to](), [postgresql](), [sql](), [test-data]() ### AI overview This tutorial explains how to develop and test a PostgreSQL database schema using SQL scripts, sample data, explicit transactions, and rollback. It demonstrates a forum-style schema populated with random data so the model can be refined interactively without affecting existing application code. ### Source excerpt In How to Write SQL we saw how to write SQL queries as separate .sql files, and we learnt about using query parameters with the psql syntax for that (:variable, :'variable', and :"identifier"). For writing our database model, the same tooling is all we need. An important aspect of using psql is its capacity to provide immediate feedback, and we can also have that with modeling too.