# Structured-data Structured data is data organized in a predefined format, typically using a fixed schema and tabular representation. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## KCP: How to Migrate to Confluent Cloud in Days, Not Weeks DevFeed: [KCP: How to Migrate to Confluent Cloud in Days, Not Weeks]() Original publisher: [Read original article]() Author: Ahmed Saef Zamzam Published: 2026-09-14T07:00:00Z Content type: tutorial Language: en Sources: [Confluent: Data in motion]() Topics: [Kafka](), [migration](), [Confluent Cloud](), [Replication](), [Infrastructure as code](), [Provisioning](), [Access Control](), [Structured-data]() Tags: [access-control](), [confluent](), [confluent-cloud](), [data-replication](), [infrastructure-as-code-iac](), [kafka](), [migration](), [open-source](), [provisioning](), [replication](), [schema]() ### AI overview The article explains how Confluent's open source KCP tool automates migration from Amazon MSK to Confluent Cloud. KCP supports discovery, infrastructure provisioning, ACL and schema mapping, and migration, while Cluster Linking provides offset-preserving data replication. Support for self-managed Kafka migrations is described as coming soon. ### Source excerpt Use Kafka Copy Paste (KCP) to automate Kafka migration with infrastructure generation, ACL and schema mapping, and offset-preserving data replication. ## PostgreSQL Monitoring and Schema Linting for Laravel with Vacuum DevFeed: [PostgreSQL Monitoring and Schema Linting for Laravel with Vacuum]() Original publisher: [Read original article]() Author: Paul Redmond Published: 2026-09-14T04:24:35Z Content type: article Language: en Sources: [Laravel]() Topics: [Laravel](), [PostgreSQL](), [Monitoring](), [ci](), [Databases](), [Structured-data]() Tags: [ci](), [github](), [laravel](), [laravel-packages](), [monitoring](), [postgresql](), [schema](), [sql]() ### AI overview Vacuum is a PostgreSQL monitoring and schema-linting package for Laravel. It analyzes PostgreSQL statistics, reports issues such as bloat, wraparound, dead tuples, unused indexes, slow statements, and unindexed foreign keys, and provides SQL remediation guidance, health scores, dashboards, CI commands, history, and explainers. ### Source excerpt Vacuum checks PostgreSQL in Laravel apps for bloat, wraparound, and unused indexes, and flags unindexed foreign keys in migrations during CI. The post PostgreSQL Monitoring and Schema Linting for Laravel with Vacuum appeared first on Laravel News. Join the Laravel Newsletter to get Laravel articles like this directly in your inbox. ## A Failed XML-Based DSL Design Using a Large Regular Expression DevFeed: [A Failed XML-Based DSL Design Using a Large Regular Expression]() Original publisher: [Read original article]() Author: Remy Porter Published: 2026-09-07T06:30:00Z Content type: opinion Language: en Sources: [The Daily WTF]() Topics: [XML](), [Structured-data](), [bug](), [Code]() Tags: [best-of](), [bug](), [code](), [documentation](), [domain-specific-languages](), [dsls](), [regex](), [xml]() ### AI overview The article revisits a failed attempt to build a domain-specific language with XML schemas and a 1,310-character regular expression. It describes a bug in the expression and criticizes the resulting design as an unsuccessful effort to save labor. ### Source excerpt It's a holiday here in the US, a celebration of labor, so we're reaching back through the archives for a story about an attempt to be labor saving that was not successful. Original. --Remy I've had to write a few domain-specific-languages in the past. As per Remy's Law of Requirements Gathering, it's been mostly because the users needed an Excel-like formula language. The danger of DSLs, of course, is that they're often YAGNI in the extreme, or at least a sign that you don't really understand your problem. XML, coupled with schemas, is a tool for building data-focused DSLs. If you have some complex structure, you can convert each of its features into an XML attribute. For example, if you had a grammar that looked something like this: The Source specification obeys the following syntax source = ( Feature1+Feature2+... ":" ) ? steps Feature1 = "local" | "global" Feature2 ="real" | "virtual" | "ComponentType.all" Feature3 ="self" | "ancestors" | "descendants" | "Hierarchy.all" Feature4 = "first" | "last" | "DayAllocation.all" If features are specified, the order of features as given above has strictly to be followed. steps = oneOrMoreNameSteps | zeroOrMoreNameSteps | componentSteps oneOrMoreNameSteps = nameStep ( "." nameStep ) * zeroOrMoreNameSteps = ( nameStep "." ) * nameStep = "#" name name is a string of characters from "A"-"Z", "a"-"z", "0"-"9", "-" and "_". No umlauts allowed, one character is minimum. componentSteps is a list of valid values, see below. Valid 'componentSteps' are: - GlobalValue - Product - Product.Brand - Product.Accommodation - Product.Accommodation.SellingAccom - Product.Accommodation.SellingAccom.Board - Product.Accommodation.SellingAccom.Unit - Product.Accommodation.SellingAccom.Unit.SellingUnit - Product.OnewayFlight - Product.OnewayFlight.BookingClass - Product.ReturnFlight - Product.ReturnFlight.BookingClass - Product.ReturnFlight.Inbound - Product.ReturnFlight.Outbound - Product.Addon - Product.Addon.Service - Product.Addon.ServiceFeatu ## When JSONB columns create schema, consistency, and performance problems DevFeed: [When JSONB columns create schema, consistency, and performance problems]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-09-01T00:00:00Z Content type: article Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [data](), [Structured-data](), [JSON]() Tags: [data-modelling](), [indexing](), [jsonb](), [migration](), [outage](), [postgres](), [postgresql](), [query](), [schema](), [schema-design](), [toast]() ### AI overview The article explains why using JSONB to avoid recurring migrations can create hidden schema and data-consistency problems. It discusses runtime failures from inconsistent keys and types, difficulty identifying dependencies across consumers, and the storage and update costs of large PostgreSQL JSONB documents. ### Source excerpt JSONB is a good fit for genuinely open-ended data and a poor one for schema you did not want to commit to yet. Without a schema there is no NOT NULL, no type, no foreign key and no way to know which keys are load bearing, so every read becomes a parse and a cast that can fail at runtime. Large documents are stored out of line and compressed, which means reading one key can require fetching and decompressing the whole document, and updating one key rewrites all of it. ## What an Ontology for AI Agents Actually Needs DevFeed: [What an Ontology for AI Agents Actually Needs]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-08-14T12:38:18Z Content type: opinion Language: en Sources: [Data Engineering Weekly]() Topics: [Artificial Intelligence](), [Graphs](), [Structured-data](), [data]() Tags: [ai](), [ai-agents](), [data](), [graph](), [knowledge-graph](), [schema]() ### AI overview The article argues that an ontology for AI agents should be treated as a governed semantic system rather than a single file or graph. It distinguishes ontology meaning from knowledge-graph facts and explains how semantic capabilities support retrieval, planning, action, verification, and operational governance. ### Source excerpt How to think about the semantic system that makes an agent coherent, governable, and useful. ## Treating Issue Bodies as Untrusted Input DevFeed: [Treating Issue Bodies as Untrusted Input]() Original publisher: [Read original article]() Author: Philipp Lehmann (philipp.lehmann@gruppe.ai) Published: 2026-08-14T07:00:00Z Content type: tutorial Language: en Sources: [Philipp Lehmann]() Topics: [prompt injection](), [GitHub](), [JSON Feed](), [Structured-data](), [issue tracker]() Tags: [agents](), [github](), [implementation](), [issue-tracker](), [json](), [llm](), [prompt-injection](), [schema](), [security](), [test]() ### AI overview The article explains how GitHub issue bodies can carry prompt injection attacks when an agent treats public input as instructions. It recommends fencing untrusted text, clearly labeling it, constraining model output to a fixed JSON action schema, and testing the validation boundary without a live model. ### Source excerpt Prompt injection via GitHub issue bodies: fence untrusted text and accept model output only as a fixed JSON action schema, tested without a model. ## Refactoring a SQL Table at Scale: Lessons from Harness CI DevFeed: [Refactoring a SQL Table at Scale: Lessons from Harness CI]() Original publisher: [Read original article]() Author: Moshe Tsur Published: 2026-08-06T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [Refactoring](), [Structured-data](), [Databases](), [SQL](), [Latency](), [API](), [ci]() Tags: [api](), [continuous-integration](), [harness](), [latency](), [refactoring](), [schema](), [sql]() ### AI overview Harness describes refactoring a flat SQL table used for CI test results into a normalized schema. The article reports reducing storage per row from 400 bytes to 28 bytes and making API latency constant at any scale. ### Source excerpt How Harness refactored a flat SQL table into a normalized schema, cutting storage per row from 400 bytes to 28 bytes and making API latency constant at any scal | Blog ## Paul Klein on collapsing the agent stack into one platform DevFeed: [Paul Klein on collapsing the agent stack into one platform]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-05T23:22:36Z Content type: article Language: en Sources: [WorkOS Blog]() Topics: [Artificial Intelligence](), [browser](), [API](), [Caching](), [Structured-data](), [Routing (disambiguation)]() Tags: [agent](), [agents](), [ai](), [api](), [browser](), [browsers](), [caching](), [model](), [route](), [routing](), [search](), [speed]() ### AI overview Browserbase founder and CEO Paul Klein discusses the company's effort to combine browser infrastructure, search, fetching, models, routing, and caching into one platform for web-browsing agents. The article also covers the launch of Browserbase agents, a prebuilt harness intended to reduce the work required to build such agents. ### Source excerpt Browserbase founder Paul Klein on collapsing the agent stack into one platform, pointing cheap models at simple sites, and why speed is the last constraint. ## How to structure a log DevFeed: [How to structure a log]() Original publisher: [Read original article]() Author: Kyle Tryon Published: 2026-07-21T09:00:00Z Content type: tutorial Language: en Sources: [Sentry Blog]() Topics: [Logging](), [Structured-data](), [TypeScript](), [ESLint](), [SIEM, Security, Observability]() Tags: [code](), [guide](), [lint](), [logs](), [programming](), [schema]() ### AI overview A practical guide to writing structured logs that are consistent, queryable, and useful for debugging in production. It covers event naming, flattened attributes, predictable low-cardinality values, severity levels, primitive-value constraints, and enforcing conventions with an ESLint plugin for TypeScript. ### Source excerpt A practical guide to writing structured, queryable, lint-enforceable logs with consistent event names, attributes, severity, and schema checks. ## Devavrat Shah's research and Ikigai Labs use tabular data for real-time forecasting and decision-making DevFeed: [Devavrat Shah's research and Ikigai Labs use tabular data for real-time forecasting and decision-making]() Original publisher: [Read original article]() Author: David Chandler | Laboratory for Information and Decision Systems Published: 2026-07-14T20:25:00Z Content type: article Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [decision-making](), [Structured-data](), [AI Models](), [data](), [Time Series](), [Computer science](), [Electrical engineering and computer science (EECS)](), [Temporal data](), [systems]() Tags: [ai](), [ai-and-decision-making](), [ai-in-business-planning](), [ai-models](), [algorithms](), [artificial-intelligence](), [business-and-management](), [business-modeling](), [celonis-context-model](), [computer-science-and-technology](), [data](), [data-systems](), [decision-making](), [devavrat-shah](), [electrical-engineering-and-computer-science-eecs](), [faculty](), [forecasting](), [idss](), [ikigailabs](), [laboratory-for-information-and-decision-systems-lids](), [mit-eecs](), [mit-idss](), [mit-intellectual-property](), [mit-lids](), [mit-schwarzman-college-of-computing](), [predictions](), [profile](), [real-time](), [research](), [school-of-engineering](), [startups](), [structured](), [tabular-data](), [time-series-data]() ### AI overview MIT Professor Devavrat Shah's research led to a foundation model for tabular and time-series enterprise data. Developed with Ikigai Labs, the system continuously tests predictions against real outcomes to support large-scale forecasting and decision-making. ### Source excerpt Through research and entrepreneurship, Professor Devavrat Shah is helping to design methods that can handle constant decision-making using limited computational resources. ## Building Which-Fuji: A Side Project for Fujifilm Camera Enthusiasts DevFeed: [Building Which-Fuji: A Side Project for Fujifilm Camera Enthusiasts]() Original publisher: [Read original article]() Author: masnun Published: 2026-06-28T06:11:45Z Content type: opinion Language: en Sources: [Abu Ashraf Masnun]() Topics: [Website](), [Web](), [Cloudflare](), [Search engine optimization (SEO)](), [Google Search](), [Structured-data](), [Content Management System]() Tags: [building](), [camera](), [cloudflare](), [cloudflare-pages](), [google-search](), [project](), [robots](), [seo](), [side-project](), [uncategorized]() ### AI overview The article presents Which-Fuji, a small website for helping people choose a Fujifilm camera. It describes the site's browse page, quiz, camera reviews, and longer-form articles, along with the author's reasons for building it. The project uses a static-first architecture on Cloudflare Pages and explores structured data, sitemaps, robots policies, and discovery in Google Search. ### Source excerpt I've been quietly working on a small side project for the last few weeks, and it's finally at a point where I can share it properly. Which-Fuji is a tiny website I built to help people figure out which Fujifilm camera to buy. That's it. No reviews, no affiliate spam, no walls of text -- just [...] The post Building Which-Fuji: A Side Project for Fujifilm Camera Enthusiasts appeared first on Abu Ashraf Masnun. ## Schema markup for SEO: What it is and how it works DevFeed: [Schema markup for SEO: What it is and how it works]() Original publisher: [Read original article]() Author: Webflow Team Published: 2026-06-24T00:00:00Z Content type: tutorial Language: en Sources: [Webflow Blog]() Topics: [Structured-data](), [Search engine optimization (SEO)](), [Website](), [AI search]() Tags: [ai-search](), [schema](), [search](), [search-engines](), [seo](), [strategy](), [web]() ### AI overview This tutorial explains schema markup, also called structured data, and how to add relevant Schema.org properties to web pages. It describes how structured data can support rich search results, SEO visibility, user experience, and discovery in AI search, while noting that it does not guarantee higher rankings. ### Source excerpt Learn how schema markup for SEO can drive more traffic to your site by giving search engines the right information for rich search results. ## The Snapshot Machine: genie learn and diff as a Change-Validation Safety Net on CML2 DevFeed: [The Snapshot Machine: genie learn and diff as a Change-Validation Safety Net on CML2]() Original publisher: [Read original article]() Author: Brent Leekley Published: 2026-06-22T00:00:00Z Content type: tutorial Language: en Sources: [brent.leekley.me blog]() Topics: [networking](), [Cisco](), [Routing (disambiguation)](), [Structured-data](), [Command-line interface]() Tags: [change-validation](), [cisco](), [cisco-modeling-labs](), [cli](), [cml2](), [genie](), [genie-diff](), [genie-learn](), [maintenance-window](), [network-automation](), [network-testing](), [ospf](), [pyats](), [routing-table](), [snapshot](), [structured](), [validation]() ### AI overview This tutorial demonstrates using Cisco Modeling Labs 2 with Genie learn and diff to validate network changes. It snapshots structured operational state before and after a maintenance change, then compares the snapshots to reveal side effects that routine show commands may miss. The source summary reports that a changed OSPF cost reroutes three destinations while adjacencies remain FULL. ### Source excerpt Snapshot a CML2 fabric with genie learn before and after a maintenance change, then genie diff the structured state. One fat-fingered OSPF cost quietly reroutes three destinations while every adjacency stays FULL. Structured before-and-after diffs catch the side effects that show commands miss. ## From Intent to Reality: Driving pyATS Tests from NetBox Against a CML2 Fabric DevFeed: [From Intent to Reality: Driving pyATS Tests from NetBox Against a CML2 Fabric]() Original publisher: [Read original article]() Author: Brent Leekley Published: 2026-06-15T00:00:00Z Content type: tutorial Language: en Sources: [brent.leekley.me blog]() Topics: [NetBox](), [Network](), [Cisco](), [data](), [Structured-data]() Tags: [cisco-modeling-labs](), [cml](), [cml2](), [custom-fields](), [genie](), [ids](), [intent-based-networking](), [netbox](), [network](), [network-automation](), [network-testing](), [ospf](), [pyats](), [pynetbox](), [router-id](), [source-of-truth](), [spine-leaf](), [structured](), [testbed]() ### AI overview This tutorial shows how to store declared OSPF router-IDs in NetBox and use Cisco pyATS to compare them with the router-IDs on a live CML2 spine-leaf fabric. The comparison turns network intent validation into a pass-or-fail test. ### Source excerpt NetBox holds the intent (declared OSPF router-IDs), a CML2 spine-leaf is the network under test, and pyATS is the judge. Close the loop and prove the live network matches what you said it should be, with real pass and fail output. ## OTel-Arrow Phase 2: From Efficient Transport to Efficient Telemetry Pipelines DevFeed: [OTel-Arrow Phase 2: From Efficient Transport to Efficient Telemetry Pipelines]() Original publisher: [Read original article]() Author: OpenTelemetry Authors; Docs CC BY Published: 2026-06-13T00:47:46Z Content type: article Language: en Sources: [Blog on OpenTelemetry]() Topics: [OpenTelemetry](), [telemetry](), [Structured-data](), [systems](), [Rust](), [Instrumentation]() Tags: [apache-arrow](), [memory](), [opentelemetry](), [overhead](), [pipelines](), [processors](), [rust](), [telemetry]() ### AI overview This article presents Phase 2 of OTel-Arrow, which uses Apache Arrow not only for transport through OTAP but also as the internal representation of telemetry pipelines. It describes the Rust-based OTel-Arrow Dataflow Engine and reports substantially lower transport and processing costs when telemetry remains on the OTAP path end to end. ### Source excerpt Phase 1 of OTel-Arrow established OTAP, the OpenTelemetry Arrow Protocol, as an efficient transport protocol for OpenTelemetry. Apache Arrow is a language-independent, columnar in-memory format designed to move and process structured data efficiently across systems. We demonstrated that telemetry could be transported with significantly lower network overhead while preserving compatibility with the OpenTelemetry data model. Phase 2 asked a different question: what happens if Arrow is used not only on the wire, but also as the representation the pipeline works with internally? ## Your event-driven architecture is a distributed monolith -- and that's fine, if you measure it DevFeed: [Your event-driven architecture is a distributed monolith -- and that's fine, if you measure it]() Original publisher: [Read original article]() Author: fidelitas.io Published: 2026-05-15T10:30:00Z Content type: opinion Language: en Sources: [fidelitas.io]() Topics: [event driven](), [Messaging](), [Structured-data](), [Traces](), [interface]() Tags: [architecture](), [coupling](), [decoupling](), [distributed-systems](), [event-driven-architecture](), [messaging](), [observability](), [schemas](), [services](), [traces]() ### AI overview This opinion article argues that event-driven systems can become distributed monoliths when message brokers hide rather than remove coupling. It focuses on explicit, versioned, validated schemas and traces as ways to reveal dependencies and diagnose failures. ### Source excerpt Async messaging didn't decouple your services; it hid the coupling. Here's how to find the seams again with traces, schemas, and a healthy fear of fan-out. ## How XY builds an AI agent orchestration platform for healthcare with Temporal DevFeed: [How XY builds an AI agent orchestration platform for healthcare with Temporal]() Original publisher: [Read original article]() Author: The XY Engineering Team Published: 2026-04-23T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [agent orchestration](), [AI Agent](), [Orchestration](), [reliability](), [human review](), [sensitive data](), [Structured-data](), [YAML]() Tags: [agent](), [agent-orchestration](), [ai](), [ai-agent](), [automation](), [how-to](), [human-review](), [long-running](), [orchestration](), [reliability](), [sensitive-data](), [temporal](), [workflow]() ### AI overview XY describes using Temporal as a DSL-driven execution engine for an AI agent orchestration platform serving complex healthcare workflows. The approach uses a YAML-based workflow language and a generic Temporal workflow class to coordinate multiple systems, reliability features, human review, and sensitive healthcare data from prototype to production. ### Source excerpt One generic Temporal workflow class can provide infinite healthcare automation. See how XY built a DSL-driven AI agent orchestration platform that scales from prototype to production. ## SaaS Billing Best Practices Every Founder Should Know DevFeed: [SaaS Billing Best Practices Every Founder Should Know]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-04-09T00:00:00Z Content type: article Language: en Sources: [Dodo Payments Blog]() Topics: [Software as a service](), [Automation](), [Structured-data]() Tags: [automation](), [best-practices](), [billing](), [churn](), [compliance](), [invoicing](), [retry](), [revenue](), [saas](), [saas-billing]() ### AI overview This article presents SaaS billing best practices covering invoice timing and machine-readable invoice data, failed-payment recovery, dunning, proration, tax compliance, and billing automation. ### Source excerpt The billing mistakes that silently kill SaaS revenue - and the best practices that prevent them. Covers invoicing, dunning, proration, tax compliance, and AI-powered billing automation. ## Oracle CDC now available in Redpanda Connect DevFeed: [Oracle CDC now available in Redpanda Connect]() Original publisher: [Read original article]() Author: Prakhar Garg Published: 2026-04-09T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Databases](), [Streaming](), [Structured-data](), [Transactions](), [data](), [systems]() Tags: [announcement](), [data](), [debezium](), [event](), [jvm](), [oracle](), [redpanda-connect](), [schema](), [streaming](), [transactions]() ### AI overview Redpanda Connect v4.83.0 introduces a native Oracle CDC connector that captures inserts, updates, and deletes without a JVM, Kafka Connect cluster, or separate workers. It can snapshot existing tables, stream subsequent changes from Oracle redo logs, resume from checkpoints, and route schema-tracked Avro events to per-table Redpanda topics. ### Source excerpt Native CDC for Oracle enables real-time access without rearchitecting. No JVM, middleware, or operational overhead. ## Resource Names for Enterprise TypeScript Monorepos DevFeed: [Resource Names for Enterprise TypeScript Monorepos]() Original publisher: [Read original article]() Author: Robin Wieruch Published: 2026-03-30T06:50:46Z Content type: tutorial Language: en Sources: [Robin Wieruch]() Topics: [TypeScript](), [Code](), [Structured-data]() Tags: [api-design](), [code](), [consistency](), [enterprise](), [monorepo](), [monorepo-resource-id](), [resource-oriented-design-typescript](), [typescript](), [typescript-resource-names](), [utilities]() ### AI overview This tutorial explains how to standardize resource names in multi-service TypeScript monorepos. It presents hierarchical resource naming, based on the Google API Design Guide, as a shared convention that replaces ad hoc string interpolation and supports consistent encoding and decoding across services. ### Source excerpt How to bring consistency to multi-service TypeScript applications by treating resource names and resource types as first-class concepts with shared utilities ... ## Smarter deployments in Tinybird: faster schema changes for the most common operations DevFeed: [Smarter deployments in Tinybird: faster schema changes for the most common operations]() Original publisher: [Read original article]() Author: Jordi Orihuela Published: 2026-03-25T00:00:00Z Content type: release Language: en Sources: [Tinybird]() Topics: [Structured-data]() Tags: [engineering-excellence](), [schema]() ### AI overview Tinybird deployments now support ALTER TABLE for simple schema changes, allowing those changes to deploy in seconds instead of requiring a full table rebuild. ### Source excerpt Most schema changes don't need a full table rebuild. We added ALTER TABLE support to Tinybird deployments so simple changes deploy in seconds, not minutes. ## Structured outputs with Pydantic AI DevFeed: [Structured outputs with Pydantic AI]() Original publisher: [Read original article]() Author: Ed Berry Published: 2026-03-24T10:34:08Z Content type: tutorial Language: en Sources: [FreeAgent]() Topics: [Large Language Model](), [JSON Schema](), [Prompt Engineering](), [Pydantic](), [Python](), [Structured-data]() Tags: [ai](), [data](), [data-ml](), [data-science](), [data-validation](), [genai](), [json-schema](), [llms](), [prompt-engineering](), [python](), [schema](), [structured-output]() ### AI overview This article explains structured outputs for LLMs using Pydantic AI. It introduces the relationship between Python type hints, static analysis, Pydantic, and runtime data validation, while noting that Pydantic AI is model-agnostic. ### Source excerpt One of the challenges of working with LLMs is getting them to respond with a consistent format, such as a given JSON schema. Anyone who has tried to solve this issue with prompt engineering knows how frustrating it can be. You add a 'MUST' here and an 'always return JSON' there, but still the output [...] ## The Schema-Driven LLM Query Pattern DevFeed: [The Schema-Driven LLM Query Pattern]() Original publisher: [Read original article]() Author: Rick Donato Published: 2026-02-27T15:09:32Z Content type: tutorial Language: en Sources: [Packet Coders - Learn Network Automation]() Topics: [Large Language Model](), [Structured-data](), [Security](), [data](), [pandas](), [Query (disambiguation)]() Tags: [ai](), [blog](), [data](), [databases](), [graphql](), [llm](), [pandas](), [rest](), [schema](), [security](), [structured]() ### AI overview This tutorial presents the Schema-Driven LLM Query Pattern. Instead of sending raw data to an external LLM, it sends the data schema, receives query syntax, and executes the query locally to reduce data exposure and token usage. ### Source excerpt Typically, when working with an LLM, you send your data along with a prompt and receive a response. But this pattern often introduces issues, especially with public LLMs, due to security concerns and token bloat. There is a different pattern we can use: the Schema-Driven LLM Query Pattern. With ## Database Design Doesn't Start With Table. It Starts With Consequences DevFeed: [Database Design Doesn't Start With Table. It Starts With Consequences]() Original publisher: [Read original article]() Author: The T-Shaped Dev Published: 2026-02-17T05:19:18Z Content type: tutorial Language: en Sources: [The T-Shaped Dev]() Topics: [Databases](), [Structured-data](), [Back end](), [PostgreSQL]() Tags: [backend](), [databases](), [how-to](), [postgresql](), [schema](), [schema-design]() ### AI overview An introduction to database design for backend engineers. The article explains why schema decisions have long-term consequences, recommends modeling the business domain before designing tables, and discusses normalization and database constraints. ### Source excerpt Here's what most backend engineers get wrong about schema design and how to fix it before you hit production. (6 min) [Next page]()