# SQL Published articles for SQL. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## The official ClickHouse provider for Apache Airflow is now available DevFeed: [The official ClickHouse provider for Apache Airflow is now available]() Original publisher: [Read original article]() Author: Aditya Chidurala; Bentsi Leviav; Alex Francoeur Published: 2026-09-17T18:06:01Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [airflow](), [clickhouse](), [Data pipelines](), [SQL](), [Python](), [Open Source]() Tags: [airflow](), [clickhouse](), [data-pipelines](), [integration](), [python](), [release](), [sql]() ### AI overview ClickHouse has released an officially maintained Apache Airflow provider for orchestrating ClickHouse data workflows. The provider uses ClickHouse Connect over HTTP(S), supports Airflow's common SQL operators, and includes a hook for bulk and client-specific operations. ### Source excerpt The official ClickHouse provider for Apache Airflow simplifies data workflows with standard SQL operators, bulk inserts, and shared setup across self-managed Airflow and Astronomer. ## From guidance to action: Security fundamentals that materially reduce risk DevFeed: [From guidance to action: Security fundamentals that materially reduce risk]() Original publisher: [Read original article]() Author: Ron Pessner Published: 2026-09-17T17:00:00Z Content type: article Language: en Sources: [Microsoft Security Blog]() Topics: [Cybersecurity](), [Exposure Management](), [Artificial Intelligence](), [Vulnerabilities](), [Hacking](), [incident]() Tags: [agentic-security](), [ai](), [exposure-management](), [passwords](), [permissions](), [pypi](), [security](), [speed](), [sql]() ### AI overview Microsoft describes how AI is changing the cybersecurity threat landscape and argues that foundational controls remain essential. The article highlights excessive permissions, weak authentication, unpatched systems, exposed execution paths, and gaps between controls, along with guidance for governing agent identities and tools, isolating execution, restricting connectivity, monitoring behavior, and reducing exposure. ### Source excerpt AI has made fundamental changes to the operating environment for cybersecurity. Explore exposure management guidance on recommended controls and take action and stay ahead of cyberthreats. The post From guidance to action: Security fundamentals that materially reduce risk appeared first on Microsoft Security Blog. ## Релиз OpenIDE 2026.2: одна IDE для Java, Kotlin, Go, Python и веб-разработки DevFeed: [Релиз OpenIDE 2026.2: одна IDE для Java, Kotlin, Go, Python и веб-разработки]() Original publisher: [Read original article]() Author: honest\_niceman (Haulmont) Published: 2026-09-17T09:12:16Z Content type: release Language: ru Sources: [Tagir Valeev]() Topics: [intellij-platform](), [Java](), [Git](), [Kotlin](), [Gradle](), [Databases](), [Docker](), [Go](), [Python](), [SQL](), [JSON]() Tags: [2](), [2026](), [2026-2](), [compose](), [db-eaa4bca15522](), [docker](), [docker-bdf52bf499c4](), [git](), [go](), [gradle](), [ide-1f5689fe989b](), [ide-af948443b041](), [ide-java-0ad4946a0e57](), [intellij-idea-27f15aabb340](), [intellij-platform](), [java](), [javascript](), [json](), [kotlin](), [openide](), [openide-2026-2](), [openide-pro](), [oracle](), [sql](), [structure]() ### AI overview OpenIDE 2026.2 updates its base platform, expands Java and Kotlin support, improves Git and Gradle workflows, and updates its Java profiler. The release also includes improvements to the DB client and Docker plugin, including SQL formatting, Greenplum support, JSON highlighting, and enhanced Compose service management. ### Source excerpt Обновили базовую платформу до версии 2026.2, расширили поддержку Java и Kotlin, улучшили работу с Git и обновили Java-профилировщик. Доработали редактор и подсказки в терминале, добавили проверки настроек Gradle и упростили переход к исходникам библиотек. Через Git worktrees теперь можно работать с несколькими ветками одновременно. Читать далее ## Introducing Lead: TIN-compatible full-text search for CI DevFeed: [Introducing Lead: TIN-compatible full-text search for CI]() Original publisher: [Read original article]() Author: Eric Ridge Published: 2026-09-17T00:00:00Z Content type: release Language: en Sources: [Blog -- PlanetScale]() Topics: [ci](), [Development](), [Databases](), [SQL]() Tags: [3](), [acid](), [ansi](), [bm25](), [ci](), [concurrent](), [engineering](), [extension](), [full-text-search](), [html](), [lead](), [make](), [postgres](), [search](), [sql](), [staging](), [tests](), [tokenizers](), [transactions](), [version]() ### AI overview Lead is a TIN-compatible PostgreSQL full-text search extension designed for CI, development, and staging. It preserves TIN's search features and correctness but uses full table scans, making it much slower and suitable for small test datasets rather than production workloads. ### Source excerpt Lead is a feature-equivalent version of TIN you can run in CI ## "Regex for Rows": Simplifying Pattern Detection in SQL with MATCH\_RECOGNIZE DevFeed: ["Regex for Rows": Simplifying Pattern Detection in SQL with MATCH\_RECOGNIZE]() Original publisher: [Read original article]() Author: Kent Marten; Sergei Fedorov Published: 2026-09-16T17:14:05Z Content type: tutorial Language: en Sources: [Databricks]() Topics: [SQL](), [databricks](), [Cybersecurity](), [Security & Privacy]() Tags: [count](), [cybersecurity](), [databricks](), [failed](), [false-positives](), [functions](), [login](), [partition](), [product](), [real-time](), [regex](), [sql](), [window-functions]() ### AI overview This tutorial explains how Databricks supports SQL MATCH_RECOGNIZE for detecting ordered event patterns. It shows how the clause can simplify sequence detection, including identifying repeated login failures followed by a successful login, compared with complex SQL queries and window functions. ### Source excerpt Imagine you work in cybersecurity and you have a table that tracks login attempts... ## Shared Selective Persistent Memory for Agentic LLM Systems DevFeed: [Shared Selective Persistent Memory for Agentic LLM Systems]() Original publisher: [Read original article]() Published: 2026-09-16T00:00:00Z Content type: article Language: en Sources: [Apple Machine Learning Research]() Topics: [Large Language Model](), [agent observability](), [Framework](), [Code](), [configuration](), [Access Control](), [Git](), [Model Context Protocol (MCP)](), [CSV](), [SQL](), [Grafana]() Tags: [access-control](), [agentic](), [agents](), [code](), [configuration](), [csv](), [dashboards](), [git](), [llm](), [mcp](), [memory](), [platform](), [replication](), [sql]() ### AI overview This research introduces shared selective persistent memory for agentic LLM systems. The architecture retains reusable task specifications, data schemas, tool configurations, and output constraints while discarding session-specific reasoning traces. Shared workspaces support role-based collaborative reuse, and experiments report higher task completion than no memory or full-history persistence, along with zero-token data refresh and lower token costs. ### Source excerpt Agentic LLM systems that generate code through multi-turn tool use face a fundamental context problem: each session starts from zero, discarding the configuration choices, domain constraints, data schemas, and tool-use patterns that made previous sessions productive. Naively persisting entire conversation histories is both token-inefficient and counterproductive--irrelevant context degrades generation quality. We introduce shared selective persistent memory, a memory architecture for agentic systems that identifies and retains four categories of reusable context--task specifications, data... ## Plan Advice in PostgreSQL 19 DevFeed: [Plan Advice in PostgreSQL 19]() Original publisher: [Read original article]() Author: Dimitri Fontaine PostgreSQL Major Contributor; Author Published: 2026-09-15T16:27:53Z Content type: tutorial Language: en Sources: [Dimitri Fontaine]() Topics: [PostgreSQL](), [version](), [SQL](), [Docker Compose]() Tags: [analyze](), [beta](), [docker-compose](), [pg-plan-advice](), [postgresql](), [sql]() ### AI overview This tutorial explains PostgreSQL 19's pg_plan_advice and pg_stash_advice modules, which represent query-plan decisions as reusable advice and can apply that advice by query ID. It also describes reconstructing comparable plan advice from ordinary plan output on earlier PostgreSQL versions. ### Source excerpt There is a conversation that happens in every PostgreSQL shop eventually. A query that has been fine for a year gets slow overnight. Nothing was deployed. The data grew a little, ANALYZE ran, and the planner -- entirely reasonably, on the numbers it had -- picked a different plan. The old plan was better. You would like it back. PostgreSQL 19 ships two new modules for exactly this: pg_plan_advice, which can read a plan back out as a string and enforce it later, and pg_stash_advice, which keeps those strings keyed by query id and applies them automatically. ▸ Every query below ran against the Lab, the free dataset bundle used throughout this blog, on PostgreSQL 19 Beta 3: POSTGRES_VERSION=19beta3 PG_MAJOR=19 docker compose up. Both modules are contrib, and the Lab image ships them; nothing below runs a LOAD to enable them, because the server already has them -- pg_plan_advice in session_preload_libraries, pg_stash_advice in shared_preload_libraries (it can survive a restart, which needs loading that way). One line each in postgresql.conf, or the equivalent server-start flag, and you're done. pg_stash_advice still needs its own CREATE EXTENSION, further down, for its SQL functions -- that's independent of how the module itself got loaded. ## PostgreSQL 19 graph queries fail the 'would you ship this?' test DevFeed: [PostgreSQL 19 graph queries fail the 'would you ship this?' test]() Original publisher: [Read original article]() Author: Lindsay Clark Published: 2026-09-15T09:42:59Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [PostgreSQL](), [SQL](), [Graphs](), [Databases]() Tags: [bugs](), [concurrent](), [databases](), [graph](), [postgresql](), [sql]() ### AI overview The article reports that PostgreSQL 19's SQL/PGQ graph queries were rejected because of unresolved bugs. It also discusses concurrent REPACK as a way to reduce overnight maintenance calls for database administrators. ### Source excerpt SQL/PGQ gets bounced over unresolved bugs as concurrent REPACK promises fewer midnight calls for DBAs ## Percona and HexaCluster: Faster, Safer Oracle Migration DevFeed: [Percona and HexaCluster: Faster, Safer Oracle Migration]() Original publisher: [Read original article]() Author: Percona Team Published: 2026-09-14T22:19:16Z Content type: article Language: en Sources: [Blog - Percona]() Topics: [Oracle Database](), [migration](), [Open Source](), [PostgreSQL](), [Database Migration](), [Replication](), [Tooling]() Tags: [database](), [database-migration](), [database-trends](), [engineering](), [insight-for-dbas](), [insight-for-developers](), [migration](), [open-source](), [oracle](), [partners](), [percona](), [postgresql](), [replication](), [schema](), [sql](), [tooling](), [validation]() ### AI overview Percona and HexaCluster describe a partnership for moving organizations from Oracle, SQL Server, DB2, and Sybase ASE to supported open source databases, primarily PostgreSQL. HexaCluster provides assessment and migration engineering through DMAT and HexaRocket, while Percona provides distributions, operators, and long-term support. HexaRocket combines schema migration, data migration, validation, change data capture, live replication, and reverse replication in one platform. ### Source excerpt Percona and HexaCluster have partnered to remove the hardest part of an open source database migration: getting off Oracle, SQL Server, DB2 or Sybase ASE with confidence, on a predictable timeline, without a multi-year consulting program. Percona brings open source expertise, its own distributions, operators and enterprise support. HexaCluster brings the assessment and migration engineering ... Continued The post Percona and HexaCluster: Faster, Safer Oracle Migration appeared first on Percona. ## How to Design Gifting Features People Actually Use: Evidence from 58 Apps DevFeed: [How to Design Gifting Features People Actually Use: Evidence from 58 Apps]() Original publisher: [Read original article]() Author: Anamol Rajbhandari Published: 2026-09-14T13:58:38Z Content type: article Language: en Sources: [freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More]() Topics: [App](), [SQL]() Tags: [apps](), [article](), [design](), [developer](), [ecommerce](), [research](), [sql](), [ux]() ### AI overview The article examines whether gifting features are worth building into consumer apps. Drawing on research across 58 apps, it identifies gifting scenarios, the triggers that prompt people to send gifts, and the costs and design work involved in implementing them. ### Source excerpt American shoppers spent about $29 billion on gift cards over the 2025 holiday season, and 43 percent of them bought at least one. This put gift cards at the top of what people said they wanted accordi ## Article: Implementing Durable Workflows on Postgres Without an External Orchestrator DevFeed: [Article: Implementing Durable Workflows on Postgres Without an External Orchestrator]() Original publisher: [Read original article]() Author: Raman Varma Published: 2026-09-14T11:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [Databases](), [SQL](), [Provisioning](), [incident](), [CI/CD](), [Kubernetes](), [AWS Step Functions](), [Artificial Intelligence]() Tags: [ai](), [ai-ml-data-engineering](), [architecture-design](), [article](), [automation](), [aws-step-functions](), [database](), [durable-workflows-postgres](), [incident-response](), [infrastructure](), [kubernetes](), [ml-data-engineering](), [orchestration](), [postgres](), [queue](), [relational-databases](), [sql](), [workflows]() ### AI overview This article explains how to implement durable workflows on Postgres without an external orchestrator. It describes using row-level locking as a concurrent work queue, primary-key checkpoints for idempotency, and leases with a sweeper for crash recovery. Workflow state, sleeps, and human approvals can persist in the database and survive process restarts. ### Source excerpt Postgres can serve as the durable state store and coordination layer for workflows, eliminating the need for an external orchestrator. SKIP LOCKED enables concurrent work processing, primary-key checkpoints enforce idempotency, and leases support crash recovery. Workflow sleeps and human approvals can also be persisted as database state and survive restarts. By Raman Varma ## Unmasking Cloud Identities: From Behavioral Clustering to Automated Detection DevFeed: [Unmasking Cloud Identities: From Behavioral Clustering to Automated Detection]() Original publisher: [Read original article]() Author: Osher Jacob Published: 2026-09-14T10:00:01Z Content type: article Language: en Sources: [Unit 42]() Topics: [AWS CloudTrail](), [Cloud](), [threat detection](), [Threat Research](), [SIEM, Security, Observability](), [Machine learning](), [Algorithms](), [SQL](), [IAM](), [identity and access management](), [Amazon Web Services]() Tags: [algorithms](), [amazon-web-services-aws](), [analysis](), [aws-cloudtrail](), [cloud](), [cloud-cybersecurity-research](), [cloud-detection](), [devops](), [iam](), [identity-and-access-management](), [logs](), [machine-learning](), [post](), [sql](), [threat-detection](), [threat-research]() ### AI overview This article presents a behavioral clustering model for mapping cloud identities to functional roles using activity patterns from audit logs. It applies unsupervised machine learning with UMAP and HDBSCAN to data from more than 40,000 identities across 125 cloud environments, and shows how the resulting map can support automated threat detection. The article also explains how lightweight heuristics extracted from the map can classify identities at scale using standard SQL, reducing the need for continuous resource-intensive machine learning pipelines. ### Source excerpt We designed a behavioral clustering model to map cloud identity roles from audit logs, enabling continuous threat detection using standard SQL queries. The post Unmasking Cloud Identities: From Behavioral Clustering to Automated Detection appeared first on Unit 42. ## 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. ## Grafana 13.2 release: easier ways to query and explore your data DevFeed: [Grafana 13.2 release: easier ways to query and explore your data]() Original publisher: [Read original article]() Author: Grafana Labs Team Published: 2026-09-12T11:22:06.456390Z Content type: release Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [Grafana](), [dashboards]() Tags: [analytics](), [dashboards](), [enterprise](), [explore](), [grafana](), [grafana-cloud](), [logs](), [metrics](), [monitoring](), [observability](), [release](), [sql](), [traces]() ### AI overview Grafana 13.2 introduces generally available saved queries for Grafana Cloud and Grafana Enterprise, letting organizations store, discover, and reuse vetted queries across dashboards, Explore, and annotation queries. The release also highlights a new View panel sidebar for exploring busy panels. ### Source excerpt Grafana 13.2 is here, bringing more improvements to help you and your team explore your data and get to insights faster. In this post, we'll highlight the latest updates to saved queries, a feature that lets teams share, discover, and reuse queries to get to trusted answers faster and help new teammates get up to speed. We'll also explore how the new View panel sidebar makes exploring busy panels a breeze. If you want to read about all the latest updates in Grafana 13.2, please refer to the changelog or our What's New documentation. Saved queries: reuse trusted queries across dashboards and teams Good queries are hard-won. Writing one means knowing both the query language and your own data, like which of four similarly named metrics is the one you can trust. That knowledge usually sits with a few experienced people, or is gradually learned through exploration (increasingly AI-assisted), validation, and revision. Often teams end up rebuilding the same Grafana queries over and over, and the best ones live in pinned Slack messages or get copy-pasted from old dashboards. New team members feel it most, since their first weeks are often spent reverse-engineering existing dashboards just to work out how to ask a question of their own. The query history in Grafana Explore helps, keeping a couple of weeks of your own queries and letting you "star" the keepers. It's private to you, though. Until recently, there hasn't been a built-in way to take a query you trust and put it somewhere your whole organization can find it. How teams use saved queries We built saved queries, which is now generally available in Grafana Cloud and Grafana Enterprise, to address this challenge by providing a shared query library for your organization. When you write a query worth keeping, you can save it with a title, description, and tags. Saving works from dashboard panels, Explore, and annotation queries. This means teammates who don't know PromQL or SQL can still build dashboards from queries tha ## Run DuckDB analytics on your Amazon DynamoDB data with zero-ETL DevFeed: [Run DuckDB analytics on your Amazon DynamoDB data with zero-ETL]() Original publisher: [Read original article]() Author: Lee Hannigan Published: 2026-09-11T14:53:57Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [Databases](), [Amazon S3](), [Apache Iceberg](), [AWS Lambda](), [AWS IAM](), [Latency]() Tags: [advanced-300](), [amazon-dynamodb](), [amazon-s3-tables](), [analytics](), [apache-iceberg](), [aws-lambda](), [duckdb](), [how-to](), [iam](), [integration](), [sql](), [technical-how-to]() ### AI overview This tutorial explains how to run ad hoc SQL analytics on Amazon DynamoDB data with DuckDB through a zero-ETL replication flow. ### Source excerpt Run ad hoc SQL analytics on your Amazon DynamoDB data with DuckDB. A zero-ETL integration replicates your table into Apache Iceberg tables on Amazon S3 Tables, and an AWS Lambda function running DuckDB serves SQL queries through an IAM-authorized function URL. ## AI Functions in ClickHouse: Upgrade your SQL to the AI age DevFeed: [AI Functions in ClickHouse: Upgrade your SQL to the AI age]() Original publisher: [Read original article]() Author: Andriy Yakovlev; George Larionov Published: 2026-09-11T12:49:32Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Large Language Model](), [AI search](), [Retrieval Augmented Generation (RAG)](), [Databases]() Tags: [ai](), [classification](), [clickhouse](), [embeddings](), [llm](), [rag](), [sql]() ### AI overview ClickHouse introduces beta AI Functions that invoke LLM and embedding providers directly from SQL for tasks including classification, extraction, generation, translation, filtering, redaction, embeddings, and semantic similarity. ### Source excerpt Explore ClickHouse AI Functions for classification, generation, translation, embeddings, semantic search, and cost controls--all directly from SQL. ## Azure SQL Data Sync stops taking newcomers before 2027 execution DevFeed: [Azure SQL Data Sync stops taking newcomers before 2027 execution]() Original publisher: [Read original article]() Author: Richard Speed Published: 2026-09-10T14:47:00Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [data]() Tags: [azure](), [data](), [databases](), [microsoft](), [sql](), [sync]() ### AI overview Azure SQL Data Sync will stop accepting new customers before its 2027 retirement. Existing customers can continue for now, with no like-for-like Microsoft successor. ### Source excerpt Existing customers can carry on for now, but Microsoft offers no like-for-like successor ## Claimable Neon: Provisioned by agents, claimed by humans DevFeed: [Claimable Neon: Provisioned by agents, claimed by humans]() Original publisher: [Read original article]() Author: Andre Landgraf Published: 2026-09-10T12:00:00Z Content type: release Language: en Sources: [Blog -- Neon Docs]() Topics: [AI-assisted coding](), [AI Bots]() Tags: [agents](), [api](), [auth](), [backend](), [community](), [database](), [oauth](), [postgres](), [product](), [sql]() ### AI overview Claimable Neon lets agents anonymously provision a temporary Neon project, continue building with scoped credentials, and generate a link for a human to claim the project later. ### Source excerpt Claimable Neon implements the anonymous registration method in auth.md, the open agent registration protocol authored by WorkOS, to give agents a way to provision a temporary Neon project without creating an account or collecting payment details. ## The lifecycle of a sharded Postgres query DevFeed: [The lifecycle of a sharded Postgres query]() Original publisher: [Read original article]() Author: PlanetScale Published: 2026-09-10T00:00:00Z Content type: tutorial Language: en Sources: [Blog -- PlanetScale]() Topics: [Database](), [networking]() Tags: [architecture](), [auth](), [database](), [engineering](), [neki](), [postgres](), [routing](), [scale](), [server](), [sql]() ### AI overview An overview of how a SQL query moves through a sharded Postgres database, from authentication and routing to execution across shards. ### Source excerpt Follow a SQL query through the router, across four Postgres shards, and back. ## ClickHouse release 26.8 DevFeed: [ClickHouse release 26.8]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-09-10T00:00:00Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [parquet](), [data]() Tags: [bug](), [clickhouse](), [data](), [feature](), [parquet](), [performance](), [release](), [sql](), [tokenizers]() ### AI overview ClickHouse 26.8 is an LTS release that adds background queries, pipelined SQL, text tokenizers, and expanded data lake integrations, alongside performance improvements for Parquet, aggregations, and joins. ### Source excerpt ClickHouse 26.8 LTS introduces background queries, pipelined SQL, new text tokenizers, expanded data lake integrations, and faster Parquet, aggregation, and join queries. ## Loading Parquet data into MySQL with ClickHouse DevFeed: [Loading Parquet data into MySQL with ClickHouse]() Original publisher: [Read original article]() Author: Mark Needham Published: 2026-09-10T00:00:00Z Content type: tutorial Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [MySQL](), [parquet](), [Databases](), [Amazon S3]() Tags: [clickhouse](), [mysql](), [parquet](), [s3](), [sql]() ### AI overview A tutorial on using ClickHouse to load Parquet data into MySQL and query MySQL through ClickHouse table functions. ### Source excerpt Use ClickHouse to load Parquet files into MySQL, explore remote data, and run MySQL queries with table functions and named collections. ## Beyond embedding: How to secure AI/BI Dashboards for every viewer DevFeed: [Beyond embedding: How to secure AI/BI Dashboards for every viewer]() Original publisher: [Read original article]() Author: Sonakshi Pandey Published: 2026-09-09T14:04:44Z Content type: tutorial Language: en Sources: [Databricks]() Topics: [databricks](), [Authorization](), [Grafana](), [SDKs](), [Back end](), [data]() Tags: [authorization](), [backend](), [dashboards](), [databricks](), [embedded](), [guide](), [idp](), [mapping](), [platform](), [product](), [sdk](), [sql]() ### AI overview This guide presents a Databricks design pattern for securing embedded AI/BI Dashboards for different viewers. It uses scoped embed tokens, __aibi_external_value, Unity Catalog row filters and column masks, and identity-provider-synchronized groups so one dashboard can show each viewer only the authorized regions and fields. The same entitlement table governs embedded dashboard access and direct Databricks SQL queries. ### Source excerpt The challengeEmbedding a Databricks AI/BI Dashboard in a customer-facing application is relatively straightforward... ## Use Drizzle ORM with Appwrite Postgres DevFeed: [Use Drizzle ORM with Appwrite Postgres]() Original publisher: [Read original article]() Author: Atharva Deosthale Published: 2026-09-08T00:00:00Z Content type: tutorial Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [Drizzle](), [migration](), [Object-relational mapping](), [PostgreSQL](), [TypeScript](), [Databases](), [SQL]() Tags: [data](), [drizzle](), [migration](), [migrations](), [orm](), [postgres](), [postgresql](), [schema](), [sql](), [tutorials](), [typescript]() ### AI overview A tutorial showing how to connect Drizzle ORM to Appwrite Postgres, define tables and queries in TypeScript, generate and apply SQL migrations, seed data, and evolve a schema while preserving existing records. ### Source excerpt Connect Drizzle ORM to Appwrite Postgres, query data with TypeScript, and apply SQL migrations as your schema changes. ## Security Week 2637: взлом Dropbox через Lenovo ID DevFeed: [Security Week 2637: взлом Dropbox через Lenovo ID]() Original publisher: [Read original article]() Author: Kaspersky\_Lab ("Лаборатория Касперского") Published: 2026-09-07T20:01:45Z Content type: news Language: ru Sources: ["Лаборатория Касперского" RU]() Topics: [Security](), [dropbox](), [Google Chrome](), [V8](), [OpenAI](), [WordPress](), [migration]() Tags: [chrome](), [dropbox](), [google-chrome](), [id](), [lenovo](), [news](), [openai](), [security](), [sql](), [tag-9fe8963de219](), [v8](), [wordpress]() ### AI overview A weekly security roundup reports that several thousand Dropbox accounts were compromised through flawed Lenovo ID authorization. It also covers ValleyRAT spyware, a Google Chrome V8 vulnerability, OpenAI agents making changes outside a test environment, Plex updates, and a WordPress plugin SQL injection. ### Source excerpt Как стало известно на прошлой неделе, несколько тысяч учетных записей в сервисе Dropbox были взломаны в период с 4 по 21 августа. Взлом стал возможен из-за некорректной авторизации при использовании стороннего идентификатора Lenovo ID. Как выяснилось позднее, ошибка на стороне Lenovo позволяла зарегистрировать учетную запись на произвольный почтовый адрес, а потом использовать ее для доступа к учетке с тем же электронным адресом в Dropbox. Dropbox отреагировала на инцидент, принудительно разлогинив всех пользователей, использовавших Lenovo ID в качестве метода авторизации. Также было добавлено обязательное требование ввода пароля непосредственно для учетной записи Dropbox. Читать далее [Next page]()