# Databases Published articles for Databases. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Perplexity's AI agents helped build a database. They weren't allowed to run it. DevFeed: [Perplexity's AI agents helped build a database. They weren't allowed to run it.]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-16T21:51:15Z Content type: article Language: en Sources: [The New Stack]() Topics: [Database](), [DynamoDB](), [Rust](), [Artificial Intelligence](), [rocksdb](), [Amazon S3]() Tags: [agents](), [ai](), [ai-agents](), [ai-infrastructure](), [api](), [coding](), [coding-agents](), [database](), [databases](), [dynamodb](), [open-source](), [performance](), [perplexity](), [rocksdb](), [rust](), [s3]() ### AI overview Perplexity built CobbleDB, a Rust key-value store, after finding DynamoDB too costly and insufficiently controllable for its search workload. Coding agents helped develop it, but were not allowed to run it in production. Perplexity measured lower read latency and expects lower costs, with plans to open-source the database. ### Source excerpt Perplexity decided it was paying too much for DynamoDB and wasn't getting the control it wanted over read performance. So The post Perplexity's AI agents helped build a database. They weren't allowed to run it. appeared first on The New Stack. ## The User Write Lock: an Alternative to Transactions for Google App Engine DevFeed: [The User Write Lock: an Alternative to Transactions for Google App Engine]() Original publisher: [Read original article]() Author: Khan Academy Published: 2016-06-20T22:00:00Z Content type: tutorial Language: en Sources: [Khan Academy]() Topics: [Transactions](), [Databases](), [Google]() Tags: [caching](), [databases](), [engineering](), [google](), [infrastructure](), [news](), [processes](), [retry](), [transactions]() ### AI overview This developer article explains how transactions in Google App Engine's datastore provide atomicity for single-entity and multi-entity writes, but can introduce frozen datastore state, retries, idempotence requirements, and restrictions on datastore queries. It presents the User Write Lock as an alternative, although the supplied text ends before describing that alternative. ### Source excerpt By Craig Silverstein Transactions are the standard method in Google App Engine's datastore -- as in many databases ... Read more ## Implementing GitOps from Infrastructure to DB Operators to Unify Ops for Kubernetes Databases DevFeed: [Implementing GitOps from Infrastructure to DB Operators to Unify Ops for Kubernetes Databases]() Original publisher: [Read original article]() Author: Sucahyo Ardy Prasetiyo Published: 2026-09-16T08:57:10Z Content type: tutorial Language: en Sources: [SeveralNines]() Topics: [GitOps](), [Kubernetes](), [Terraform](), [argo-cd](), [ClusterControl](), [Databases](), [opentofu]() Tags: [argo-cd](), [clustercontrol](), [databases](), [gitops](), [hybrid-operations](), [infrastructure](), [kubernetes](), [opentofu](), [sovereign-dbaas](), [terraform]() ### AI overview This article describes a layered GitOps approach for Kubernetes database platforms: manage infrastructure with Terraform or OpenTofu, manage Kubernetes deployments with Argo CD, and standardize operations for operator-managed databases with ClusterControl. It explains how declarative configuration and drift reconciliation differ between Terraform and Argo CD, and compares infrastructure automation options including Atlantis and Flux tf-controller. ### Source excerpt Most platform teams already use GitOps for their Kubernetes apps. The config lives in Git, Argo CD applies it, and deployments are predictable. But look one layer down and things get messy. Infrastructure setup is often still done by hand, running Terraform locally or through scattered scripts. Database operations, especially for databases running through Kubernetes [...] The post Implementing GitOps from Infrastructure to DB Operators to Unify Ops for Kubernetes Databases appeared first on Severalnines. ## Turso Accident Protection Enables Database Restoration for Up to 5 Days DevFeed: [Turso Accident Protection Enables Database Restoration for Up to 5 Days]() Original publisher: [Read original article]() Author: Pedro Muniz Published: 2026-09-16T00:00:00Z Content type: release Language: en Sources: [Turso Blog]() Topics: [Turso](), [Databases](), [MCP Server]() Tags: [ai-agents](), [database](), [databases](), [feature](), [mcp](), [mcp-server](), [turso]() ### AI overview Turso has released Accident Protection, allowing databases deleted on a paid plan to be restored for up to five days at no additional cost. The feature is available through the UI and Turso MCP server. ### Source excerpt Agents delete things. Now you can undelete them. Restore databases for up to 5 days after deletion, on any paid plan, at no additional cost. ## Kubernetes 1.36 restores a lost guarantee for database backups DevFeed: [Kubernetes 1.36 restores a lost guarantee for database backups]() Original publisher: [Read original article]() Author: Shubham Pampattiwar Published: 2026-09-15T13:00:00Z Content type: news Language: en Sources: [Kubernetes Overview, News and Trends | The New Stack](), [The New Stack]() Topics: [Kubernetes](), [PostgreSQL](), [Databases]() Tags: [backups](), [cncf](), [consistency](), [database](), [databases](), [kubernetes](), [post-contributed](), [postgresql](), [snapshots](), [sponsor-cncf](), [sponsored-post-contributed](), [stateful](), [storage]() ### AI overview The article explains how Kubernetes 1.36 restores coordinated snapshots for applications whose state spans multiple volumes, addressing inconsistent database backups caused by taking individual PersistentVolumeClaim snapshots at different times. ### Source excerpt It's 2 a.m., and you're restoring a PostgreSQL cluster from last night's backup. Its data directory lives on one PersistentVolumeClaim The post Kubernetes 1.36 restores a lost guarantee for database backups appeared first on The New Stack. ## 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 ## AI DevFeed: [AI]() Original publisher: [Read original article]() Published: 2026-09-15T02:59:12.316196Z Content type: article Language: en Sources: [Cockroach Labs]() Topics: [Artificial Intelligence](), [AI Agent](), [Security](), [Google](), [Protocol (disambiguation)](), [Databases](), [CockroachDB]() Tags: [2026](), [agents](), [ai](), [cockroachdb](), [databases](), [google](), [security](), [vector-search]() ### AI overview An AI-focused Cockroach Labs blog category page listing articles about agent-to-agent communication, AI agent security, database isolation, compliance, resilience, identity, vector search, operations automation, and durable execution with CockroachDB. ### Source excerpt Blog ai A2A Is Now an Open Standard. The Data Layer Underneath It Isn't. In April 2025, Google released a protocol for agent-to-agent communication. Within three months, Google had donated it to the Linux Foundation... Quentin Packard August 19, 2026 Multi-Tenant AI Agents: Why Data Isolation Starts at the Database Most SaaS teams shipping agentic features focus on prompt safety and API-layer filtering. But effective AI agent security also depends on... ## Oracle celebrates banner quarter with another round of layoffs DevFeed: [Oracle celebrates banner quarter with another round of layoffs]() Original publisher: [Read original article]() Author: Brandon Vigliarolo Published: 2026-09-14T20:46:57Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [Cloud](), [Artificial Intelligence]() Tags: [ai](), [ai-and-ml](), [cloud](), [databases](), [layoff](), [oracle]() ### AI overview Oracle celebrates a strong quarter while announcing another round of layoffs, described as connected to the growth of its AI cloud business. ### Source excerpt Congratulations on helping Larry Ellison's AI cloud boom. Now please pack your bags and go ## Unify your marketing data with Lakeflow Connect DevFeed: [Unify your marketing data with Lakeflow Connect]() Original publisher: [Read original article]() Author: Sonia Bendre; Giselle Goicochea Published: 2026-09-11T18:00:00Z Content type: article Language: en Sources: [Databricks]() Topics: [databricks](), [Software as a service](), [Databases](), [API](), [Automation](), [dashboards](), [hubspot](), [CI/CD]() Tags: [analytics](), [api](), [automation](), [churn](), [ci-cd](), [connectors](), [crm](), [customer](), [customers](), [dashboards](), [data](), [databases](), [databricks](), [engineering](), [marketing](), [product](), [retention](), [saas](), [salesforce](), [series]() ### AI overview This first post in a series presents Lakeflow Connect as a fully managed data-ingestion service for unifying fragmented marketing and Customer 360 data. It describes native connectors for SaaS applications, databases, and files, configured through a point-and-click UI or API, with governed tables in Unity Catalog and integrations such as HubSpot and Salesforce. The article highlights reduced maintenance compared with custom pipelines and support for downstream reporting, analytics, retention, and churn analysis. ### Source excerpt This is the first post in a new series exploring how Lakeflow Connect brings fully... ## Presentation: How To Run on Three Clouds at Once, and When Not To DevFeed: [Presentation: How To Run on Three Clouds at Once, and When Not To]() Original publisher: [Read original article]() Author: Ross McFarlane, Kevin Holditch Published: 2026-09-11T11:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [networking](), [Databases](), [Kubernetes]() Tags: [amazon](), [architecture](), [architecture-design](), [availability](), [aws](), [cloud](), [cloud-architecture](), [cloud-computing](), [cloud-networking](), [cockroach-labs](), [cockroachdb](), [containers](), [databases](), [devops](), [disaster-recovery](), [engineering](), [europe](), [financial-applications](), [fintech](), [form3-multicloud-architecture](), [infoq](), [kubernetes](), [microservices](), [networking](), [presentation](), [qcon-london-2026](), [qcon-software-development-conference](), [resilience](), [transcripts](), [us]() ### AI overview A presentation on Form3's move from one cloud to a triple-active multi-cloud architecture, covering cross-cloud networking, distributed databases, Kubernetes operators, and regional disaster-recovery expectations. ### Source excerpt Ross McFarlane and Kevin Holditch discuss Form3's evolution from a single-cloud setup to a triple active multi-cloud architecture. They share key engineering strategies for cross-cloud networking, distributed databases with CockroachDB and NATS, custom Kubernetes operators, and navigating distinct regional disaster recovery expectations across the UK, Europe, and US financial markets. By Ross McFarlane, Kevin Holditch ## 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 ## v0 adds one-click integrations for email, auth, search, and databases DevFeed: [v0 adds one-click integrations for email, auth, search, and databases]() Original publisher: [Read original article]() Author: Jathin Singaraju Published: 2026-09-09T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [AI Chat]() Tags: [agent-skills](), [auth](), [code](), [databases](), [integration](), [react](), [search](), [skills](), [vercel]() ### AI overview Vercel's v0 adds inline Marketplace integrations for providers including Resend, Amazon OpenSearch, MongoDB Atlas, Algolia, and Clerk. Connected providers can be configured automatically, and provider skills can guide generated code. ### Source excerpt We're working toward bringing parity across Vercel integration and v0, starting with Resend, Amazon OpenSearch, MongoDB Atlas, Algolia and Clerk. Prompt v0 with what you want to build, and when your prompt requires a provider, v0 renders a connect card in the chat. With Marketplace integrations in v0, you get: Connect as you build: Prompt v0 with what you want to create, and connect the required provider inline. Automatic setup: Once connected, v0 handles the required environment variables and configuration. Provider skills loaded automatically: When you connect a provider that publishes agent skills, v0 loads them and generates code that follows the provider's recommended patterns. Provider-specific capabilities: Resend, for example, adds transactional email with React Email components. To get started, open a v0 chat and prompt what you want to add to your stack. Read more ## How MCP Toolbox turns agent text into ClickHouse vectors DevFeed: [How MCP Toolbox turns agent text into ClickHouse vectors]() Original publisher: [Read original article]() Author: Pete Hampton Published: 2026-09-07T09:00:00Z Content type: tutorial Language: en Sources: [ClickHouse Blog]() Topics: [MSP MCP]() Tags: [ai-agent](), [clickhouse](), [databases](), [embedding](), [gemini](), [go](), [google](), [how-to](), [mcp](), [search](), [sql](), [vector]() ### AI overview A tutorial for configuring Google's MCP Toolbox with ClickHouse to embed text during inserts and searches, then return cosine-ranked results without a separate embedding service. ### Source excerpt Google's MCP Toolbox for Databases embeds agent text into vectors on insert and search, then lets ClickHouse rank the results - no embedding service to build or maintain. Here's how to set it up, and what it looks like end to end. ## Turn your app into an MCP server with the Appwrite OAuth2 server DevFeed: [Turn your app into an MCP server with the Appwrite OAuth2 server]() Original publisher: [Read original article]() Author: Atharva Deosthale Published: 2026-09-04T00:00:00Z Content type: tutorial Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [Model Context Protocol](), [OAuth 2.0](), [Authorization](), [Claude Code](), [Serverless](), [web applications](), [Databases]() Tags: [ai](), [ai-tools](), [app](), [auth](), [authorization](), [browser](), [claude](), [claude-code](), [code](), [databases](), [http](), [mcp](), [mcp-server](), [oauth2](), [remote-mcp-server](), [serverless](), [tutorial](), [tutorials](), [web](), [web-app]() ### AI overview This tutorial explains how to turn an Appwrite-based application into a remote MCP server. It uses an Appwrite Function, the OAuth2 server included in an Appwrite project, and a consent screen so AI tools such as Claude Code can access and act on behalf of signed-in users. ### Source excerpt Build a remote MCP server for your product, host it on Appwrite Functions, and let AI tools like Claude Code sign in through the OAuth2 server built into your Appwrite project. ## How Databases Keep Their Sanity with Concurrency Control DevFeed: [How Databases Keep Their Sanity with Concurrency Control]() Original publisher: [Read original article]() Author: ByteByteGo Published: 2026-09-03T15:31:17Z Content type: article Language: en Sources: [ByteByteGo]() Topics: [Databases](), [Concurrency]() Tags: [article](), [concurrency](), [concurrency-control](), [databases]() ### AI overview The article introduces how databases handle bugs related to concurrency control, but the supplied evidence does not provide the specific methods or findings discussed. ### Source excerpt So how do we handle such bugs? This is what we are going to try to answer in this article. ## Why Output Metrics Can Miss Problems in Complex Production Systems DevFeed: [Why Output Metrics Can Miss Problems in Complex Production Systems]() Original publisher: [Read original article]() Author: Remy Porter Published: 2026-09-02T06:30:00Z Content type: opinion Language: en Sources: [The Daily WTF]() Topics: [Monitoring](), [dashboards](), [robot sense of touch](), [Embedded Software Dev](), [Databases](), [Computer vision]() Tags: [algorithm](), [computer-vision](), [databases](), [embedded](), [feature-articles](), [manufacturing](), [metrics](), [monitoring](), [robotics](), [widget]() ### AI overview This commentary examines a metrics-driven manufacturing team whose automated production line combines robotics, embedded firmware, web-based monitoring tools, and PLC code. It argues that tracking output and limited performance metrics does not adequately explain how such a complex system behaves or why bottlenecks occur. ### Source excerpt Rachel joined a new team which was proudly "metrics driven". When she first met with her boss, Zane, he explained his thinking. "We need to be data-driven to make good decisions, right? We're a manufacturing company. We make widgets. At the end of the day, we need to make the most widgets for the lowest cost of goods sold. So we track that, and that feeds into every decision." The team oversaw an automated production line, which meant the software was a mix of robotics, embedded firmware, high-level web based monitoring tools, and thickets of dreaded PLC code. And because you can't build an entire factory for test purposes, they only way they could test real-world scales with real-world data was to roll changes out to production. They could simulate, they could run tests on subsets of the system, but a change in the production line software couldn't truly be validated until it rolled out into the real world. Rachel's first task on the new team involved making some changes to their metrics dashboard. It was viewed as a good way to get her feet wet with the new team. As it turned out, the metrics dashboard was a Google Sheet, with a complex series of formulas that involved multi-level INDEX functions- essentially querying the spreadsheets like they were a database. Why not use an actual database? Oh, they did -- six actually -- but the company obeyed Remy's Law of Requirements Gathering: "no matter what the requirements the users ask for, what they really wanted was Excel". The database data was pulled into the spreadsheet for reporting. Now, a complicated sheet pulling in data from not one, but six different databases, they must have a pretty complex model to explain how changes to their software would impact productivity. And since they needed to model the software to make predictions about how it'd behave in production, that model must be extremely useful. Of course it wasn't. The only metrics they tracked were output metrics, variations on "widgets produced per unit ## Announcing VectorsDB: Similarity search as a first-class Appwrite database DevFeed: [Announcing VectorsDB: Similarity search as a first-class Appwrite database]() Original publisher: [Read original article]() Author: Arnab Chatterjee Published: 2026-09-02T00:00:00Z Content type: release Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [AI search](), [Databases](), [Embeddings]() Tags: [ai](), [announcements](), [databases](), [embeddings](), [product](), [search](), [vector]() ### AI overview Appwrite announces VectorsDB, a first-class database for storing and searching vectors. It supports built-in text embedding generation, externally generated embeddings, similarity ranking, metadata, permissions, queries, pagination, ordering, transactions, bulk operations, and CSV import and export. ### Source excerpt Store embeddings, generate them from text with built-in models, and rank documents by similarity without adding a separate vector service to your stack. ## Announcing native MySQL databases: Bring your MySQL workloads to Appwrite DevFeed: [Announcing native MySQL databases: Bring your MySQL workloads to Appwrite]() Original publisher: [Read original article]() Author: Jake Barnby Published: 2026-09-02T00:00:00Z Content type: release Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [MySQL](), [Databases](), [Provisioning](), [Availability](), [Replication](), [autoscaling](), [TLS (Transport Layer Security)]() Tags: [announcements](), [autoscaling](), [availability](), [database](), [databases](), [mysql](), [provisioning](), [replication](), [tls]() ### AI overview Appwrite announces native managed MySQL databases that can run inside the same project as an application's existing backend. The service supports MySQL 8.4 and 8.0, standard drivers and ORMs, TLS, backups and point-in-time recovery, replicas with automatic failover, database branches, scaling, and connection pooling. ### Source excerpt Provision a managed MySQL 8.4 instance inside your Appwrite project and keep using the drivers, ORMs, and tools your applications already depend on. ## Four things you can build with Appwrite VectorsDB DevFeed: [Four things you can build with Appwrite VectorsDB]() Original publisher: [Read original article]() Author: Atharva Deosthale Published: 2026-09-02T00:00:00Z Content type: tutorial Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [AI search](), [Databases]() Tags: [ai](), [cache](), [database](), [databases](), [embedding](), [history](), [search](), [vector-search]() ### AI overview This tutorial explains how Appwrite VectorsDB uses vector embeddings to find documents by meaning rather than exact keywords. It demonstrates help-article search, filtered search, recommendations from user history, and an answer cache, including a 256-number vector approach used in the examples. ### Source excerpt Appwrite VectorsDB finds documents by meaning, not by words. This post builds four features with it: a help article search, a filtered search, a recommendation from user history, and an answer cache. ## Native databases vs Appwrite databases: which one should you pick? DevFeed: [Native databases vs Appwrite databases: which one should you pick?]() Original publisher: [Read original article]() Author: Atharva Deosthale Published: 2026-09-01T00:00:00Z Content type: tutorial Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [Databases](), [MySQL](), [PostgreSQL](), [SDKs]() Tags: [api](), [backend](), [comparisons](), [databases](), [guide](), [mysql](), [orm](), [postgresql](), [realtime](), [transactions]() ### AI overview This guide compares Appwrite's managed TablesDB, DocumentsDB, and VectorsDB with native PostgreSQL and MySQL databases. It explains the trade-off between Appwrite's SDK-based permissions, realtime events, functions, and other platform features and the direct SQL access available through native database drivers or ORMs. ### Source excerpt Appwrite offers two kinds of databases. This guide explains how TablesDB, DocumentsDB, and VectorsDB differ from native PostgreSQL and MySQL, and how to choose between them for your project. ## Amazon EC2 R9g and R9gd instances powered by AWS Graviton5 processors are now generally available DevFeed: [Amazon EC2 R9g and R9gd instances powered by AWS Graviton5 processors are now generally available]() Original publisher: [Read original article]() Author: Daniel Abib Published: 2026-08-31T19:53:34Z Content type: release Language: en Sources: [AWS News Blog]() Topics: [Amazon EC2](), [cpu](), [networking]() Tags: [amazon-ec2](), [announcements](), [aws](), [big-data](), [cache](), [compute](), [databases](), [graviton](), [hardware](), [launch](), [memory](), [networking](), [news](), [nitro-isolation-engine](), [nitro-system](), [performance](), [storage]() ### AI overview Amazon EC2 R9g and R9gd memory-optimized instances powered by AWS Graviton5 processors are generally available. The release highlights higher compute performance, faster memory, larger cache, increased network and EBS bandwidth, and local NVMe SSD storage for R9gd. ### Source excerpt Amazon EC2 R9g and R9gd instances powered by AWS Graviton5 are now generally available, delivering up to 25% better compute performance than R8g, ideal for databases, in-memory caches, and real-time analytics. ## Tailscale PAM beta: Manage connectivity and privileged access in one place DevFeed: [Tailscale PAM beta: Manage connectivity and privileged access in one place]() Original publisher: [Read original article]() Author: Smriti Sharma Published: 2026-08-27T14:00:00Z Content type: release Language: en Sources: [Blog on Tailscale]() Topics: [Authorization](), [Security](), [Kubernetes](), [OpenSSH](), [API](), [Databases](), [web applications](), [incident]() Tags: [api](), [compliance](), [databases](), [external](), [identity](), [incident](), [infrastructure](), [kubernetes](), [logs](), [production](), [security](), [servers](), [slack](), [ssh](), [web-applications]() ### AI overview Tailscale PAM beta brings privileged access management into the Tailscale admin console. It provides resource-specific access policies, just-in-time approvals through Slack, and session logs and recordings for audits and investigations across databases, servers, Kubernetes clusters, and web applications. ### Source excerpt Just-in-time access, resource policies, and session auditing, right where you need them. ## Benchmarking vector indexes DevFeed: [Benchmarking vector indexes]() Original publisher: [Read original article]() Author: Evgeniy Patlan Published: 2026-08-27T13:35:32Z Content type: article Language: en Sources: [Blog - Percona]() Topics: [benchmarking](), [Ground truth / benchmark quality](), [Databases](), [ann](), [datasets](), [Sorting]() Tags: [ai-vector](), [ann](), [benchmark](), [benchmarking](), [benchmarks](), [blog](), [blog-post](), [data](), [database-performance](), [database-trends](), [databases](), [datasets](), [embeddings](), [index](), [measurements](), [model](), [open-source](), [percona](), [points](), [report](), [run](), [search](), [sorting](), [vector-search](), [vectorsearch]() ### AI overview This article explains how Percona's vector-bench measures vector indexes by building database engines from pinned versions, running them in identical containers with the same data and hardware, and comparing consistent measurements. It describes embeddings, exact search, approximate nearest neighbour search, and ground truth as the brute-force reference needed to evaluate index accuracy. ### Source excerpt Nearly every database has vector search now, and every one of them has a blog post with a big number in it. Almost none of those numbers can be checked, because the thing that makes them meaningful is usually missing. We built a vector-bench to stop guessing. You name the engines you want, build them ... Continued The post Benchmarking vector indexes appeared first on Percona. ## DuckDB and the changing physics of analytics DevFeed: [DuckDB and the changing physics of analytics]() Original publisher: [Read original article]() Author: werner@allthingsdistributed.com (Dr. Werner Vogels) Published: 2026-08-26T14:00:00Z Content type: opinion Language: en Sources: [All Things Distributed]() Topics: [DuckDB](), [data](), [Databases](), [Amazon S3](), [Amazon Web Services](), [Computer science]() Tags: [analytics](), [aws](), [data](), [databases](), [duckdb](), [posts](), [s3](), [software](), [systems]() ### AI overview The article describes how changing relative costs for compute, memory, and networking are enabling more data processing to remain within applications. It presents DuckDB as a database supporting this shift and discusses its relationship to AWS data offerings, including S3 Files, S3 Tables, and S3 Vectors, alongside DuckLabs joining AWS. ### Source excerpt In this post, Andy Warfield explains how databases like DuckDB are enabling a new way to build with data, why they matter right now, and how they complement the work we've been doing in S3 (e.g., S3 Files, S3 Tables, S3 Vectors). And most importantly, why DuckLabs, the team behind DuckDB, is joining AWS [Next page]()