# Amazon S3 Cloud object storage service for storing and retrieving data in Amazon Web Services. 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. ## Appwrite Init 2026 recap: Everything we shipped DevFeed: [Appwrite Init 2026 recap: Everything we shipped]() Original publisher: [Read original article]() Author: Aishwari Pahwa Published: 2026-09-15T00:00:00Z Content type: release Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [releases](), [PostgreSQL](), [Amazon S3](), [Firewall](), [OAuth 2.0](), [Self-hosted]() Tags: [2](), [firewall](), [init](), [oauth2](), [postgresql](), [recap](), [release](), [releases](), [s3](), [self-hosted]() ### AI overview An Appwrite Init 2026 recap covering five days of launches, including Appwrite 2.0, a redesigned Console, native PostgreSQL, VectorsDB, DocumentsDB, MySQL, S3-compatible Storage, Firewall, OAuth2, and Domains. ### Source excerpt An Appwrite Init 2026 recap of all five days of launches, from Appwrite 2.0 and native PostgreSQL to VectorsDB, S3 support, Firewall, OAuth2, and Domains. ## Announcing On-Demand State Repartitioning for Apache Spark™ Structured Streaming on Databricks DevFeed: [Announcing On-Demand State Repartitioning for Apache Spark™ Structured Streaming on Databricks]() Original publisher: [Read original article]() Author: Thangam Vaiyapuri; Jay Palaniappan; B. Micheal Okutubo; Zifei Feng Published: 2026-09-14T21:04:30Z Content type: release Language: en Sources: [Databricks]() Topics: [Streaming](), [databricks](), [real-time](), [Amazon S3](), [API]() Tags: [amazon-s3](), [api](), [data-engineering](), [databricks](), [engineering](), [fraud-detection](), [infrastructure](), [monitoring](), [net-11-preview-7](), [production](), [real-time](), [streaming]() ### AI overview Databricks announces on-demand state repartitioning for Apache Spark Structured Streaming in Public Preview, available in Databricks Runtime 18 and later. The capability lets production stateful streaming queries resize their partition count while preserving checkpoint state, supporting workloads such as aggregations, stream-stream joins, deduplication, sessionization, and transformWithState. Coveo reports reducing related Amazon S3 API costs by 40%. ### Source excerpt Anyone running stateful Apache Spark™ Structured Streaming queries in production... ## Automate replenishment with MMF, Databricks Genie, and Amazon Quick DevFeed: [Automate replenishment with MMF, Databricks Genie, and Amazon Quick]() Original publisher: [Read original article]() Author: Venkatavaradhan Viswanathan Published: 2026-09-14T15:42:06Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [databricks](), [Amazon S3 Tables](), [Amazon S3](), [Automation](), [foundation-models](), [API]() Tags: [advanced-300](), [amazon-quick-suite](), [amazon-s3](), [amazon-s3-tables](), [api](), [automation](), [databricks](), [foundation-models](), [machine-learning](), [retail](), [technical-how-to]() ### AI overview This technical walkthrough presents an unattended replenishment workflow for retail. Databricks Many Model Forecasting uses Chronos-2 to predict seven-day demand for each SKU, Databricks Genie detects demand surges, and Amazon Quick reconciles those surges with supplier availability in Amazon S3 Tables. The workflow places routine purchase orders through a Supplier Order API and escalates cases without a suitable single supplier for human review. ### Source excerpt Foundation models made catalog-wide demand forecasting easy; the hard part is now acting on the forecast. This post builds a closed detect-decide-act loop on Databricks and Amazon Quick that reconciles demand surges against live supplier availability and places replenishment orders unattended, escalating to a human only when no supplier can cover a surge. ## Appwrite 2.1 is now available for self-hosting DevFeed: [Appwrite 2.1 is now available for self-hosting]() Original publisher: [Read original article]() Author: Atharva Deosthale Published: 2026-09-14T00:00:00Z Content type: article Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [Amazon S3](), [hosting](), [API](), [API keys](), [Command-line interface](), [SDKs](), [Compression](), [Encryption]() Tags: [api](), [api-documentation](), [aws](), [cli](), [compression](), [encryption](), [hosting](), [products](), [s3](), [self-hosting](), [sign-in]() ### AI overview Appwrite 2.1 is available for self-hosted deployments. It adds the S3-compatible Storage API and AutoGravity image previews, along with TikTok and Kakao sign-in and Appwrite Console fixes. The release uses the existing upgrade command and migration process from Appwrite 2.0. ### Source excerpt Appwrite 2.1 brings the S3 API and AutoGravity to self-hosted instances, adds TikTok and Kakao sign-in, and fixes for Appwrite Console. ## 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. ## Netflix Reworks Conductor for 420 Million Monthly Workflow Executions and 10X Larger Workflows DevFeed: [Netflix Reworks Conductor for 420 Million Monthly Workflow Executions and 10X Larger Workflows]() Original publisher: [Read original article]() Author: Leela Kumili Published: 2026-09-11T14:17:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Orchestration](), [Amazon S3](), [Kafka](), [Apache Iceberg]() Tags: [amazon-s3](), [apache-iceberg](), [apache-kafka](), [architecture-design](), [asynchronous-architecture](), [cassandra](), [cloud-architecture](), [concurrency](), [development](), [devops](), [distributed-systems](), [elasticsearch](), [java-operator-sdk](), [kafka](), [latency](), [microservices](), [netflix](), [netflix-conductor-4-workflow](), [news](), [orchestration](), [s3](), [scalability](), [windows-workflow-foundation](), [workflow](), [workflow-bpm](), [workflow-foundation]() ### AI overview Netflix reworked Conductor 4.0 to scale workflow orchestration to roughly 200,000 definitions and 420 million monthly executions. The redesign raises supported workflow size to 30,000 tasks and reports a roughly 40% reduction in p99 evaluation latency by loading only task data needed for each decision. ### Source excerpt Netflix has reworked its Conductor workflow orchestration engine to handle larger workloads, increasing supported workflow size from about 2,500 to 30,000 tasks and reducing p99 workflow evaluation latency by about 40%. Conductor 4.0 separates workflow metadata from task data, moves evaluation to asynchronous processing, and introduces dynamic worker allocation and concurrency controls. By Leela Kumili ## Video and image search in Amazon Bedrock Knowledge Base using Marengo 3.0 DevFeed: [Video and image search in Amazon Bedrock Knowledge Base using Marengo 3.0]() Original publisher: [Read original article]() Author: Eric Kim Published: 2026-09-10T21:15:39Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [AI search](), [Retrieval Augmented Generation (RAG)](), [Amazon S3](), [AWS IAM]() Tags: [amazon-bedrock](), [amazon-bedrock-knowledge-bases](), [announcements](), [audio](), [embedding](), [how-to](), [images](), [intermediate-200](), [multimodal](), [rag](), [s3](), [search](), [video]() ### AI overview A walkthrough for building an Amazon Bedrock Knowledge Base with TwelveLabs Marengo Embed 3.0 to perform natural-language semantic search across video, images, and audio. ### Source excerpt TwelveLabs Marengo Embed 3.0 is now generally available as an embedding model in Amazon Bedrock Knowledge Bases, bringing fully managed natural language search to video, image, and audio content. This walkthrough shows how to build a knowledge base powered by Marengo 3.0 and run semantic queries against your media. ## Build an end-to-end RFI questionnaire workflow using Amazon Quick Automate DevFeed: [Build an end-to-end RFI questionnaire workflow using Amazon Quick Automate]() Original publisher: [Read original article]() Author: Chaytanya Kumar Published: 2026-09-10T16:08:57Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Automation](), [Amazon S3](), [CSV](), [Amazon Web Services](), [datasets]() Tags: [advanced-300](), [amazon-quick-suite](), [amazon-s3](), [amazon-simple-storage-service-s3](), [automation](), [aws](), [data](), [how-to](), [technical-how-to](), [tutorial]() ### AI overview A tutorial for automating RFI questionnaire processing with Amazon Quick Automate. It reads multi-tab workbooks from Amazon S3, extracts and structures questions, and writes CSV output to Amazon S3. ### Source excerpt Learn how to build an end-to-end RFI questionnaire workflow with Amazon Quick Automate. Read a multi-tab RFI workbook from Amazon S3, use natural-language prompts to extract and structure the questionnaire data, refine the workflow through conversation, and write clean CSV output back to Amazon S3 -- cutting development from days to hours. ## Backblaze B2 x Suite Studios: S3 Native File Streaming Turns B2 Cloud Storage Into a High-Performance Drive DevFeed: [Backblaze B2 x Suite Studios: S3 Native File Streaming Turns B2 Cloud Storage Into a High-Performance Drive]() Original publisher: [Read original article]() Author: Dave Simon Published: 2026-09-10T14:04:38Z Content type: article Language: en Sources: [Backblaze Blog | Cloud Storage & Cloud Backup]() Topics: [Amazon S3](), [Cloud](), [Streaming](), [Filesystems](), [mount](), [data](), [migration]() Tags: [architecture](), [automation](), [b2cloud](), [cloud](), [cloud-storage](), [data](), [featured](), [featured-cloud-storage](), [media](), [media-workflow](), [migration](), [mount](), [nas](), [object-storage](), [s3](), [storage](), [streaming]() ### AI overview Backblaze B2 integrates with Suite Studios' S3 Native File Streaming to provide drive-like, high-performance access to cloud-stored data. Teams can mount B2 buckets, stream only the file portions applications need, and use standard S3-compatible objects without duplicating or relocating datasets. The integration supports media production and other workflows involving large cloud datasets. ### Source excerpt Backblaze B2 now integrates with Suite Studios S3 Native File Streaming, giving teams high-performance, drive-like access to cloud data. Work directly with standard S3-compatible objects across media, scientific, geospatial, and engineering workflows without duplicating datasets or creating new storage silos. The post Backblaze B2 x Suite Studios: S3 Native File Streaming Turns B2 Cloud Storage Into a High-Performance Drive appeared first on Backblaze Blog | Cloud Storage & Cloud Backup ## Improving Lakebase Postgres compute cache DevFeed: [Improving Lakebase Postgres compute cache]() Original publisher: [Read original article]() Author: David Wein; Sunil Kamath; Haoyu Huang Published: 2026-09-10T13:47:03Z Content type: article Language: en Sources: [Databricks]() Topics: [Caching](), [databricks](), [Databases](), [autoscaling](), [Amazon S3](), [Filesystems](), [cloud-infrastructure]() Tags: [autoscaling](), [cache](), [caching](), [data](), [data-engineering](), [databricks](), [engineering](), [filesystem](), [latency](), [low-latency](), [memory](), [s3](), [storage]() ### AI overview This Databricks article describes improvements to compute-side caching for Lakebase Postgres, a disaggregated storage system backed by object storage such as Amazon S3. It explains PostgreSQL shared buffers, the operating system page cache, and the planned use of dynamically autoscaling shared buffers consuming up to 75% of compute memory. It also introduces a local file cache as an incremental solution for fixed compute instances. ### Source excerpt The disaggregated storage model of Lakebase Postgres provides a feature rich, flexible... ## 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. ## Customize Amazon API Gateway destinations for execution logs DevFeed: [Customize Amazon API Gateway destinations for execution logs]() Original publisher: [Read original article]() Author: Giedrius Praspaliauskas Published: 2026-09-09T22:35:42Z Content type: tutorial Language: en Sources: [AWS Compute Blog]() Topics: [Amazon API Gateway](), [Amazon CloudWatch Logs](), [log management](), [Amazon S3](), [SIEM, Security, Observability]() Tags: [amazon-api-gateway](), [amazon-cloudwatch-logs](), [amazon-s3](), [announcements](), [intermediate-200](), [logs](), [observability](), [rest-api]() ### AI overview This tutorial explains how to route Amazon API Gateway REST API execution logs to CloudWatch Logs, Amazon S3, or Amazon Data Firehose. It contrasts execution logs with access logs and describes their use for diagnosing authorization, validation, integration, mapping, and error-handling behavior. ### Source excerpt Amazon API Gateway execution logs help you trace request processing step by step through your REST API stages. They capture authorization results, integration latency, mapping template output, and error details that are otherwise invisible at the API surface. When a production request fails in a way the access log cannot explain, the execution log is [...] ## Validating multi-Region DR for Terraform Enterprise with AWS FIS DevFeed: [Validating multi-Region DR for Terraform Enterprise with AWS FIS]() Original publisher: [Read original article]() Author: Frenil Randeria Published: 2026-09-09T21:05:02Z Content type: article Language: en Sources: [AWS Architecture Blog]() Topics: [Terraform](), [AWS Fault Injection Service (FIS)](), [Chaos Engineering](), [Amazon S3](), [Amazon EC2](), [Databases]() Tags: [advanced-300](), [amazon-aurora](), [amazon-ec2](), [amazon-s3](), [aws](), [aws-fault-injection-service-fis](), [disaster-recovery](), [postgresql](), [technical-how-to](), [terraform](), [validation]() ### AI overview This article explains how to validate customer-operated multi-Region disaster recovery for Terraform Enterprise on AWS using three-phase AWS FIS experiments. It covers failover and failback testing, hidden dependency discovery, and reported recovery times of 12-14 minutes. ### Source excerpt Learn how AWS, HashiCorp, and Athenahealth designed and chaos-tested a multi-Region disaster recovery strategy for Terraform Enterprise on AWS. This post walks through three-phase AWS Fault Injection Service experiments across Amazon EC2, Aurora, and Amazon S3, the 12-14 minute recovery times achieved, and the state file dependency pitfall to avoid. ## Improving Lakebase Postgres Compute Cache on Neon, Part 1 DevFeed: [Improving Lakebase Postgres Compute Cache on Neon, Part 1]() Original publisher: [Read original article]() Author: Sunil Kamath Published: 2026-09-09T12:00:00Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [Caching](), [Databases](), [Operating system](), [Amazon S3](), [cpu](), [Latency](), [autoscaling]() Tags: [autoscaling](), [cache](), [compute](), [cpu](), [engineering](), [latency](), [memory](), [os](), [postgres](), [s3](), [storage]() ### AI overview Neon describes a Lakebase Postgres compute-cache change that allocates most memory to shared buffers backed by huge pages. The goal is to keep hot pages in DRAM, reducing storage reads, CPU use, and latency; the article reports up to roughly 2x throughput on specified fixed-size computes. ### Source excerpt On large fixed-size Lakebase Postgres computes on Neon, we now put most of the machine's memory into Postgres shared buffers and back that cache with huge pages. Hot pages stay in DRAM instead of falling through to a local disk cache, so the same working set is served faster and with less CPU. ## Full-Text Search, Object Storage Backend, and More in ScyllaDB 2026.3 DevFeed: [Full-Text Search, Object Storage Backend, and More in ScyllaDB 2026.3]() Original publisher: [Read original article]() Author: Tzach Livyatan Published: 2026-09-08T21:32:47Z Content type: release Language: en Sources: [ScyllaDB]() Topics: [releases](), [Amazon S3](), [AI search](), [Retrieval Augmented Generation (RAG)]() Tags: [ai](), [product](), [rag](), [release](), [s3](), [search](), [storage]() ### AI overview ScyllaDB 2026.3 adds full-text search, a preview object-storage backend, OCI integration, and experimental table and migration capabilities. It also introduces large-data guardrails and cluster-wide restoration from object-storage backups. ### Source excerpt new updates should help you move even more workloads to ScyllaDB, at a fraction of the cost. ## Introducing chdb Postgres extension: High-performance imports from cloud storage DevFeed: [Introducing chdb Postgres extension: High-performance imports from cloud storage]() Original publisher: [Read original article]() Author: David Wheeler Published: 2026-09-08T15:42:52Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [cloud-infrastructure](), [Amazon S3](), [parquet](), [CSV]() Tags: [benchmark](), [clickhouse](), [cloud](), [duckdb](), [extension](), [json](), [parquet](), [performance](), [postgres](), [s3](), [storage]() ### AI overview chdb is a new Postgres extension that uses the in-process ClickHouse engine to import and export data across cloud storage systems and formats. The article presents import benchmarks, format support, and usage through a query function and a COPY hook module. ### Source excerpt The chdb Postgres extension brings fast imports and exports across cloud storage platforms and data formats, powered by the embedded ClickHouse engine. ## Cut GPU inference cold start from 8 minutes to less than a minute DevFeed: [Cut GPU inference cold start from 8 minutes to less than a minute]() Original publisher: [Read original article]() Author: Sajjan Gundapuneedi Published: 2026-09-03T18:30:00Z Content type: article Language: en Sources: [Kubernetes Overview, News and Trends | The New Stack]() Topics: [GPU](), [Inference](), [model-deployment](), [Amazon Elastic Kubernetes Service](), [CUDA](), [Optimization](), [Amazon S3](), [Kubernetes]() Tags: [ai-infrastructure](), [amazon-eks](), [aws-marketplace](), [cpu](), [cuda](), [gpu](), [inference](), [kubernetes](), [large-language-models](), [optimization](), [post-contributed](), [s3](), [sponsor-aws-marketplace](), [sponsored-post-contributed]() ### AI overview The article measures GPU model startup from pod creation to the first inference response and identifies six sequential phases. It reports that CUDA kernel recompilation dominates startup for a 64 GB model, while S3 weight downloads dominate for a 203 GB model. Configuration and platform changes reduced warm-node startup times by 80-93%, with additional cold-node improvements requiring Amazon EKS Auto Mode. ### Source excerpt We instrumented the full path from pod creation to first inference response on a GPU node running a 70B-class model. The post Cut GPU inference cold start from 8 minutes to less than a minute appeared first on The New Stack. ## Announcing the S3 API: Use any S3 client with Appwrite Storage DevFeed: [Announcing the S3 API: Use any S3 client with Appwrite Storage]() Original publisher: [Read original article]() Author: Torsten Dittmann Published: 2026-09-03T00:00:00Z Content type: release Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [Amazon S3](), [API](), [Amazon Web Services](), [SDKs](), [Command-line interface](), [migration]() Tags: [announcements](), [api](), [aws](), [backup](), [cli](), [code](), [commands](), [data](), [files](), [migration](), [reuse](), [s3](), [sdk](), [sdks](), [server](), [storage](), [tools]() ### AI overview Appwrite Storage now exposes an S3-compatible API that supports AWS CLI, AWS SDKs, rclone, s3cmd, and other S3 clients. Existing buckets and files remain available through both the native Storage API and the S3 API, with support for standard operations, multipart uploads, and presigned URLs. ### Source excerpt Appwrite Storage now exposes an S3-compatible API. Point the AWS CLI, the AWS SDKs, and tools like rclone at your Appwrite buckets, with no migration required. ## Fast model loading for AI inference on Amazon EKS DevFeed: [Fast model loading for AI inference on Amazon EKS]() Original publisher: [Read original article]() Author: Sajjan Gundapuneedi Published: 2026-09-01T15:48:15Z Content type: article Language: en Sources: [Containers]() Topics: [Inference Performance](), [Amazon Elastic Kubernetes Service](), [Amazon S3](), [Language models]() Tags: [advanced-300](), [ai](), [ai-inference](), [amazon-eks](), [amazon-elastic-kubernetes-service](), [best-practices](), [cuda](), [gpu](), [inference](), [kubernetes](), [language-models](), [pytorch](), [s3](), [sglang](), [startup](), [vllm]() ### AI overview The article analyzes cold-start delays for AI inference pods on Amazon EKS. It finds that startup bottlenecks vary by model size: torch.compile dominates for smaller models, while loading weights from S3 to GPU memory dominates for larger models. Configuration changes to Run:ai Model Streamer reduce model-loading time on repeat launches. ### Source excerpt When you scale AI inference on Amazon EKS, every new pod must load model weights into GPU memory before serving traffic. We investigated where cold-start time goes and found two configuration-only changes to Run:ai Model Streamer that cut model startup time by 80-93% on subsequent launches, with no code changes. ## AWS Weekly Roundup: Welcome DuckLabs to the team, Agentic Resource Discovery (ARD), and more (August 31, 2026) DevFeed: [AWS Weekly Roundup: Welcome DuckLabs to the team, Agentic Resource Discovery (ARD), and more (August 31, 2026)]() Original publisher: [Read original article]() Author: Daniel Abib Published: 2026-08-31T14:45:25Z Content type: news Language: en Sources: [AWS News Blog]() Topics: [data](), [Amazon S3](), [Amazon Redshift](), [Node.js]() Tags: [acquisition](), [amazon-ec2](), [amazon-elastic-container-service](), [amazon-gamelift](), [amazon-redshift](), [amazon-s3](), [amazon-sagemaker](), [amazon-sagemaker-ai](), [analytics](), [announcements](), [aws](), [aws-fargate](), [aws-glue](), [aws-iot-core](), [aws-lambda](), [database](), [duckdb](), [json](), [news](), [node-js](), [open-source](), [parquet](), [python](), [sql](), [week-in-review]() ### AI overview AWS weekly roundup covering the planned acquisition of DuckLabs, the company behind DuckDB, alongside Amazon ECS recovery updates and AWS Lambda preview runtimes for Node.js 26 and Python 3.15. ### Source excerpt The news that interested me the most last week was the DuckLabs acquisition. AWS has signed a definitive agreement to acquire DuckLabs, the Amsterdam-based company behind DuckDB, the popular open source analytical database that runs in-process and executes SQL directly against files like Parquet, CSV, and JSON. DuckDB stays open source under its independent foundation [...] ## Announcing Appwrite 2.0: a new foundation for your apps DevFeed: [Announcing Appwrite 2.0: a new foundation for your apps]() Original publisher: [Read original article]() Author: Eldad Fux Published: 2026-08-31T00:00:00Z Content type: release Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [Databases](), [Amazon S3](), [OAuth](), [Firewall](), [MySQL](), [PostgreSQL]() Tags: [feature](), [firewall](), [mysql](), [oauth](), [postgresql](), [products](), [release](), [s3]() ### AI overview Appwrite 2.0 is a major platform release with a new engine, rebuilt Console, relational, schemaless, and vector data support, native PostgreSQL and MySQL engines, S3-addressable storage, OAuth 2.1 and OpenID Connect support, and organization-level Domains and Firewall. ### Source excerpt Appwrite 2.0 brings a new engine, a rebuilt Console, five database types, an S3 API, an OAuth 2.1 server, and organization-level Domains and Firewall. ## Detecting multi-stage attacks on AWS: A guide to cross-service signal correlation DevFeed: [Detecting multi-stage attacks on AWS: A guide to cross-service signal correlation]() Original publisher: [Read original article]() Author: Nisha Kashyap Published: 2026-08-26T17:39:19Z Content type: article Language: en Sources: [AWS Security Blog]() Topics: [Amazon Web Services (AWS)](), [Security](), [Detection engineering](), [Amazon CloudWatch Logs](), [AWS CloudTrail](), [VPC Flow Logs](), [Amazon S3](), [data]() Tags: [advanced-300](), [amazon-cloudwatch](), [amazon-cloudwatch-logs](), [amazon-guardduty](), [amazon-route-53](), [amazon-web-services-aws](), [analysis](), [aws-cloudtrail](), [aws-security-hub](), [best-practices](), [cybersecurity](), [guide](), [incident-response](), [logs](), [s3](), [security](), [security-blog](), [security-identity-compliance](), [shared-responsibility-model](), [technical-how-to](), [threat-detection](), [vpc-flow-logs]() ### AI overview This article explains how security engineers can detect multi-stage attacks on AWS by correlating signals across services with business context. It presents examples using CloudWatch Logs Insights and discusses expanding the correlations into an automated pipeline. ### Source excerpt A single alert from one security service tells you something happened. Read that signal alongside activity from other services and your own business context, and you will know whether what happened is part of a multi-stage attack. Consider a short sequence. An identity calls GetCallerIdentity from a source address it hasn't previously used. Within minutes, [...] ## 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]()