# object storage Published articles for object storage. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## CTERA Data Archiving Solution Pairs InsightAI With CTERA Archive to Move Inactive Files Off Primary Storage With an Audit Trail DevFeed: [CTERA Data Archiving Solution Pairs InsightAI With CTERA Archive to Move Inactive Files Off Primary Storage With an Audit Trail]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-17T17:29:28Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [data-processing](), [Artificial Intelligence](), [Security](), [Amazon S3]() Tags: [ai](), [audit-trail](), [data-protection](), [data-storage](), [enterprise](), [object-storage](), [restore](), [retention](), [s3]() ### AI overview CTERA launched a Data Archiving Solution that combines InsightAI with CTERA Archive to identify inactive files and move them from primary storage under retention policies and an audit trail. The solution keeps archived data governed and accessible within the CTERA Intelligent Data Platform, using on-premises or cloud storage tiers. ### Source excerpt CTERA has launched the CTERA Data Archiving Solution, which pairs its InsightAI data service with CTERA Archive so the platform can recommend which files to move off primary storage and then execute the move under retention policy and an audit trail. It's the product follow-through to the Cold Data Storage Report CTERA published a day The post CTERA Data Archiving Solution Pairs InsightAI With CTERA Archive to Move Inactive Files Off Primary Storage With an Audit Trail appeared first on StorageReview.com. ## Data Engineering Weekly #287 DevFeed: [Data Engineering Weekly #287]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-09-14T02:52:23Z Content type: article Language: en Sources: [Data Engineering Weekly]() Topics: [data-engineering](), [Kafka](), [data observability](), [Embeddings](), [Multi-tenancy](), [Event-Streaming](), [OpenAI](), [Library](), [Code]() Tags: [architecture](), [architectures](), [data-engineering](), [data-quality](), [embedding](), [engineering](), [kafka](), [multi-tenancy](), [object-storage](), [observability](), [openai](), [streaming](), [vector]() ### AI overview Data Engineering Weekly #287 covers building data platforms from scratch, including composable architectures, data quality, and observability. It also previews talks on governed machine-executable ontologies for marketing activation and fair, order-preserving Kafka consumption for many tenants. The issue links to OpenAI's storage platform scaling for ChatGPT and Pinterest's embedding retrieval platform. ### Source excerpt The Weekly Data Engineering Newsletter ## 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 ## Introducing the Backblaze B2 MCP Server DevFeed: [Introducing the Backblaze B2 MCP Server]() Original publisher: [Read original article]() Author: Jeronimo De Leon Published: 2026-09-08T18:13:00Z Content type: article Language: en Sources: [Backblaze Blog | Cloud Storage & Cloud Backup]() Topics: [MCP Server](), [Model Context Protocol](), [Cloud](), [Authorization](), [Open Source]() Tags: [agents](), [ai](), [ai-ml](), [api](), [b2cloud](), [cloud](), [cloud-storage](), [developers](), [featured](), [featured-cloud-storage](), [mcp](), [mcp-server](), [object-storage](), [open-source](), [storage](), [uncategorized]() ### AI overview Backblaze introduces an open-source B2 MCP Server that lets AI agents operate cloud object storage through MCP-compatible clients. Access is constrained by the permissions of the connected B2 application key, including limits on buckets and operations. ### Source excerpt The Backblaze B2 MCP Server gives AI agents a safe, standard way to operate cloud object storage. Connect MCP-compatible clients to scoped B2 application keys, manage files and buckets, move large objects directly, and control destructive actions with configurable safeguards. The post Introducing the Backblaze B2 MCP Server appeared first on Backblaze Blog | Cloud Storage & Cloud Backup ## AI-Ready Private Cloud with Cisco and VMware DevFeed: [AI-Ready Private Cloud with Cisco and VMware]() Original publisher: [Read original article]() Author: sabina anja Published: 2026-09-08T15:33:39Z Content type: article Language: en Sources: [VMware Blogs]() Topics: [Artificial Intelligence](), [Network](), [Cloud](), [networking](), [cloud-infrastructure](), [distributed-training](), [datacenter](), [GPU](), [Latency](), [Inference](), [model-deployment](), [inference-endpoints]() Tags: [ai](), [cisco](), [cloud](), [cloud-infrastructure](), [cloud-platform](), [data-center](), [distributed-training](), [fabric](), [gpu](), [home-page](), [inference](), [inference-endpoints](), [latency](), [networking](), [object-storage](), [pipelines](), [private-cloud](), [telemetry](), [vcf-9-1](), [vcf-networking](), [vmware](), [vmware-cloud-foundation]() ### AI overview This article explains why an AI-ready private cloud requires more than adding GPUs. It focuses on how Broadcom and Cisco are integrating VMware Cloud Foundation with Cisco Nexus One Fabric to address AI workload networking, including bandwidth-intensive east-west traffic, bursty north-south traffic, latency, congestion management, and telemetry across virtual and physical infrastructure. ### Source excerpt An AI-ready private cloud is not simply a private cloud with GPUs added to it. What determines whether a private cloud platform can actually serve AI workloads effectively is everything built around them: how the fabric carries traffic, how the tenancy model lets teams consume capacity, and how policy and telemetry stay coherent across the ... Continued The post AI-Ready Private Cloud with Cisco and VMware appeared first on VMware Blogs. ## ASUS Lays Out a Full AI Factory Platform: Vera Rubin NVL72 Racks, STX Storage, and a Governance Layer DevFeed: [ASUS Lays Out a Full AI Factory Platform: Vera Rubin NVL72 Racks, STX Storage, and a Governance Layer]() Original publisher: [Read original article]() Author: Lyle Smith Published: 2026-09-04T17:54:45Z Content type: article Language: en Sources: [StorageReview.com]() Topics: [AI Factory](), [AI Infrastructure](), [AI Platform](), [NVIDIA Vera Rubin](), [NVIDIA DGX](), [NVIDIA Vera](), [MLOps](), [NVIDIA DSX](), [ai-governance](), [Server]() Tags: [2026](), [ai](), [ai-factory](), [ai-governance](), [ai-infrastructure](), [asus](), [enterprise](), [mlops](), [nvidia-dsx](), [nvidia-vera](), [nvl72](), [object-storage](), [platform](), [storage](), [vera-rubin-nvl72]() ### AI overview ASUS is broadening its AI infrastructure business from individual servers to a full AI factory platform covering accelerated computing, networking, storage, deployment, infrastructure management, MLOps, and governance. The article describes NVIDIA DSX-based planning, ASUS deployment and management tools, governance for AI services and autonomous agents, and server systems based on NVIDIA Vera Rubin hardware. ### Source excerpt ASUS is expanding its role in AI infrastructure, moving from individual AI servers to platforms for building, deploying, and operating entire AI factories. At AI Tech 2026 in Seoul, the company laid out a broader strategy that brings accelerated computing, networking, storage, deployment software, infrastructure management, and AI governance together under one platform. That puts The post ASUS Lays Out a Full AI Factory Platform: Vera Rubin NVL72 Racks, STX Storage, and a Governance Layer appeared first on StorageReview.com. ## Four Platforms, One Timeline: Bringing TAMS Workflow to Life on Backblaze B2 DevFeed: [Four Platforms, One Timeline: Bringing TAMS Workflow to Life on Backblaze B2]() Original publisher: [Read original article]() Author: Dave Simon Published: 2026-09-03T14:23:57Z Content type: article Language: en Sources: [Backblaze Blog | Cloud Storage & Cloud Backup]() Topics: [cloud-infrastructure](), [API](), [Kubernetes](), [browser](), [systems]() Tags: [api](), [article](), [b2cloud](), [browser](), [cloud](), [cloud-native](), [cloud-storage](), [featured](), [featured-cloud-storage](), [kubernetes](), [media](), [object-storage](), [storage]() ### AI overview The article explains how Time Addressable Media Store (TAMS) organizes live media by timeline-based, timestamped segments in object storage. It describes a cloud-native, interoperable workflow built around Backblaze B2 and ecosystem partners LiveWyer, CuttingRoom, and Drastic Technologies, enabling ingest, editing, distribution, playback, and archiving through shared access to the same content. ### Source excerpt TAMS organizes live media by time, giving distributed production teams faster access to shared footage. See how Backblaze B2, LiveWyer, CuttingRoom, and Drastic Technologies create an interoperable, cloud-native workflow for ingest, editing, playback, distribution, and archive with economics at scale. The post Four Platforms, One Timeline: Bringing TAMS Workflow to Life on Backblaze B2 appeared first on Backblaze Blog | Cloud Storage & Cloud Backup ## Encore now works with Entire DevFeed: [Encore now works with Entire]() Original publisher: [Read original article]() Author: Ivan Cernja Published: 2026-09-02T00:00:00Z Content type: tutorial Language: en Sources: [Encore Updates]() Topics: [AI-assisted coding](), [codex](), [Claude Code](), [coding](), [Git](), [cursor](), [PostgreSQL]() Tags: [claude-code](), [codex](), [coding](), [coding-agents](), [cursor](), [git](), [object-storage](), [postgresql]() ### AI overview Encore now works with Entire, allowing developers to use Codex, Claude Code, Gemini, and Cursor in Encore projects while recording agent sessions in Git and linking them to commits. The article demonstrates building and testing an image service with local PostgreSQL and object storage, including integration tests and a live upload/download verification. ### Source excerpt Keep the agent session behind every Encore change, directly in Git. ## How Object Storage Performance Affects GPU Training Workloads DevFeed: [How Object Storage Performance Affects GPU Training Workloads]() Original publisher: [Read original article]() Author: Sridhar Rajarao Published: 2026-08-30T00:00:00Z Content type: article Language: en Sources: [Sridhar Rajarao]() Topics: [GPU](), [cloud-infrastructure](), [Caching](), [Disk image]() Tags: [ai](), [aws](), [cache](), [compute](), [gpu](), [nvme](), [object-storage](), [performance](), [storage](), [training-data]() ### AI overview This article explains how object-storage latency, network transfers, many small files, and concurrent requests can leave GPUs idle during AI training. It recommends sharding small samples into larger files, reusing connections, and caching data near the GPU; it also notes AWS's suggested shard range of roughly 100 MB to 1 GB for many training workloads. ### Source excerpt The GPU does not care that object storage is durable and scalable. It cares whether the next batch of data arrives before it goes idle. ## KV vs Prefix vs Prompt vs Semantic Caching DevFeed: [KV vs Prefix vs Prompt vs Semantic Caching]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-08-27T20:01:47Z Content type: article Language: en Sources: [Daily Dose of Data Science]() Topics: [Caching](), [Large Language Model](), [Neo4j]() Tags: [caching](), [embedding](), [knowledge-graph](), [latency](), [llm](), [memory](), [nvme](), [object-storage](), [retrieval](), [server](), [token](), [vector]() ### AI overview The article distinguishes KV caching, prefix caching, prompt caching, and semantic caching in LLM systems. It explains what each cache stores, how exact-match and fuzzy-match behavior differ, and how cache misses affect cost, latency, and correctness. It also describes Konig's tiered storage approach for many small knowledge graphs, though the supplied text is truncated. ### Source excerpt ...explained with best practices in production. ## Vercel Sandbox is now globally available DevFeed: [Vercel Sandbox is now globally available]() Original publisher: [Read original article]() Author: Rob Herley Published: 2026-08-24T04:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Vercel](), [Command-line interface](), [SDKs](), [Latency](), [cloud-infrastructure]() Tags: [cli](), [databases](), [documentation](), [enterprise](), [latency](), [object-storage](), [reduce](), [sdk](), [snapshots](), [vercel]() ### AI overview Vercel Sandbox is now globally available in four regions, with region selection supported on all plans and configurable failover regions for Pro and Enterprise teams. ### Source excerpt Vercel Sandbox now runs globally, starting with four regions: iad1 (Washington, D.C.), sfo1 (San Francisco), cle1 (Cleveland), and cdg1 (Paris). iad1 remains the default. Support for all Vercel regions is coming soon. Choose a region close to the databases, object storage, and other services your sandboxes access to reduce latency. Region selection is available on all plans. Pro and Enterprise teams can also configure failover regions. If the primary region is unavailable, new sandboxes start in the closest configured failover region. Failover only applies to regions you configure; without it, a sandbox starts in its selected region, or the project default. Update the SDK or CLI you use to the latest version: Configure the default region and failover regions from Settings > Sandboxes in your project, or with the Vercel CLI: Those defaults apply to every sandbox. To override them for a single sandbox, pass region and optionally failoverRegions at create time with the SDK: Or with the CLI: Learn more in the Sandbox regions documentation. Read more ## Results from the Backblaze Generative AI Media Hackathon DevFeed: [Results from the Backblaze Generative AI Media Hackathon]() Original publisher: [Read original article]() Author: Jeronimo De Leon Published: 2026-08-14T15:52:05Z Content type: article Language: en Sources: [Backblaze Blog | Cloud Storage & Cloud Backup]() Topics: [Generative AI](), [Orchestration](), [cloud-infrastructure](), [For the Love of Code](), [Open Source](), [SDKs]() Tags: [2026](), [ai-ml](), [b2cloud](), [cloud](), [cloud-storage](), [developer](), [featured](), [featured-cloud-storage](), [generative](), [hackathon](), [object-storage](), [open-source](), [orchestration](), [sdk](), [tech-lab](), [technical]() ### AI overview The article examines the strongest projects from the Backblaze Generative AI Media Hackathon and argues that production-ready generative media applications depend on more than generating images, video, or audio. It highlights storage design, provenance, verification, correction, deletion protection, and orchestration across multiple providers. ### Source excerpt See what the strongest projects from the Backblaze Generative AI Media Hackathon had in common. These five generative media apps show how storage and orchestration become part of production-ready design. The post Results from the Backblaze Generative AI Media Hackathon appeared first on Backblaze Blog | Cloud Storage & Cloud Backup ## Inside Appwrite's new build cache: 4x faster dependency installs DevFeed: [Inside Appwrite's new build cache: 4x faster dependency installs]() Original publisher: [Read original article]() Author: Torsten Dittmann Published: 2026-08-13T00:00:00Z Content type: article Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [Caching](), [Cache](), [Deployment](), [Next.js](), [Package manager](), [Compression](), [cloud-infrastructure]() Tags: [cache](), [compression](), [dependencies](), [deployment](), [engineering](), [filesystem](), [next-js](), [object-storage](), [pnpm](), [production]() ### AI overview Appwrite's build network caches dependencies between builds for sites and functions. The cache is restored before installation and saved after successful builds, with snapshots isolated per resource. The article explains how Appwrite uses SquashFS images to efficiently store and unpack package-manager data, reporting faster installs for example Next.js applications. ### Source excerpt A deep dive into how Appwrite now caches your dependencies between builds, and what it means for your deployment times. ## My agent cloned GitHub (sort of) using the Neon backend DevFeed: [My agent cloned GitHub (sort of) using the Neon backend]() Original publisher: [Read original article]() Author: Roman Zaynetdinov Published: 2026-07-23T09:00:00Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [Git](), [GitHub](), [Hono](), [JavaScript](), [Database](), [Amazon S3](), [Serverless](), [web applications](), [Authorization](), [Back end]() Tags: [app](), [auth](), [backend](), [cli](), [code](), [database](), [git](), [github](), [javascript](), [object-storage](), [product](), [s3](), [server](), [serverless](), [web]() ### AI overview An experiment uses an AI agent and Neon's beta backend suite to build a working Git host without a filesystem. Git objects are stored in Postgres, large blobs are offloaded to Neon Object Storage, and a serverless Hono application serves Git over HTTPS. The implementation supports cloning, pushing, pulling, commit and file browsing, authentication, and testing against edge cases such as binary, large, empty, and oddly named files. ### Source excerpt GitHub has been down a lot lately. During one of those episodes, I had the idea of asking my agent to use our new backend suite (still in beta) to build a working Git host. This is genuinely outside of my comfort zone as I had no prior knowledge of Git server internals, so I didn't have much hopes, but it went surprisingly well... ## v19.2.5 Squid released DevFeed: [v19.2.5 Squid released]() Original publisher: [Read original article]() Author: Yuri Weinstein Published: 2026-07-14T00:00:00Z Content type: news Language: en Sources: [Ceph Blog]() Topics: [changelog](), [Amazon S3](), [bug](), [TLS (Transport Layer Security)](), [Authentication](), [Authorization](), [SSL]() Tags: [authentication](), [authorization](), [blog-post](), [bug](), [changelog](), [concurrent](), [configuration](), [en-article](), [en-blog-post](), [object-storage](), [queuing](), [release](), [s3](), [squid](), [ssl](), [tls](), [vector]() ### AI overview Ceph v19.2.5 Squid is the fifth backport release in the Squid series. It fixes issues across MDS, OSD, and RGW, including request queuing faults, rollback and erasure-coding behavior, TLS 1.3 cipher configuration, S3 ACL handling, multisite synchronization, policy evaluation, presigned URL authentication, object tagging, frontend concurrency, and bucket statistics. ### Source excerpt This is the fifth backport release in the Squid series. We recommend all users update to this release. Notable Changes ¶ MDS (Metadata Server) ¶ Fixed a segmentation fault relating to MDSContext completion and request queuing. OSD (Object Storage Daemon) ¶ Rollback & Vector Fixes: Corrected rollback logic for partial write object information (OI) and optimized Erasure Coding (EC) by ensuring Twiddle creates a full-sized vector. RGW (RADOS Gateway) ¶ TLS 1.3 Ciphersuites: Introduced the ssl_ciphersuites configuration option for the Beast front-end to support TLS 1.3 cipher customizations. S3 PutObject ACLs Fix: Fixed a bug where PutObject requests were erroneously rejected under BlockPublicAcls due to an incorrect comparison return value from canned_acl.compare(). Multisite Sync Robustness: Resolved an infinite loop scenario in RGWBucketFullSyncCR when a source bucket was deleted mid-sync by introducing a mechanism to clear stateful data in reused bucket_list_result objects. SNS Policy Evaluation: Updated ListTopics to use the account root ARN for policy evaluation when invoked by a non-root account user, preventing unexpected implicit denies from identity-based policies. Presigned URLs: Fixed an internal server error occurring during the authentication process of presigned URLs. Prerequest Hook: Restructured the order of execution so that the prerequest hook now runs after the authorization process has completed. Object Tagging: Added handling for plain-text object tags during execution of RGWObjTags::decode(). Beast Frontend Stability: Applied a strand executor to the Beast timeout timer to avoid concurrent socket access and potential race conditions. Bucket Statistics: Added a fix for bucket stats execution when the underlying bucket index does not exist. Changelog ¶ [Stretch Mode] mon: restrict changing mon election strategy post stretch mode (pr#65458, Kamoltat Sirivadhna) Check if HTTP\_X\_AMZ\_COPY\_SOURCE header is empty (pr#66028, Suyash Dongre) Fix multifs ## Vercel Sandbox now supports FUSE-based filesystems DevFeed: [Vercel Sandbox now supports FUSE-based filesystems]() Original publisher: [Read original article]() Author: Brandon Tuttle Published: 2026-07-03T01:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Vercel](), [Filesystems](), [mount](), [Amazon S3](), [Network](), [datasets]() Tags: [data](), [datasets](), [driver](), [filesystems](), [mount](), [network](), [object-storage](), [remote](), [s3](), [vercel]() ### AI overview Vercel Sandbox now supports FUSE-based filesystems, allowing remote storage and custom filesystems to be mounted as regular paths inside a running Sandbox. This includes S3 buckets, network filesystems, and other FUSE-compatible drivers, enabling direct access to large datasets without copying them into the Sandbox. ### Source excerpt Vercel Sandbox now supports FUSE, letting you mount remote storage and custom filesystems inside a running Sandbox. Use it to attach S3 buckets, network filesystems, or any other FUSE-compatible driver as a regular path. This makes it possible to stream large datasets directly from object storage, share state across Sandboxes through a common filesystem, or run tools that expect POSIX paths against remote sources without copying data into the Sandbox first. Learn more about remote storage mounts in the documentation. Read more ## How Redpanda Cloud Topics rethinks Kafka compaction DevFeed: [How Redpanda Cloud Topics rethinks Kafka compaction]() Original publisher: [Read original article]() Author: Willem Kaufmann Published: 2026-06-30T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Kafka](), [cloud-infrastructure](), [Cloud](), [Streaming](), [Algorithm]() Tags: [apache](), [apache-kafka](), [architecture](), [cloud-native](), [cloud-storage](), [clusters](), [cpu](), [cycles](), [engineering](), [kafka](), [object-storage](), [reduce](), [retention](), [scale](), [storage](), [streaming]() ### AI overview The article explains how Redpanda Cloud Topics redesigns Kafka log compaction for cloud-native streaming. It describes how the architecture reduces redundant processing and CPU use, lowers cloud storage costs, and preserves Kafka behavior while addressing scaling challenges such as limited memory, tombstone removal, and rewriting large volumes of object storage data. ### Source excerpt Compaction can overwhelm poorly sized Kafka clusters, leading to full disks and maxed-out CPUs. Learn how Redpanda's Cloud Topics architecture redesigns compaction to cut redundant work, reduce cloud storage costs, and preserve the Kafka semantics you rely on. ## Adaptive write request scheduling in Redpanda's Cloud Topics DevFeed: [Adaptive write request scheduling in Redpanda's Cloud Topics]() Original publisher: [Read original article]() Author: Evgeny Lazin Published: 2026-06-18T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Kafka](), [Amazon S3](), [Latency](), [Cloud](), [data]() Tags: [batching](), [cloud](), [concurrent](), [cost](), [efficiency](), [engineering](), [kafka](), [latency](), [object-storage](), [s3]() ### AI overview This article explains Redpanda's adaptive write-request scheduler for Cloud Topics. Because each produce batch is uploaded to Amazon S3 before acknowledgment, the scheduler dynamically adjusts upload parallelism across CPU cores to balance batching efficiency, producer latency, throughput, and storage cost. It also describes how centralized batching avoids the latency and request-cost problems of running one batcher per shard. ### Source excerpt How we turned to the buddy allocator algorithm for Redpanda's Cloud Topics to balance batching efficiency against latency and cost. ## Ceph Days Seattle 2026 - Cloud Transition to Tape DevFeed: [Ceph Days Seattle 2026 - Cloud Transition to Tape]() Original publisher: [Read original article]() Author: John Shubeck Published: 2026-06-15T00:00:00Z Content type: article Language: en Sources: [Ceph Blog]() Topics: [Open Source](), [Amazon S3](), [ibm](), [Cloud](), [Software]() Tags: [2026](), [blog-post](), [ceph](), [cloud](), [conferences](), [developers](), [en-article](), [en-blog-post](), [ibm](), [network](), [object-storage](), [open-source](), [s3](), [seattle](), [storage]() ### AI overview This article recaps Ceph Days Seattle 2026, a community-driven event about open-source storage. It introduces Ceph as a software-defined platform for block, file, and object storage, and highlights a presentation on transitioning Ceph cloud workloads to S3-enabled tape. ### Source excerpt I'm John Shubeck, an information technology professional with over 44 years of industry experience spanning both the customer and technology provider experience. I'm currently serving as a Senior Storage Technical Specialist for IBM Object Storage platforms across all market segments in the Americas. On Thursday, May 28th, 30 members, myself included, of the Ceph faithful descended on the SURF business incubator in downtown Seattle for "Ceph Days Seattle 2026". Following the traditional Ceph Days agenda, the Seattle program consisted of a series of "Ceph Talks" designed to introduce new ideas and share lessons learned. Ceph Days are community-driven events designed to bring together Ceph users, developers, architects, administrators, and anyone interested in open-source storage. Held throughout the year in locations around the world, these one-day gatherings offer a unique opportunity to learn from real-world deployments, explore new technologies, connect with peers, and contribute to discussions that help shape the future of Ceph. Did you know? ¶ Ceph is a software-defined, hardware-independent storage solution that provides block, file, and object storage on a single unified platform. Ceph is championed by developers, administrators, users, IT leaders, and Fortune 500 enterprise customers. It is a living, vibrant, and active group that has adopted Ceph in its IT operations. The Ceph project and community are supported by a Foundation (https://ceph.io/en/foundation/) comprising organizations, stakeholders, and industry leaders who collaborate to coordinate investment, development, and community activities for Ceph. With the setting, plot, and characters stated above, we turn to today's topic. A key activity of the Ceph community is a series of "Ceph Days" conferences. A Ceph Day is a full-day meetup where participants learn, network, make new acquaintances, and share ideas on how to enhance the value of Ceph. Sometimes IBM participates by providing the venue, a gue ## Cloud Topics: the Metastore DevFeed: [Cloud Topics: the Metastore]() Original publisher: [Read original article]() Author: Andrew Wong Published: 2026-06-11T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Kafka](), [Cloud](), [rocksdb](), [data]() Tags: [architecture](), [cloud](), [consumer](), [engineering](), [kafka](), [learn](), [mapping](), [memory](), [object-storage](), [retention](), [rocksdb](), [scale](), [serialization](), [storage]() ### AI overview This article explains Redpanda's metastore for Cloud Topics. The metastore maps Apache Kafka offsets to byte ranges in L1 objects stored in object storage, while tracking metadata such as leader-term boundaries and compaction state. Redpanda built a general-purpose key-value store, inspired by LevelDB and RocksDB and implemented as an LSM tree, to scale metadata independently of memory, local disk, and metadata formats. ### Source excerpt Learn how Redpanda's metastore powers Cloud Topics, from offset lookups and whole cluster restore to cross-region read replicas, and why it's built to be a foundational primitive for the future. ## Managing Multiple Lua Scripts with Ceph Object Storage DevFeed: [Managing Multiple Lua Scripts with Ceph Object Storage]() Original publisher: [Read original article]() Author: Kirby Chin Published: 2026-06-10T00:00:00Z Content type: article Language: en Sources: [Ceph Blog]() Topics: [Lua](), [Script](), [Scripting](), [Security](), [Command-line interface](), [cloud-infrastructure](), [ransomware]() Tags: [availability](), [blog](), [blog-post](), [ceph](), [cli](), [data](), [developer](), [en-article](), [en-blog-post](), [examples](), [feature](), [go](), [lua](), [management](), [new-feature](), [object-storage](), [operations](), [rados](), [ransomware](), [rgw](), [s3](), [scripting](), [security](), [storage]() ### AI overview This article introduces a Ceph RADOS Gateway feature for managing multiple Lua scripts within the same request context and tenant. It demonstrates scripts for enforcing bucket object locks to mitigate ransomware risks and for optimizing storage through object auto-tiering. ### Source excerpt Since the Pacific release, Lua scripting in Ceph's RADOS Gateway (RGW) has provided users the ability to interpolate a single script to upload operations per request context and tenant. This way of working might be completely fine for a storage deployment with limited scripting customizations. However, script management becomes increasingly difficult as more than one team wants to get involved in managing the Lua script within the same context and tenant. For this reason, we've released a new feature in RGW allowing you to manage more than one Lua script at a time. In this blog, we'll go over a couple of examples to walk you through how this feature can help your team to reduce runtime errors and lower developer friction when managing Lua scripts in RGW. To begin, we will create a script to enforce a bucket security control and then add another script to demonstrate the new feature. Script 1: Bucket security ¶ Let's suppose we are part of a security team and want to mitigate the risk of ransomware threats happening on our storage platform. One possible way to achieve this is to implement a Write Once, Read Many (WORM) strategy to add an object lock onto any newly created bucket. By using an object lock, we can add a constraint to write objects to disk only once, ensuring that an infected client cannot delete or override objects at a later time. To set our WORM strategy, we can create a Lua script in the prerequest context that aborts when a create_bucket operation is made without the write-once (object lock) requirement. objectlock.lua -- enforcing object lock on bucket creation if Request.RGWOp == "create_bucket" and Request.HTTP.Metadata["x-amz-bucket-object-lock-enabled"] ~= "true" then RGWDebugLog("object lock is missing on bucket: " .. Request.Bucket.Name) Request.Response.Message = "Bucket must have object lock enabled" return RGW_ABORT_REQUEST end You can create a new objectlock.lua file with the contents above and run the CLI commands below to upload the scr ## How we shipped 15 Tbps for OpenAI in 90 days (Session 1 of 3) DevFeed: [How we shipped 15 Tbps for OpenAI in 90 days (Session 1 of 3)]() Original publisher: [Read original article]() Author: Sridhar Rajarao Published: 2026-06-08T00:00:00Z Content type: article Language: en Sources: [Sridhar Rajarao]() Topics: [cloud-infrastructure](), [Network architectures](), [Network Configuration](), [OpenAI]() Tags: [architecture](), [gpu](), [network](), [network-configuration](), [object-storage](), [openai](), [platform-engineering](), [production](), [region](), [scale](), [service](), [sre](), [storage](), [teams]() ### AI overview Session 1 describes how a team closed the architecture for OpenAI's second Object Storage instance in the same physical region as its primary. The production system was designed for 15 Tbps of GPU-fleet traffic and a three-month delivery timeline, with major architecture decisions completed in one week. ### Source excerpt OpenAI wanted a second Object Storage instance, in customer-facing production, at 15 Tbps, in three months. Session 1 covers the first week: closing the architecture. ## Metering Object Storage and S3 Billing for SaaS DevFeed: [Metering Object Storage and S3 Billing for SaaS]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-05-18T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Amazon S3](), [Software as a service](), [cloud-infrastructure](), [Azure](), [Cloud](), [Google Cloud Platform (GCP)]() Tags: [architecture](), [aws](), [aws-s3](), [azure](), [cloud](), [google-cloud](), [guide](), [how-to](), [object-storage](), [s3](), [saas](), [storage](), [usage-based-billing]() ### AI overview This article explains how SaaS products can meter object storage across uploads, stored capacity, egress, and storage operations. It describes an architecture for handling rolling storage measurements, retries, partial failures, high volume, and different pricing models across AWS S3, Google Cloud Storage, Azure Blob Storage, and compatible object stores. ### Source excerpt Bill customers for storage uploads, capacity, and bandwidth across S3, GCS, and Azure Blob. Architecture for accurate object storage metering. ## Under the hood: Redpanda Cloud Topics architecture DevFeed: [Under the hood: Redpanda Cloud Topics architecture]() Original publisher: [Read original article]() Author: Tyler Rockwood Published: 2026-03-30T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Streaming](), [Replication](), [Amazon S3](), [Raft](), [Kafka]() Tags: [engineering](), [kafka](), [object-storage](), [raft](), [replication](), [streaming]() ### AI overview This article explains Redpanda Cloud Topics, which store record payloads in object storage while retaining partition metadata in Raft logs. It describes batching writes for object storage, preserving transaction and idempotency logic through Raft metadata, and the feature's general availability in Redpanda Streaming 26.1. ### Source excerpt Cloud Topics represents a new replication mechanism in our Redpanda streaming engine that cuts costs by using object storage. Here's how it actually works. [Next page]()