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204 posts about AWS. Every summary links to the original.
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Amazon Bedrock is the managed reasoning tier in an agent architecture, not the whole system. This layered view shows the pipelines, governed tables, query layer, and guardrails around it.
CloudFormation is a declarative reconciler: you describe the target state in a template, and it provisions, updates, and reports on the stack that results. Here is the loop.
Amazon EVS runs VMware Cloud Foundation on AWS bare metal, so the design question becomes where each workload's data lives. This layered diagram shows how vSAN, FSx for ONTAP, and cross-Region DR divide the work.
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Launched in 2016, MongoDB Atlas was designed to bring MongoDB's developer experience to the cloud while eliminating operational overhead for production environments. Over ten years, Atlas expanded beyond a managed database into a multi-cloud data platform that supports multi-document ACID transactions, Queryable Encryption, native search, and stream processing. The platform introduced Atlas Vector Search in 2023 and acquired Voyage AI in 2025 to embed advanced embedding and reranking models natively. These integrated capabilities address the requirements of retrieval-augmented generation and autonomous AI agents without relying on disparate vector stores or sync pipelines. Atlas now serves more than three trillion queries daily across over 125 AWS, Google Cloud, and Microsoft Azure regions for nearly 70,000 customers.
Pinterest’s foundation ranking models store approximately 99% of their parameters in embedding tables, causing cross-node NCCL all-to-all communication to severely bottleneck multi-node distributed training. Initial multi-node scaling stalled at 1.21x on four nodes even after adopting AWS Elastic Fabric Adapter for OS-bypass networking. To resolve this, engineers profiled GPU activity with PyTorch Profiler and systematically addressed communication bottlenecks across multiple layers. The team implemented FP8 quantized communications using FBGEMM, balanced table sharding across GPUs, halved embedding dimensions while doubling row counts, and adopted a 2D parallel communication topology alongside PyTorch Distributed Checkpoint. These combined interventions achieved near-linear scaling factors of 3.9x on four nodes and 7.5x on eight nodes across 64 GPUs.
amazon.science
Amazon is investing in next-generation nuclear technology powered by tristructural isotropic (TRISO) fuel particles to satisfy the increasing energy demands of cloud computing and artificial intelligence infrastructure. Each sub-millimeter TRISO particle encloses a high-assay low-enriched uranium kernel within a porous carbon buffer and a three-layer ceramic shell featuring silicon carbide that acts as a robust miniature containment vessel. Testing by the U.S. Department of Energy demonstrated that irradiated particles withstand temperatures of 1600°C for 300 hours without detectable failures. In pebble-bed designs like the X-energy Xe-100 reactor, graphite spheres containing TRISO fuel circulate continuously through the core to enable online refueling, passive heat removal, and modular 80-megawatt deployments at facilities like the Cascade Energy Center.
Visa used ClickHouse Cloud to modernize reporting on Authorize.net payments data, replacing multi-day analyst-led turnaround with natural-language queries for internal teams. The solution pairs LibreChat, an open-source conversational interface, with the ClickHouse MCP server and runs on ClickHouse Cloud on AWS, allowing agents to explore and query a roughly 40 PB data footprint containing millions of rows. ClickHouse materialized views pre-compute and incrementally update results, reducing scanned data and bringing example analyses down to 100–200 milliseconds, with reported speedups of roughly 30–70 times. The platform also uses encrypted transfers, Amazon S3, VPC PrivateLink, change data capture, and federated access controls, while Visa reports identifying millions of dollars in revenue risk and reclaiming 8–10 hours per user each week.
Appcues replaced an analytics stack that included Snowflake and Airflow after rising scale pushed queries to 20 seconds or more and limited reports to 10-minute windows. The company evaluated ClickHouse Cloud on AWS for real-time segmentation, exporting Snowflake data to Parquet in S3 and testing complex email segments with nested conditions, historical events, and negative logic. Production events now pass through Kafka and a clickwriter consumer, while a clickclient library built on Elixir’s Ecto generates controlled ClickHouse queries; SharedReplacingMergeTree, table engines, and materialized views were tuned during a one-year migration. Across 1.31 PB and 410 billion events, P95 queries fell below two seconds, ingestion latency dropped to about five seconds, and analytics costs decreased 23%, while new funnel reports and AI-powered insights were added.
Postgres managed by ClickHouse, which entered public beta in May, received a broad update covering console operations, per-service RBAC, Terraform, Stripe provisioning, ClickPipes migrations, billing, capacity reservations, and extensions. The redesigned console adds service health, resource, and query views, while RBAC provides View and Manage roles across the console and public REST API; the alpha Terraform provider covers lifecycle operations including resizing, HA, replicas, restore, and password rotation. Postgres-to-Postgres ClickPipes now automate schema migration, usage is integrated into ClickHouse Cloud’s metering and billing, and AWS capacity reservations are allocated dynamically across availability zones. Underneath, pg_clickhouse and pg_stat_ch moved to the header-only clickhouse-c library, removing a C++ exception conflict, using Postgres memory contexts, and reducing the x86-64 Linux extension binary from 4.9 MB to 1.4 MB; the update shipped less than a month after beta, with Terraform and extensions still evolving.
Cisco Talos operates a threat-intelligence reputation service that classifies file hashes from Cisco product telemetry, enabling researchers to make security decisions about benign and potentially malicious artifacts. Previously, a custom pipeline moved data from Databricks Delta and S3 through EKS, where staging and aggregated tables supported an API; the self-managed design retained about a petabyte of compressed, replicated data on 32 high-compute nodes. The team rebuilt the system in four phases, using Terraform, SSO/SAML, sharded ClickPipes for a 12-month backfill, shadow traffic, and a validated cutover to ClickHouse Cloud on AWS. The resulting architecture uses EventBridge, SQS, ClickPipes, SharedMergeTree, object storage, and AWS PrivateLink, delivering less than a minute of queue lag, zero data loss, a 100% response match across more than 1,000 hashes, zero downtime, roughly 90% lower storage costs, and about 75% lower TCO while reclaiming over 300 engineer hours annually.
ClickHouse has achieved the AWS Retail Competency in the Advanced Data Insights category, a designation recognizing validated technical expertise and demonstrated customer success in retail real-time analytics. Retail workloads described here combine high-volume, high-cardinality, high-concurrency data with hard freshness requirements, including inventory, pricing, personalization, and peak seasonal demand. The post attributes the platform's fit to compressed columnar storage, parallel queries, continuous Kafka and Amazon S3 ingestion through ClickPipes, compute-compute separation, and AWS support for private networking, multi-region deployment, and Marketplace billing. Examples include Mercado Libre's 90-TB-per-day observability ingestion and trace lookups reduced from 15–20 minutes to about two seconds, while other customers report cost, speed, and analyst-efficiency gains.
AWS and Databricks describe their expanded collaboration at Data + AI Summit 2026, where AWS returns as a Legend Sponsor with sessions, demos, customer stories, and industry forums. The partnership centers on generative AI adoption, unified governance, and open data architectures, including an agentic stack that combines Amazon Bedrock, Bedrock AgentCore, Kiro, and the Databricks Data + AI Platform. A featured integration uses a governed MCP connection through Databricks Apps so AgentCore can query Unity Catalog-governed data, ask AI/BI Genie questions, and read low-latency state from Lakebase while honoring existing permissions. AWS will demonstrate these workflows at Booth #100 and present a session on federating Unity Catalog to AWS Glue, alongside customer examples including Mastercard, Talkdesk, nCino, Addepar, and Workday. Attendees can also join technical conversations, receptions, and a 14-day Databricks on AWS Marketplace trial with $400 in usage credits.
amazon.science
AWS has introduced M9g and M9gd EC2 instances powered by Graviton5 CPUs, featuring the formally verified Nitro Isolation Engine to enforce virtual machine isolation. To prove the engine's correctness, engineers developed 330,000 lines of machine-checked mathematics using the Isabelle/HOL proof assistant. The system architecture separates hypervisor policy from isolation mechanisms, requiring the deprivileged Nitro Hypervisor to route all guest state modifications through the minimal Rust-based engine. Functional verification uses a restricted language subset called micro Rust, Separation Logic contracts specifying total correctness, and weakest-precondition calculus from the open-source AutoCorrode library. Finally, high-level transition relations and noninterference proofs ensure that memory integrity and guest confidentiality are preserved across every execution step.
amazon.science
Amazon announced the general availability of M9g and M9gd EC2 instances powered by the new Graviton5 processor. Built on a three-nanometer process, Graviton5 features 192 Neoverse V3 cores, DDR5-8800 memory support, PCIe gen6 interconnects, and 192 megabytes of level-three cache. The chip transitions from seven discrete dies to four unified chiplets connected at 420 gigabytes per second, eliminating dedicated I/O and memory controller dies. These architecture updates deliver up to 25% higher computational performance over Graviton4, with up to 35% faster performance for web applications and machine learning inference. The new instances also introduce the Nitro Isolation Engine, which uses formal verification to enforce hardware and software isolation between virtual machines.
ClickHouse Cloud is benchmarked against Snowflake, Databricks, BigQuery, and Redshift on the join-heavy TPC-H workload, with the main comparison using SF100: 100 GB, 866M rows, and 22 queries. Using a single 59-core AWS compute node, the benchmark measures fastest hot runs from three executions with result caching disabled, applies public billing models and comparable Enterprise-tier regional pricing, and publishes scripts, queries, and result files for reproduction. ClickHouse completed SF100 in 19.8 seconds for $0.063 and ranked first on the cost-performance score, while Snowflake Large was faster at 15.9 seconds but cost $0.143; at SF10, ClickHouse ran the workload in 2.9 seconds for $0.009. The results are attributed to two years of join engineering, including faster hash joins, improved planning, correlated subquery support, lazy column replication, runtime filters, and statistics-based join reordering.
Anthropic says it has raised $65 billion in Series H funding at a $965 billion post-money valuation, with the round led by Altimeter Capital, Dragoneer, Greenoaks, and Sequoia Capital. The company says its run-rate revenue crossed $47 billion earlier this month and that the financing will support safety and interpretability research, additional compute, and expanded products and partnerships. It reports agreements for up to five gigawatts of new Amazon capacity, five gigawatts of next-generation TPU capacity with Google and Broadcom, and GPU access in SpaceX’s Colossus 1 and Colossus 2; AWS remains its primary cloud provider and training partner. The round includes $15 billion in previously committed hyperscaler investments, including $5 billion from Amazon, alongside strategic infrastructure partners Micron, Samsung, and SK hynix.
Slack evolved its Slack AI serving infrastructure across multiple phases to handle enterprise LLM workloads reliably and securely. The initial deployment on AWS SageMaker provided zero-knowledge escrow VPC isolation and FedRAMP compliance, but engineers faced scaling latency, GPU scarcity, and significant operational overhead. Slack then migrated live traffic to Amazon Bedrock to leverage managed Model Units and eliminate model release lag without customer-facing incidents. However, fixed Provisioned Throughput commitments and regional peak traffic variations created persistent underutilization challenges. Consequently, Slack expanded into a multi-cloud orchestration architecture that normalizes disparate provider APIs, integrates unified cross-cloud telemetry, and routes traffic dynamically around latency spikes and outages.
amazon.science
Traditional data center networks rely on hierarchical fat-tree topologies, which introduce overhead, upper-layer congestion, and vulnerability to router failures. Although flat random networks theoretically maximize route diversity and resilience, routing computation and cabling complexity historically made them impractical for physical data centers. AWS addressed these constraints by introducing Resilient Network Graphs, a quasi-random architecture powered by a passive optical device called ShuffleBox and a routing algorithm named Spraypoint. Spraypoint sprays packets across random neighbors toward designated waypoints, unlocking twice as many independent paths while operating within commodity router memory limits. Adopted as the default architecture across new AWS facilities, this design reduces router counts by 69%, lowers projected network power consumption by 40%, and increases throughput by up to 33%.
amazon.science
Amazon Research Awards has announced 70 award recipients across 49 universities in 11 countries for its fall 2025 funding cycle. The funded research spans six distinct tracks: AI for Information Security, Agentic AI, Automated Reasoning, AWS Cryptography, Cybersecurity and Anti-Abuse Technologies, and Sustainability. Each selected academic team receives unrestricted financial grants, AWS Promotional Credits to access specialized cloud AI and machine learning services, and access to more than 700 Amazon public datasets. Awardees are paired with an Amazon research contact who provides advisory support, consultation, and access to company training sessions. Furthermore, the program explicitly encourages recipients to publish their discoveries, deliver presentations at Amazon offices worldwide, and release associated software implementations under open-source licenses.
Yelp transitioned from a legacy customer support chatbot that relied on rigid phrase matching and static menus to a retrieval-augmented generation system. The updated architecture classifies incoming queries using a large language model to route users into one of five specialized workflows, including QA, Billing, Refund, Cancel, and Review. For general QA, the system uses FAISS to search an in-memory vectorstore built exclusively from article metadata such as titles, summaries, and headers rather than raw article text. Retrieved articles are supplied to the language model as prompt context, followed by automated validations for safety, character limits, and hallucinated hyperlinks. In production A/B testing, this metadata-driven design doubled the chatbot resolution rate while achieving approximately 94% recall@5.
The post explains why npm supply-chain attacks matter to Supabase projects: Edge Functions, the Supabase CLI, and packages including supabase-js, @supabase/ssr, and @supabase/server all depend on npm. Attackers can compromise maintainers, register typosquats, or exploit build pipelines; once malicious code reaches node_modules, lifecycle scripts may read environment variables, cloud metadata, tokens, kubeconfig, and SSH keys. The proposed defense is layered: upgrade to pnpm 11, increase minimumReleaseAge to several days, pin sensitive dependency versions, commit and review lockfiles, disable or allowlist install scripts, verify package names, and pin GitHub Actions to full commit SHAs. Supabase is publishing a canonical security guide, hardening its GitHub Actions, adding secret-handling warnings to API documentation, and coordinating communications; the post also recommends rotating credentials after a potentially compromised install and using scanners as supplementary defense.
Data teams at Yelp struggled to identify table consumers and track how datasets were accessed across their petabyte-scale data lake. To solve this, the team built a usage attribution system using Amazon S3 server access logs to visualize time-based partition keys against access timestamps and IAM roles. These visualizations revealed distinctive patterns for daily batch jobs, backfills, and ad hoc queries, guiding migrations of thousands of tables to Apache Iceberg format. Furthermore, Yelp implemented a Default Access Retention strategy gated by IAM bucket policies to prevent accidental queries from resetting S3 Intelligent Tiering transitions. Combining these granular usage insights with targeted S3 storage classes reduced Yelp's S3 storage costs by 33 percent.
Airbnb migrated its massive identity graph from a third-party graph database vendor to a unified, in-house knowledge graph platform to solve long-tail latency and scaling bottlenecks. Built on JanusGraph with AWS DynamoDB for persistence and OpenSearch for indexing, the new architecture decouples storage operations from graph traversal logic. The migration improved P99 query latency, eliminated routine instance reboots, and supported a tenfold increase in write throughput.
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