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AWS

Thursday, July 09, 2026 · 8 stories, curated & summarized — click any story for the source.

AWS What's New awsdatabase

AWS EC2 U7i high-memory instances now in Europe Zurich

Amazon EC2 U7i instances featuring 12TiB of DDR5 memory are now available in the AWS Europe (Zurich) region. Powered by fourth-generation Intel Xeon Scalable processors, these instances provide 896 vCPUs and support up to 100Gbps for both network and EBS bandwidth. This expansion targets mission-critical in-memory database workloads such as SAP HANA, Oracle, and SQL Server.

  • 12TiB DDR5 memory supports massive in-memory datasets for SAP HANA and Oracle.
  • 896 vCPUs on Sapphire Rapids deliver high transaction processing throughput.
  • 100Gbps EBS and network bandwidth accelerate data loading and backups.
  • ENA Express support ensures low-latency communication for critical apps.
BY THE NUMBERSMassive Memory for SAP HANA12TiBDDR5 RAM per instanceSupports mission-critical in-memory databases

AWS Security Hub now features Network Scanning to detect resources actually reachable from the internet by probing them directly. This active verification complements existing findings that only assess configuration risks like security group rules. The tool discovers public IPs, VMs, and load balancers across AWS and Azure environments, identifying open ports and running services.

  • Active scanning confirms real internet reachability, not just theoretical risk from config analysis.
  • Supports multi-cloud coverage, scanning both AWS and Azure resources for public exposure.
  • Generates specific Security Hub findings for each detected reachable port and service.
  • Reduces false positives by validating if resources are truly accessible from the public internet.
TRADE-OFFConfig Analysis vs Active ScanningConfig AnalysisTheoretical risk assessmentBased on security rulesHigh false positive rateActive ScanningReal internet reachabilityDirect probing verificationValidates actual exposurevs
AWS What's New awsdatabase

AWS Security Hub now monitors Microsoft Azure resources

AWS Security Hub has extended its unified security management capabilities to include Microsoft Azure environments. The service automatically discovers key Azure assets such as VMs, container images, Function Apps, and identities to evaluate them for misconfigurations and internet exposure. This update allows organizations to consolidate risk analytics, posture management, and vulnerability assessments into a single interface across both clouds.

  • Eliminates tool fragmentation by unifying AWS and Azure security monitoring in one console.
  • Auto-discovers Azure VMs, ACR images, Function Apps, and identities for continuous evaluation.
  • Enables holistic risk prioritization and consistent response actions across hybrid cloud workloads.
  • Centralizes cloud security posture management and vulnerability detection for multi-cloud fleets.
TRADE-OFFUnified Cloud SecurityBefore Security HubFragmented tool sprawl across cloudsManual asset discovery effortsInconsistent risk visibilityWith Multi-Cloud HubSingle console for AWS and AzureAuto-discovers VMs and identitiesHolistic risk prioritizationvs

AWS detailed how industrial-wearables maker ProGlove scaled its SaaS platform to operate over one million Lambda functions. The architecture spans thousands of dedicated customer accounts, leveraging AWS’s isolation models. This case study highlights strategies for managing massive concurrency and resource fragmentation in multi-tenant serverless environments.

  • ProGlove runs 1M+ Lambda functions across thousands of dedicated customer accounts.
  • AWS highlighted architectural patterns for handling extreme scale in multi-tenant SaaS.
  • Dedicated accounts provide isolation but require careful resource management strategies.
  • Serverless concurrency limits and cold starts are critical factors at this scale.
AWS What's New awsdatabase

AWS Redshift Graviton RG instances now on trailing track

Amazon Redshift has extended support for Graviton-based rg.4xlarge and rg.xlarge instances to the trailing track (P201). This update allows customers prioritizing stability to leverage AWS Graviton performance, which offers up to 2.4x faster query speeds than RA3 instances at a 30% lower price per vCPU. Deployment is supported via the console, CLI, and SDKs for both new clusters and existing resize operations.

  • Graviton RG instances are now available on the stable P201 trailing track.
  • Expect up to 2.4x faster query performance compared to RA3 instances.
  • Cost reduction of 30% per vCPU makes Graviton attractive for production.
  • Resize existing clusters or provision new ones using rg.xlarge/rg.4xlarge.
  • Provisioning supported via Console, AWS CLI, and SDKs.
BY THE NUMBERS30% Lower Cost Per vCPU30%Savings per vCPU vs RA3Faster queries and lower prices on P201 track

Amazon EMR Serverless now supports worker configurations up to 32 vCPUs and 244 GB of memory, doubling the previous maximum of 16 vCPUs and 120 GB. These larger instances are designed to improve runtime performance and cost efficiency for compute and memory-intensive Spark jobs. The update specifically addresses challenges like shuffle-heavy operations, data skew, and caching requirements by reducing data transfer overhead and lowering out-of-memory failure risks.

  • Max worker size doubled to 32 vCPUs and 244 GB RAM for heavier workloads
  • Shuffle-heavy jobs benefit from reduced inefficient data transfers between executors
  • Data skew scenarios see fewer out-of-memory failures due to increased memory headroom
  • Data caching performance improves as larger workers can hold more data in memory
  • Recommended for compute and memory-intensive Spark and Trino jobs
THE SHIFTEMR Serverless Worker Limits Doubled16 vCPUPREVIOUS MAX VCPU32 vCPUNEW MAX VCPUMemory also increased from 120GB to 244GB

Microsoft has integrated Hugging Face models into its Foundry Managed Compute service. This update allows users to deploy and run pre-trained models directly within the Foundry environment. The integration simplifies access to a wide range of AI capabilities for enterprise workloads.

  • Hugging Face models are now natively available on Microsoft Foundry Managed Compute
  • Simplifies deployment of pre-trained AI models within the Foundry ecosystem
  • Enables enterprise users to leverage community models without external infrastructure
  • Reduces friction for integrating open-source AI into existing data workflows

Hugging Face has partnered with SkyPilot to enable running AI workloads on any cloud provider while storing data in Hugging Face storage. This integration aims to eliminate egress fees by keeping data within the Hugging Face ecosystem regardless of the compute destination. The solution allows engineers to leverage multi-cloud flexibility without incurring data transfer costs.

  • Enables multi-cloud AI compute without data egress penalties.
  • Keeps datasets in Hugging Face storage during model training/inference.
  • Simplifies cross-cloud workflows via SkyPilot integration.
  • Reduces operational costs for large-scale model operations.