Tag: Amazon S3

Modern data architectures often rely on separate databases for transactions, analytics, and AI, leading to extract, transform, and load (ETL) latency; data inconsistency; and rising costs. OceanBase addresses these challenges with a unified multimodel architecture that converges online transaction processing (OLTP), online analytical processing (OLAP), and vector retrieval within a single engine on Amazon Web Services (AWS).

Most AI systems today have no direct understanding of the physical world because the underlying spatial data requires purpose-built preprocessing before AI can use it. By using Wherobots, built on AWS, you can transform petabyte-scale spatial data in Amazon S3 into context that AI can reason about through distributed inference, a spatial query engine, and agentic AI integrations. Learn how customers like Leaf Agriculture, SatSure, and Aarden use Wherobots on AWS to operationalize earth observation data, accelerate spatial processing by up to 300 times, and build AI systems grounded in physical reality.

SAS Viya Workbench on AWS provides a unified cloud environment for Python, R, and SAS, letting teams focus on building models instead of managing infrastructure. Its split-plane architecture keeps your data securely within your AWS account while SAS handles orchestration. With elastic scalability, pre-configured environments, and built-in collaboration, teams can accelerate analytics innovation from day one.

OPSWAT’s MetaDefender Storage Security delivers automated file security for regulated financial institutions on AWS, combining Deep Content Disarm and Reconstruction with multi-scanning and data loss prevention capabilities. A major U.S. bank achieved 75% faster security update deployments while maintaining compliance with GDPR, HIPAA, and GLBA through containerized deployment on Amazon ECS and EKS with seamless S3 integration. Available through AWS Marketplace, the solution provides continuous monitoring, automated compliance reporting, and reduced operational costs for organizations managing sensitive data across multiple cloud regions.

Life sciences organizations invest significantly in real-world data (RWD), yet face persistent challenges in turning these investments into actionable insights. Panalgo’s IHD Cloud platform on AWS addresses these challenges by deploying advanced analytics capabilities directly within customers’ secure environments. Through this approach, organizations have reduced analysis time from months to days while maintaining strict data security and regulatory compliance standards.

The integration of Flywheel with Amazon S3 offers a powerful solution for streamlining medical imaging workflows. This combination enables healthcare organizations to efficiently manage, analyze, and share vast amounts of imaging data while significantly reducing costs and preparation time.

This blog explores how Cloud Native Qumulo (CNQ) on AWS enables organizations to modernize and scale their cloud-based file storage infrastructure. By leveraging AWS’s cloud-native architecture, CNQ ensures compatibility across hybrid environments, providing cost-efficient, high-performance storage solutions for demanding workloads, including AI, HPC, and large-scale data management.

RADCOM provides a cloud-native, AI-driven assurance solution for 5G networks on AWS, improving reliability, speed, and user experience. The flexible SaaS model lowers costs, allowing focus on technical tasks with adaptable, usage-based pricing. Discover how RADCOM ACE offers rapid, scalable analytics and automated life cycle management for 5G network assurance evolution on AWS.

In today’s digital landscape, the sheer volume of security data generated across organizations presents a significant challenge. To provide Amazon Web Services (AWS) customers with efficient, scalable, and user-friendly solutions in security data management, we’re excited to announce the Amazon Security Lake Ready Specialization.

Discover how EPAM’s Collaborative Omics Research Accelerator™ (CORA™), powered by AWS HealthOmics, enables life sciences organizations to accelerate omics research through scalable, cost-effective data analysis. Learn how this solution addresses the challenges of exponential genomic data growth while providing a user-friendly, secure, and GxP-ready environment for analyzing and correlating omics data at scale.