AWS Storage Blog
Tag: AWS Glue
Track healthcare data lineage in real time with Amazon S3 annotations
Organizations that store sensitive data need to answer a simple question: where did this data come from, and what happened to it? In healthcare, this isn’t optional. Regulations like HIPAA, GDPR, and SOX require organizations to produce this information on demand. When an auditor asks, for example, “Show me every transformation that touched patient record […]
Accelerate Amazon S3 Replication with automated S3 Batch Operations parallelization
As data volumes grow, organizations must move large datasets between storage locations to meet compliance requirements, optimize performance, and help meet data sovereignty requirements. However, migrating petabytes of data presents significant challenges: lengthy transfer times, complex coordination of parallel operations, data integrity verification, and substantial engineering overhead. Automation reduces resource consumption and operational complexity during […]
How Vanderbilt University scales digital archive discovery with Amazon S3 Metadata
Managing massive digital collections is hard. When you’re preserving decades of historical content and adding new materials daily, making that content discoverable matters more than the storage itself. Vanderbilt University Library discovered this firsthand while managing their extensive digital archives, including the renowned Vanderbilt Television News Archive (VTNA). Amazon S3 Metadata accelerates data discovery by […]
Amazon S3 audit logging, Part 1: Analyzing server access logs with Amazon Athena for performance insights
Organizations storing sensitive data must maintain complete visibility into how it’s accessed, by whom, and what changes occur over time. Regulatory frameworks demand detailed audit trails, security teams need rapid answers during investigations, and finance teams require granular cost attribution. Yet as data grows from terabytes to petabytes, the scale that makes centralized storage attractive […]
Enabling natural language access to structured data using Amazon S3 Tables and Amazon Bedrock Knowledge Bases
Organizations generate massive volumes of structured data from customer transactions, operational metrics, product catalogs, and compliance records. This data contains insights that can help businesses make better and timely decisions. Financial advisors need to review client transaction histories, retail analysts track inventory trends, and healthcare administrators monitor patient outcomes. Yet accessing these insights creates a […]
Accelerate Apache Hadoop and Apache Iceberg on Amazon S3 with the Analytics Accelerator Library
Organizations processing large-scale data for analytics, machine learning (ML), and business intelligence face a persistent challenge: how to access and read massive datasets quickly and cost-effectively. As data volumes grow exponentially, the performance of data access patterns becomes more critical. Inefficient read operations can lead to longer processing times, higher compute costs, and delayed insights, […]
Build intelligent ETL pipelines using AWS Model Context Protocol and Amazon Q
Data scientists and engineers spend hours writing complex data pipelines to extract, transform, and load (ETL) data from various sources into their data lakes for data integration and creating unified data models to build business insights. The process involves understanding the source and target systems, discovering schemas, mapping source and target, writing and testing ETL […]
Optimizing recommendations and analytics using Amazon DynamoDB and Amazon S3
Today, consumers navigate thousands of products on e-commerce sites, hundreds of shows on streaming platforms, and countless options in digital marketplaces. This choice overload creates decision fatigue, yet consumers continue to demand more variety and make more purchases online. As a result, personalization has become essential—consumers reward brands that deliver relevant, tailored online experiences. However, […]
Faster threat detection at scale: Real-time cybersecurity graph analytics with PuppyGraph and Amazon S3 Tables
Modern cybersecurity teams are facing unprecedented challenges in data analysis by the scale, complexity, and velocity of data. Cloud environments continuously generate massive amounts information in form of access logs, configuration changes, alerts, and telemetry. Traditional analysis methods of looking at these data points in isolation can’t effectively detect threats such as lateral movement and […]
Cloud-powered tick data: revolutionizing financial data storage with Amazon S3 and LSEG
Data has become the lifeblood of modern financial markets, driving everything from investment decisions to regulatory compliance. Nowhere is this more evident than in electronic trading, where the ability to efficiently store, process, and analyze historical market data can make the difference between success and failure. Market participants are witnessing an unprecedented surge in tick […]





