Category: Amazon S3 Tables

Customer Story | Amazon S3 Tables – Learn how Comcast redesigned their analytics layer with GraphQL and Amazon S3 Tables, scaling to 80x higher concurrent request volumes while cutting costs by 38% and reducing query latency by 28%. Comcast is building America’s largest and most intelligent broadband network with a simple purpose: connect everyone, everywhere […]

Customer Story | Amazon S3 Tables – Learn how Tubular Labs (part of Chartbeat Inc.) reclaimed 50% of engineering capacity by replacing fragile, file-based data pipelines with a Common Pipeline Runtime (CPR) built on Apache Iceberg and Amazon S3 Tables. By Tubular Labs Engineering (part of Chartbeat Inc.), in collaboration with the AWS Solution Architecture team […]

Customers managing large volumes of objects in Amazon Simple Storage Service (Amazon S3) often need to attach rich business context like compliance classifications, processing lineage, AI-generated labels, and more. Until now, this context lived in external databases or sidecar files that were stored as separate objects, which created complexity to manage and keep it up […]

Knowing who accessed your data, when, and how is the foundation for security investigations, compliance audits, cost attribution, and performance troubleshooting. Detailed access logs capture every request: who made it, which resource was accessed, and what response was returned. In practice, though, they arrive as semi-structured records spread across different locations. Turning them into actionable […]

Enterprise storage administrators manage hundreds of terabytes, and sometimes petabytes, of file data spanning business units, applications, and users. As that storage grows, so does the challenge of understanding what is actually stored in it. Administrators are asked to make capacity decisions, identify archive candidates, track storage costs, and support compliance reviews — but with […]

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 […]

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, […]

Organizations are rapidly adopting Apache Iceberg for their data lakes because it supports petabyte-scale growth and performance with the flexibility to evolve schemas and partitions without costly rewrites. Its architecture enables modern data lake management via features like time travel and incremental processing. However, managing Iceberg datasets efficiently can become a challenge over time as […]

The fast pace of the logistics industry necessitates real-time vehicle fleet tracking for maintaining a competitive edge and meeting customer expectations. Traditional methods often provide delayed or incomplete information, leading to inefficiencies and missed opportunities. The demand for accurate, up-to-the-minute data on vehicle locations, driver behavior, and route performance has never been higher. Companies need […]

As enterprises scale their adoption of generative AI, one of the biggest technical challenges is connecting AI applications to the right data and making that data fast, accessible, and secure. AI agents are transforming industries through applications like customer support automation, personalized e-commerce recommendations, and research assistance in financial services and healthcare. These applications require […]