AWS Storage Blog
Category: Amazon S3 Tables
Real-time fleet tracking using AWS IoT Core, Amazon S3 Tables, and Amazon Quick Sight
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 […]
Architecting high performance AI-driven data applications with Spice AI and AWS
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 […]
Building an open warehouse architecture: Supabase’s integration with Amazon S3 Tables
As applications scale, developers face a persistent challenge: analytical queries that slow down transactional databases, force them to copy data across multiple proprietary tools, and create disconnected data silos. For the 5 million developers building on Supabase, an open source Postgres development platform, this tension between operational and analytical workloads has become increasingly critical. The […]
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 […]
Derive intelligent storage insights using S3 Metadata and Model Context Protocol (MCP)
Organizations face mounting challenges in managing and operationalizing their ever-growing data assets for machine learning and analytics workflows. When dealing with billions and trillions of objects, teams struggle to find what data they have and how to efficiently find specific datasets. Without proper data discovery and metadata management, teams spend valuable time searching for relevant […]
How Zeta Global scales multi-tenant data ingestion with Amazon S3 Tables
Zeta Global is a data-driven marketing technology company that uses consumer insights to empower brands in customer acquisition, growth, and retention. At the core of its operations is the Zeta Marketing Platform, an advanced system that applies sophisticated AI and machine learning (ML) capabilities on proprietary data from over 245 million U.S. consumer profiles. This […]
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 […]
Implementing conversational AI for S3 Tables using Model Context Protocol (MCP)
In today’s data-driven world, the ability to interact with your data through natural language is becoming increasingly valuable. By combining the power of conversational AI with Amazon S3 Tables, organizations can democratize data access and enable individuals across technical skill levels to query, analyze, and gain insights from their data using simple conversations. Model Context […]
Query Amazon S3 Tables from open source Trino using Apache Iceberg REST endpoint
Organizations are increasingly focused on addressing the growing challenge of managing and analyzing vast data volumes, while making sure that their data teams have timely access to this data to enable rapid insights and decision-making. Data analysts and scientists need self-service analytics capabilities to build and maintain data products, often involving complex transformations and frequent […]
From raw to refined: building a data quality pipeline with AWS Glue and Amazon S3 Tables
Organizations often struggle to extract maximum value from their data lakes when running generative AI and analytics workloads due to data quality challenges. Although data lakes excel at storing massive amounts of raw, diverse data, they need robust governance and management practices to prevent common quality issues. Without proper data validation, cleansing processes, and ongoing […]
