Category: Serverless

AWS Professional Services has partnered with the National Environmental Satellite, Data, and Information Service (NESDIS), part of the National Oceanic and Atmospheric Administration (NOAA), alongside GAMA-1 Technologies to broaden the scope and capabilities of the NESDIS Common Cloud Framework (NCCF). This work supports NOAA’s ability to ingest, manage, process, and disseminate critical environmental data. Read this post to learn more.

This post demonstrates how to build serverless workflows on AWS that process such data using AWS Step Functions and integrate with downstream systems that have concurrency limitations.

In this blog post, we’ll share how the Republic of Singapore’s Air Force, with the support of AWS, responded to a specific challenge: how to operate unmanned aerial vehicles (UAVs or drones) in a crowded, complex airspace.

Organizations require solutions for real time or near real time dashboards that can be provided to their customers without impacting their database performance or service level agreements (SLAs) to their end users. In this post, we showcase the integration of Amazon Web Services (AWS) capabilities to present an end-to-end architecture for this data flow.

Sharing data is essential for organizations to make informed decisions and foster innovation. Amazon Web Services (AWS) offers a variety of tools and services to help distribute data securely and at scale. Whether it’s sharing public data for the common good, monetizing private datasets for business purposes, or collaborating with colleagues, AWS provides the necessary infrastructure and support. Read this post to learn more.

Nonprofit organizations are typically frugal and responsible. They strive to improve the human condition in innumerable ways, yet they cannot raise capital like a commercial organization, so they have to make the most of the resources they have. They apply that frugal approach to IT: they build and operate only what they need to pursue their mission, and constantly innovate both to meet mission objectives and optimize cost. Even with these constraints, nonprofits aspire to solve some of the world’s biggest problems, and often, they use innovative IT architectures on Amazon Web Services (AWS) to do it.

In April 2020, during the COVID-19 pandemic, while frontline workers were under immense pressure, three experienced live event professionals wanted to thank all of the UK’s National Health Service (NHS) workers and paid care workers. They combined their extensive experience and knowledge to launch the not-for-profit charity Concerts for Carers, whose mission is to promote the mental health and well-being of all NHS workers and paid caregivers and to provide them with free tickets to live events in the UK as an ongoing gesture of thanks. This post highlights how they’ve used Amazon Web Services to meet their mission.

Cognitive Space is a leading Amazon Web Services (AWS) Partner delivering intelligent automation to satellite constellation operations using the CNTIENT platform. The system uses AWS-powered artificial intelligence (AI) decision making to handle highly complex and dynamic satellite tasking requirements, and demanding mission requirements. This blog post provides technical guidance for building and operating mission operation centers (MOCs) on AWS.

The World Meteorological Organization (WMO) wants to build and modernize a global weather framework with WMO Information Systems (WIS) 2.0 to enable and democratize unified access to critical, up-to-date weather data across the world. The WIS 2.0 system and the global cache provide a single point of access to improve the speed and accuracy with which forecasts can be generated while decreasing the time and capital requirements. This post describes the value of a global weather cache as well as the design and architecture for building the WIS 2.0 global weather cache on Amazon Web Services (AWS).

In this post, we cover an Amazon Web Services (AWS) Cloud infrastructure with a modular architecture that enables you to explore and take advantage of the benefits from different Retrieval-Augmented Generation (RAG)-based generative AI resources in a flexible way. This solution provides several benefits, along with faster time-to-market and shorter development cycles.