AWS Architecture Blog

Category: Industries

Field Notes: Tracking Overall Equipment Effectiveness with AWS IoT Analytics and Amazon QuickSight

This post was co-authored with Michael Brown, Senior Solutions Architect, Manufacturing at AWS. Overall equipment effectiveness (OEE) is a measure of how well a manufacturing operation is utilized (facilities, time and material) compared to its full potential, during the periods when it is scheduled to run. Measuring OEE provides a way to obtain actionable insights […]

Figure 5. Event registration and check-in

Using AWS Serverless to Power Event Management Applications

Most large events have common activities such as event registration, check-in upon arrival, and requesting of amenities. When designing applications, factors such as high availability, low latency, reliability, and security must be considered. In this blog post, we’d like to show how Amazon Web Services (AWS) can assist you in event planning activities. We’ll share […]

Figure 4. Machine to Cloud Connectivity (M2C2) Framework architecture

Securely Ingest Industrial Data to AWS via Machine to Cloud Solution

As a manufacturing enterprise, maximizing your operational efficiency and optimizing output are critical factors in this competitive global market. However, many manufacturers are unable to frequently collect data, link data together, and generate insights to help them optimize performance. Furthermore, decades of competing standards for connectivity have resulted in the lack of universal protocols to […]

Architecture for a multi-Region setup using a Region and Outpost

Ensure Workload Resiliency and Comply with Data Residency Requirements with AWS Outposts

Personally Identifiable Information (PII) and Personal Health Information (PHI) data are critical to the continued operation of healthcare organizations. Disaster recovery (DR) strategies must ensure that healthcare provider workloads with PII and PHI data operate with near-zero data loss recovery point objective (RPO) and recovery time objective (RTO). In most cases, an organization would typically […]

Figure 1. Synchronous container applications diagram

Scaling Data Analytics Containers with Event-based Lambda Functions

The marketing industry collects and uses data from various stages of the customer journey. When they analyze this data, they establish metrics and develop actionable insights that are then used to invest in customers and generate revenue. If you’re a data scientist or developer in the marketing industry, you likely often use containers for services […]

Field Notes: Deploy and Visualize ROS Bag Data on AWS using rviz and Webviz for Autonomous Driving

In the automotive industry, ROS bag files are frequently used to capture drive data from test vehicles configured with cameras, LIDAR, GPS, and other input devices. The data for each device is stored as a topic in the ROS bag file. Developers and engineers need to visualize and inspect the contents of ROS bag files to identify […]

Figure 1. On-premises and AWS queue integration for third-party services using AWS Lambda

Queue Integration with Third-party Services on AWS

Commercial off-the-shelf software and third-party services can present an integration challenge in event-driven workflows when they do not natively support AWS APIs. This is even more impactful when a workflow is subject to unpredicted usage spikes, and you want to increase decoupling and fault tolerance. Given the third-party nature of services, polling an Amazon Simple […]

Figure 1. Data pipeline architecture using AWS Services

Building a Data Pipeline for Tracking Sporting Events Using AWS Services

In an evolving world that is increasingly connected, data-centric, and fast-paced, the sports industry is no exception. Amazon Web Services (AWS) has been helping customers in the sports industry gain real-time insights through analytics. You can re-invent and reimagine the fan experience by tracking sports actions and activities. In this blog post, we will highlight […]

Figure 5. Amazon Redshift federated query with Amazon Redshift ML

Address Modernization Tradeoffs with Lake House Architecture

Many organizations are modernizing their applications to reduce costs and become more efficient. They must adapt to modern application requirements that provide 24×7 global access. The ability to scale up or down quickly to meet demand and process a large volume of data is critical. This is challenging while maintaining strict performance and availability. For […]

reference architecture - build automated scene detection pipeline - Autonomous Driving

Field Notes: Building an automated scene detection pipeline for Autonomous Driving – ADAS Workflow

This Field Notes blog post in 2020 explains how to build an Autonomous Driving Data Lake using this Reference Architecture. Many organizations face the challenge of ingesting, transforming, labeling, and cataloging massive amounts of data to develop automated driving systems. In this re:Invent session, we explored an architecture to solve this problem using Amazon EMR, Amazon […]