Category: AWS IoT Analytics

Introduction Visualizing IoT data that can vary significantly over short periods of time (seconds) is important for several reasons: exploration and discovery of patterns, assessing trends and cyclicity, as well as observing potential correlations and anomalies. Insightful time-series visualizations can help identify anomalies, raise alerts based on these anomalies, and improve communication between various stakeholders, […]

AWS CloudFormation support for AWS IoT Analytics resources was launched on December 18th, 2018. In this blog post, we introduce conventions for building IoT Analytics projects using CloudFormation and provide an array of sample templates to help you get started. As a refresher, every AWS IoT Analytics project has three required resources for data ingestion, […]

A question frequently asked by our customers in relation to our AWS IoT Analytics offering, is how to achieve a real-time dashboard of data flowing through IoT Analytics. In this blog, I’ll show you one pattern for achieving real-time metrics by utilizing Amazon CloudWatch and the AWS Lambda activity of a pipeline. I’ve simulated 25 […]

Our oil and gas customer wanted to deploy AWS IoT Analytics to help them:  Better understand their assets in the field (for example, pumps, generators, valve assemblies, and so on).  Derive actionable insights from their data.  Build a predictive maintenance solution to help reduce their costs. By using IoT Analytics, our customer can: Pre-process the […]

The interest in machine learning for industrial and manufacturing use cases on the edge is growing. Manufacturers need to know when a machine is about to fail so they can better plan for maintenance. For example, as a manufacturer, you might have a machine that is sensitive to various temperature, velocity, or pressure changes. When […]