Remove Data Processing Remove Events Remove IoT Remove Risk
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Architectural Patterns for real-time analytics using Amazon Kinesis Data Streams, Part 2: AI Applications

AWS Big Data

Part 1 also contains architectural examples for building real-time applications for time series data and event-sourcing microservices. An event tracker provides an endpoint that allows you to stream interactions that occur in your application back to Amazon Personalize in near-real time. You do this by using the PutEvents API.

IoT 91
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Invoke AWS Lambda functions from cross-account Amazon Kinesis Data Streams

AWS Big Data

It also mitigates risks, improves scalability, and allows for advanced networking configurations. In a streaming architecture, you may have event producers, stream storage, and event consumers in a single account or spread across different accounts depending on your business and IT requirements.

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Customizing Personal Lines Insurance with Location Data

Cloudera

According to Statista , the projected installed base of IOT devices is expected to increase to 30.9 I recently attended one of Majesco’s excellent webinars hosted by Denise Garth, Chief Strategy Officer. Much of the evidence required in the past is already available from the IOT sensors. billion units that exist today.

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Sustainability trends: 5 issues to watch in 2024

IBM Big Data Hub

Investors, regulators and stakeholders are increasingly demanding that companies disclose their exposure to climate-related risks , such as dependence on fossil fuels or vulnerability to weather events. The goal is for there to be more nature by 2030 than there is today—which means taking actionable steps in 2024.

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Top 4 Ways to Improve Storage Performance and Increase Agility

CDW Research Hub

In today’s data-driven world, your storage architecture must be able to store, protect and manage all sources and types of data while scaling to manage the exponential growth of data created by IoT, videos, photos, files, and apps. This helps them accelerate time to market and easily deploy and manage their environments.

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The Data Behind Tokyo 2020: The Evolution of the Olympic Games

Sisense

Not only does it support the successful planning and delivery of each edition of the Games, but it also helps each successive OCOG to develop its own vision, to understand how a host city and its citizens can benefit from the long-lasting impact and legacy of the Games, and to manage the opportunities and risks created.

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Strengthening cybersecurity in life sciences with IBM and AWS

IBM Big Data Hub

With the growth in usage of digital technology and cloud in the life sciences industry, digital information is more readily available and at a greater risk for exploitation. The role of AWS and cloud security in life sciences However, with greater power comes great responsibility.