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Dynatrace supports SnapStart for Lambda as an AWS launch partner

Dynatrace

Today, application modernization efforts are centered on application programming interfaces and microservices that are sensitive to startup latency. Most enterprises use serverless functions as part of a broader hybrid environment, covering both cloud and traditional technologies. Simplify error analytics.

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IT modernization improves public health services at state human services agencies

Dynatrace

Program staff depend on the reliable functioning of critical program systems and infrastructure to provide the best service delivery to the communities and citizens HHS serves, from newborn infants to persons requiring health services to our oldest citizens. IT modernization can help.

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The Power of Integrated Analytics Within an IMDG

ScaleOut Software

ScaleOut StateServer® Pro Adds Analytics to In-Memory Data Grids . For more than fifteen years, ScaleOut StateServer® has demonstrated technology leadership as an in-memory data grid (IMDG) and distributed cache. It also transparently makes use of the IMDG’s scalable computing resources to accelerate the analysis.

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The Power of Integrated Analytics Within an IMDG

ScaleOut Software

ScaleOut StateServer® Pro Adds Analytics to In-Memory Data Grids . For more than fifteen years, ScaleOut StateServer® has demonstrated technology leadership as an in-memory data grid (IMDG) and distributed cache. It also transparently makes use of the IMDG’s scalable computing resources to accelerate the analysis.

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Using Real-Time Digital Twins for Aggregate Analytics

ScaleOut Software

When analyzing telemetry from a large population of data sources, such as a fleet of rental cars or IoT devices in “smart cities” deployments, it’s difficult if not impossible for conventional streaming analytics platforms to track the behavior of each individual data source and derive actionable information in real time. The list goes on.

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Using Real-Time Digital Twins for Aggregate Analytics

ScaleOut Software

When analyzing telemetry from a large population of data sources, such as a fleet of rental cars or IoT devices in “smart cities” deployments, it’s difficult if not impossible for conventional streaming analytics platforms to track the behavior of each individual data source and derive actionable information in real time. The list goes on.

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Why Use “Real-Time Digital Twins” for Streaming Analytics?

ScaleOut Software

And how are they different from streaming pipelines like Azure Stream Analytics and Apache Flink/Beam? What Problems Does Streaming Analytics Solve? To understand why we need real-time digital twins for streaming analytics, we first need to look at what problems are tackled by popular streaming platforms.