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The history of Grail: Why you need a data lakehouse

Dynatrace

Grail needs to support security data as well as business analytics data and use cases. With that in mind, Grail needs to achieve three main goals with minimal impact to cost: Cope with and manage an enormous amount of data —both on ingest and analytics. High-performance analytics—no indexing required. Start using Grail now.

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Our most critical mission: Adopting AI

Dynatrace

Artificial Intelligence (AI) is a complex, rapidly growing technology. In a recent FedScoop panel Brett Vaughn, Navy Chief AI Officer, and Willie Hicks, Federal CTO for Dynatrace discuss this up-and-coming technology including: Their definition of AI. Dynatrace news. How AI is used in the Navy.

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What is a data lakehouse? Combining data lakes and warehouses for the best of both worlds

Dynatrace

This approach enables organizations to use this data to build artificial intelligence (AI) and machine learning models from large volumes of disparate data sets. This data lands in its original, raw form without requiring schema definition. Support diverse analytics workloads. Massively parallel processing.

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How to evaluate modern APM solutions

Dynatrace

G2 Research includes a clear definition of APM in its Grid Report for Application Performance Monitoring for Spring 2021. Artificial intelligence for IT operations (AIOps) for applications. APM solutions track key software application performance metrics using monitoring software and telemetry data. Insight into business KPIs.

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RPA Vs Test Automation

Testsigma

While it is definitely true that both of these processes automate the process, “what” and “how” of their automation is entirely different. Surprisingly, this definition fits perfectly with RPA by making slight adjustments. The word “automation” seems to be a culprit in this case. What is Test Automation?

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What is APM?

Dynatrace

According to Gartner , “Application performance monitoring is a suite of monitoring software comprising digital experience monitoring (DEM), application discovery, tracing and diagnostics, and purpose-built artificial intelligence for IT operations.” User experience and business analytics.

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When should enterprises choose codeless automation for testing?

Testsigma

Instead, it relies on natural language processing , drag-and-drop UI elements , artificial intelligence, self-healing capabilities etc. Definitely, it comes with certain limitations and is not 100% perfect. Codeless testing is a type of software testing that requires no code writings from the testers. Conclusion.

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