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Eliminate inefficiencies and innovate faster by optimizing hybrid mainframe environments on IBM Z

Dynatrace

Unlike other solutions that attempt to connect disparate tools to stitch together a business transaction, the Dynatrace software intelligence platform provides end-to-end transaction visibility by automatically discovering and mapping each transaction with our AI-powered solution.

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Dynatrace supports SnapStart for Lambda as an AWS launch partner

Dynatrace

Lambda serverless functions help developers innovate faster, scale easier, and reduce operational overhead, removing the burden of managing underlying infrastructure when updating and deploying code. The latest Amazon Lambda innovation, Lambda SnapStart, has day one support from Dynatrace. What is Lambda? How does Dynatrace help?

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How multicloud observability boosts cloud performance at Tractor Supply Co.

Dynatrace

shifted most of its ecommerce and enterprise analytics workloads to Kubernetes-managed software containers running in Microsoft Azure. “Caching’s one of the key components of any commerce application,” as it has a major impact on performance, Bollampally said. ” Three years ago, Tractor Supply Co.

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Key Advantages of DBMS for Efficient Data Management

Scalegrid

It comprises a collection of interrelated data and a set of software tools that aid in the access, processing, and management of data. Join the revolution with ScaleGrid’s DBaaS – where efficiency meets innovation. It provides tools for organizing and retrieving data efficiently. What is the main advantage of using a database?

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The Amazing Evolution of In-Memory Computing

ScaleOut Software

From Distributed Caches to Real-Time Digital Twins. Harnessing the power of in-memory computing requires software platforms that can make in-memory computing’s scalability readily available to applications using APIs while hiding the complexity of its implementation.

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The Amazing Evolution of In-Memory Computing

ScaleOut Software

From Distributed Caches to Real-Time Digital Twins. Harnessing the power of in-memory computing requires software platforms that can make in-memory computing’s scalability readily available to applications using APIs while hiding the complexity of its implementation.

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The case for a learned sorting algorithm

The Morning Paper

But what about systems software? Radix Sort is carefully designed to make effective use of the L2 cache and sequential memory accesses, whereas Learned Sort is making random accesses all over the destination array. How can learned sort be adapted to make it cache-efficient? Sympathy for the machine. Linear splines result in 2.7x

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