Fri.Oct 14, 2022

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Getting answers from data starts with automated log acquisition, at any scale

Dynatrace

Log data provides a unique source of truth for debugging applications, optimizing infrastructure, and investigating security incidents. To further enrich log data for automated observability, it’s necessary to dynamically tie logs to distributed traces on the code level, user sessions in the app front-end, and the topology of your IT landscape. This contextualization of log data enables AI-powered problem detection and root cause analysis at scale.

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Intermodular Analysis of C and C++ Projects in Detail (Part 1)

DZone

Starting from PVS-Studio 7.14, the C and C++ analyzer has supported intermodular analysis. In this two-part article, we'll describe how similar mechanisms are arranged in compilers and reveal some technical details of how we implemented intermodular analysis in our static analyzer. Preface Before we inspect intermodular analysis, let's remember how compilation works in the C and C++ world.

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Dynatrace Managed release notes version 1.252

Dynatrace

We have released Dynatrace Managed version 1.252. To learn what’s new, have a look at the release notes. The post Dynatrace Managed release notes version 1.252 appeared first on Dynatrace news.

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HammerDB on Linux driving MicrosoftSQL SQLserver on Windows

n0derunner

Often it’s nice to be able to drive Windows applications and databases from Linux, especially if you are more comfortable in a Unix environment. This post will show you how to drive a Microsoft SQL Server database running on a Windows server from a remote Linux machine. In this example I am using Ubuntu 22.04, … The post HammerDB on Linux driving MicrosoftSQL SQLserver on Windows appeared first on n0derunner.

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What is artificial intelligence? See how it differs from machine learning in IT ops

Dynatrace

As more organizations are moving from monolithic architectures to cloud architectures, the complexity continues to increase. These systems are generating more data than ever, and teams simply can’t keep up with a manual approach. Therefore, organizations are increasingly turning to artificial intelligence and machine learning technologies to get analytical insights from their growing volumes of data.