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Enhancing Azure data analytics and Azure observability with Dynatrace Grail

Dynatrace

Azure observability and Azure data analytics are critical requirements amid the deluge of data in Azure cloud computing environments. As digital transformation accelerates and more organizations are migrating workloads to Azure and other cloud environments, they need observability and data analytics capabilities that can keep pace.

Azure 180
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What is log analytics? How a modern observability approach provides critical business insight

Dynatrace

What is log analytics? Log analytics is the process of viewing, interpreting, and querying log data so developers and IT teams can quickly detect and resolve application and system issues. In what follows, we explore log analytics benefits and challenges, as well as a modern observability approach to log analytics.

Analytics 215
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What is log analytics? How a modern observability approach provides critical business insight

Dynatrace

What is log analytics? Log analytics is the process of viewing, interpreting, and querying log data so developers and IT teams can quickly detect and resolve application and system issues. In what follows, we explore log analytics benefits and challenges, as well as a modern observability approach to log analytics.

Analytics 181
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How to leverage mobile analytics to ensure crash-free, five-star mobile applications

Dynatrace

Mobile analytics can help organizations optimize their mobile application performance, earning customer accolades and increasing revenue in the process. Learn how one Dynatrace customer leveraged mobile analytics to ensure a crash-free, five-star mobile application. The business value of mobile applications.

Mobile 211
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Mitigating risk with AI observability: Dynatrace empowers organizations to embrace AI for all use cases

Dynatrace

“By packaging [these capabilities] into hypermodal AI, we are able to run deep custom analytics use cases in sixty seconds or less.” Performance analytics Dynatrace hypermodal AI empowers development teams to dig deep into database statements and remediate issues quickly. But contextual analytics don’t stop here. “AI

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Boost DevOps maturity with observability and a data lakehouse

Dynatrace

They’re unleashing the power of cloud-based analytics on large data sets to unlock the insights they and the business need to make smarter decisions. From a technical perspective, however, cloud-based analytics can be challenging. All these steps and stages slow down processes, are error-prone, and introduce additional security risks.

DevOps 184
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Why applying chaos engineering to data-intensive applications matters

Dynatrace

Stream processing One approach to such a challenging scenario is stream processing, a computing paradigm and software architectural style for data-intensive software systems that emerged to cope with requirements for near real-time processing of massive amounts of data. This significantly increases event latency.