Dynatrace
NOVEMBER 6, 2023
Azure observability and Azure data analytics are critical requirements amid the deluge of data in Azure cloud computing environments. Dynatrace recently announced the availability of its latest core innovations for customers running the Dynatrace® platform on Microsoft Azure, including Grail. Digital transformation 2.0
Dynatrace
MAY 3, 2024
Real-time streaming needs real-time analytics As enterprises move their workloads to cloud service providers like Amazon Web Services, the complexity of observing their workloads increases. Log data—the most verbose form of observability data, complementing other standardized signals like metrics and traces—is especially critical.
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Dynatrace
FEBRUARY 16, 2024
The growing complexity of modern multicloud environments has created a pressing need to converge observability and security analytics. Security analytics is a discipline within IT security that focuses on proactive threat prevention using data analysis. Clair determined what log data was available to her.
Dynatrace
AUGUST 9, 2023
The massive volumes of log data over months, sometimes years, of a breach have made this a complicated and expensive problem to solve. A traditional log-based SIEM approach to security analytics may have served organizations well in simpler on-premises environments. and “was any sensitive data stolen?”
Dynatrace
JANUARY 17, 2023
OpenTelemetry signals are often analyzed in data silos with missing context and relationships between the data and underlying topology. This leads to significant time wasted in connecting data with application workloads by manually applying labels, or by building crosslinks between the dashboards of incompatible tools.
Dynatrace
NOVEMBER 15, 2022
Following the launch of Dynatrace® Grail for Log Management and Analytics , we’re excited to announce a major update to our Business Analytics solution. Business events deliver the industry’s broadest, deepest, and easiest access to your critical business data. The need for real-time business observability.
Dynatrace
JANUARY 4, 2024
Traditional analytics and AI systems rely on statistical models to correlate events with possible causes. While this approach can be effective if the model is trained with a large amount of data, even in the best-case scenarios, it amounts to an informed guess, rather than a certainty. But to be successful, data quality is critical.
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