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Dynatrace unveils Security Analytics to elevate threat detection, forensics, and incident response

Dynatrace

A traditional log-based SIEM approach to security analytics may have served organizations well in simpler on-premises environments. As our experience with MOVEit shows, IoCs that remained hidden in logs alone quickly revealed themselves with observability runtime context data, such as metrics, traces, and spans.

Analytics 223
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Unified observability delivers deeper insights with AI-driven analytics and automation

Dynatrace

Today’s organizations flock to multicloud environments for myriad reasons, including increased scalability, agility, and performance. With unified observability and security, organizations can protect their data and avoid tool sprawl with a single platform that delivers AI-driven analytics and intelligent automation.

Analytics 186
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Dynatrace extends contextual analytics and AIOps for open observability

Dynatrace

The complexity of such deployments has accelerated with the adoption of emerging, open-source technologies that generate telemetry data, which is exploding in terms of volume, speed, and cardinality. How can we optimize for performance and scalability? Dynatrace extends its unique topology-based analytics and AIOps approach.

Analytics 246
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Easily monitor IBM i with updated Dynatrace extension

Dynatrace

It is based on the IBM AS/400 system and is known for its reliability, scalability, and security features. Messages overview Monitor disks and disk pool utilization One of the most important functions of your mainframe infrastructure is reading and writing data at high speeds while making it readily available.

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Microsoft Azure digital transformation: Embracing the power of cloud technology with next-gen observability

Dynatrace

This massive migration is critical to organizations’ digital transformation , placing cloud technology front and center and elevating the need for greater visibility, efficiency, and scalability delivered by a unified observability and security platform. The speed of change is only going to accelerate, thus requiring more innovation.

Azure 177
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Enhance data management with Grail: Ultimate guide to custom buckets and security policies

Dynatrace

Table name Default bucket logs default_logs events default_events metrics default_metrics bizevents default_bizevents dt.system.events dt_system_events entities spans (in the future) The default buckets let you ingest data immediately, but you can also create additional custom buckets to make the most of Grail.

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The history of Grail: Why you need a data lakehouse

Dynatrace

These technologies are poorly suited to address the needs of modern enterprises—getting real value from data beyond isolated metrics. Grail needs to support security data as well as business analytics data and use cases. From the beginning, Grail was built to be fast and scalable to manage massive volumes of data.