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AI techniques enhance and accelerate exploratory data analytics

Dynatrace

Increasingly, organizations seek to address these problems using AI techniques as part of their exploratory data analytics practices. The next challenge is harnessing additional AI techniques to make exploratory data analytics even easier. Start by asking yourself what’s there, whether it’s logs, metrics, or traces.

Analytics 210
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The top four log analytics and log management best practices

Dynatrace

By following key log analytics and log management best practices, teams can get more business value from their data. Challenges driving the need for log analytics and log management best practices As organizations undergo digital transformation and adopt more cloud computing techniques, data volume is proliferating.

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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 218
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Site-Speed Topography

CSS Wizardry

When first working on a new site-speed engagement, you need to work out quickly where the slowdowns, blindspots, and inefficiencies lie. Google Analytics can show us individual slow pages, but doesn’t necessarily help us build a bigger picture of the site as a whole. Higher variance means a less stable metric across pages.

Speed 292
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Unified observability delivers deeper insights with AI-driven analytics and automation

Dynatrace

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. Grail handles data storage, data management, and processes data at massive speed, scale, and cost efficiency,” Singh said.

Analytics 181
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Data lakehouse innovations advance the three pillars of observability for more collaborative analytics

Dynatrace

The short answer: The three pillars of observability—logs, metrics, and traces—converging on a data lakehouse. As teams try to gain insight into this data deluge, they have to balance the need for speed, data fidelity, and scale with capacity constraints and cost. Logs on Grail Log data is foundational for any IT analytics.

Analytics 186
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How unified data and analytics offers a new approach to software intelligence

Dynatrace

The only way to address these challenges is through observability data — logs, metrics, and traces. IT pros want a data and analytics solution that doesn’t require tradeoffs between speed, scale, and cost. The next frontier: Data and analytics-centric software intelligence. Enter Grail-powered data and analytics.

Analytics 191