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Metrics

three pillars of observability converge on the Grail data lakehouse

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

By Jane Mulcaster -
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Extract metrics from business events to increase the value of Business Analytics

By Daniel Marschnig -
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Dynatrace | OpenTelemetry

Support of OpenTelemetry metrics with enhanced AI capabilities

By Florian Ortner -
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AI-driven analysis of Spring Micrometer metrics in context, with topology at scale

By Sonja Chevre -
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Benefits of AIOps transform business operations.

Transform log data into actionable metrics and have Davis AI do the work for you

By Michal Dec -
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Metric Expressions explained

By Andreas Grabner -
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Automatic intelligent observability into your HashiCorp Consul service mesh

By James Kitson -
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What is Prometheus and 4 challenges for enterprise adoption

Simplify observability for all your custom metrics (Part 4: Prometheus)

By Alois Mayr -
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Simplify observability for all your custom metrics (Part 3: Scripting languages)

By Michael Kopp -
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Announcing enterprise-grade observability at scale for OpenTelemetry custom metrics (Part 2)

By Daniela Rabiser -
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Announcing enterprise-grade observability at scale for OpenTelemetry custom metrics (Part 1)

By Daniela Rabiser -
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Ingesting JMeter, temperature and humidity metrics: A Dynatrace innovation day report

By Andreas Grabner -
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Developer life

Automate complex metric-related use cases with the Metrics API version 2

By Florian Aigner -
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Giving data a heartbeat

By Ben Rushlo -
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Digital-Transformation

Integrate Dynatrace more easily using the new Metrics REST API

By Wolfgang Beer -
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Dynatrace employees

Multi-dimensional analysis views for service metrics and custom metrics

By Michael Kopp -
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