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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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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. Grail combines the big-data storage of a data warehouse with the analytical flexibility of a data lake. With Grail, we have reinvented analytics for converged observability and security data,” Greifeneder says.

Analytics 186
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TTP-based threat hunting with Dynatrace Security Analytics and Falco Alerts solves alert noise

Dynatrace

In this blog post, we’ll use Dynatrace Security Analytics to go threat hunting, bringing together logs, traces, metrics, and, crucially, threat alerts. Dynatrace Grail  is a data lakehouse that provides context-rich analytics capabilities for observability, security, and business data.

Analytics 195
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Data Reprocessing Pipeline in Asset Management Platform @Netflix

The Netflix TechBlog

Production Use Cases Real-Time APIs (backed by the Cassandra database) for asset metadata access don’t fit analytics use cases by data science or machine learning teams. Existing data got updated to be backward compatible without impacting the existing running production traffic.

Media 237
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The road to observability demo part 3: Collect, instrument, and analyze telemetry data automatically with Dynatrace

Dynatrace

Making applications observable—relying on metrics, logs, and traces to understand what software is doing and how it’s performing—has become increasingly important as workloads are shifting to multicloud environments. We also introduced our demo app and explained how to define the metrics and traces it uses.

Metrics 171
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MySQL Key Performance Indicators (KPI) With PMM

Percona

As a MySQL database administrator, keeping a close eye on the performance of your MySQL server is crucial to ensure optimal database operations. This includes metrics such as query execution time, the number of queries executed per second, and the utilization of query cache and adaptive hash index.

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Kubernetes vs Docker: What’s the difference?

Dynatrace

This opens the door to auto-scalable applications, which effortlessly matches the demands of rapidly growing and varying user traffic. Containers can be replicated or deleted on the fly to meet varying end-user traffic. In production, containers are easy to replicate. What is Docker? Networking. Observability.