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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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DevOps automation: From event-driven automation to answer-driven automation [with causal AI]

Dynatrace

They need event-driven automation that not only responds to events and triggers but also analyzes and interprets the context to deliver precise and proactive actions. These initial automation endeavors paved the way for greater advancements, leading to the next evolution of event-driven automation.

DevOps 220
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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

Not only that, teams struggle to correlate events and alerts from a wide range of security tools, need to put them into context, and infer their risk for the business. In this blog post, we’ll use Dynatrace Security Analytics to go threat hunting, bringing together logs, traces, metrics, and, crucially, threat alerts.

Analytics 195
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Stream logs to Dynatrace with Amazon Data Firehose to boost your cloud-native journey

Dynatrace

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.

Cloud 217
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What is log management? How to tame distributed cloud system complexities

Dynatrace

In cloud-native environments, there can also be dozens of additional services and functions all generating data from user-driven events. Metrics, logs , and traces make up three vital prongs of modern observability. Comparing log monitoring, log analytics, and log management. These two processes feed into one another.

Systems 185
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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. Generally, this flow is used for small datasets.

Media 237