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What is log analytics? How a modern observability approach provides critical business insight

Dynatrace

What is log analytics? Log analytics is the process of viewing, interpreting, and querying log data so developers and IT teams can quickly detect and resolve application and system issues. This is also known as root-cause analysis. What are the use cases for log analytics? Peak performance analysis. Dynatrace news.

Analytics 214
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Conducting log analysis with an observability platform and full data context

Dynatrace

Modern organizations ingest petabytes of data daily, but legacy approaches to log analysis and management cannot accommodate this volume of data. Traditional log analysis evaluates logs and enables organizations to mitigate myriad risks and meet compliance regulations. Grail enables 100% precision insights into all stored data.

Analytics 187
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What is log analytics? How a modern observability approach provides critical business insight

Dynatrace

What is log analytics? Log analytics is the process of viewing, interpreting, and querying log data so developers and IT teams can quickly detect and resolve application and system issues. This is also known as root-cause analysis. What are the use cases for log analytics? Peak performance analysis. Dynatrace news.

Analytics 181
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Dynatrace and Red Hat expand enterprise observability to edge computing

Dynatrace

As an example, many retailers already leverage containerized workloads in-store to enhance customer experiences using video analytics or streamline inventory management using RFID tracking for improved security. These challenges stem from the distributed and often resource-constrained nature of edge computing.

Retail 258
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Pioneering customer-centric pricing models: Decoding ingest-centric vs. answer-centric pricing

Dynatrace

Customers experience delayed time to value from the data they’re ingesting as they must take additional steps to make the data useful for troubleshooting and analysis, such as re-indexing. Customers find themselves confined to models that limit their ability to leverage the volume of data they possess for practical analysis.

Retail 237
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Intelligent, context-aware AI analytics for all your custom metrics

Dynatrace

This gives you all the benefits of a metric storage system, including exploring and charting metrics, building dashboards, and alerting on anomalies. Let’s take the example of a globally distributed retailer that collects revenue measurements every minute for all its shops worldwide. This is exactly what Dynatrace now delivers.

Metrics 245
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Causal AI use cases for modern observability that can transform any business

Dynatrace

Software developers can use causal analysis to identify the root causes of bugs or application performance issues and to predict potential system failures or performance degradations. Retailers can analyze how factors such as demand, competition, and market trends affect pricing. Software development.