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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 206
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Expanded Grail data lakehouse and new Dynatrace user experience unlock boundless analytics

Dynatrace

Grail – the foundation of exploratory analytics Grail can already store and process log and business events. Now we’re adding Smartscape to DQL and two new data sources to Grail: Metrics on Grail and Traces on Grail. With Dynatrace and Smartscape for DQL, metrics are a completely different game.

Analytics 226
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Create compelling insights into business and operational KPIs through metric calculations in the Data explorer

Dynatrace

Metrics matter. But without complex analytics to make sense of them in context, metrics are often too raw to be useful on their own. To achieve relevant insights, raw metrics typically need to be processed through filtering, aggregation, or arithmetic operations. Examples of metric calculations. Dynatrace news.

Metrics 245
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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. In what follows, we explore log analytics benefits and challenges, as well as a modern observability approach to log analytics.

Analytics 211
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Flexible, scalable, self-service Kubernetes native observability now in General Availability

Dynatrace

From a cost perspective, internal customers waste valuable time sending tickets to operations teams asking for metrics, logs, and traces to be enabled. A team looking for metrics, traces, and logs no longer needs to file a ticket to get their app monitored in their own environments. This approach is costly and error prone.

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Extending modern observability for exploratory analytics

Dynatrace

A modern observability and analytics platform brings data silos together and facilitates collaboration and better decision-making among teams. Further, it presents data in intuitive, user-friendly ways to enable data gathering, analysis, and collaboration among far-flung teams. Here are some examples: IT infrastructure and operations.

Analytics 130
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Automate complex metric-related use cases with the Metrics API version 2

Dynatrace

Dynatrace collects a huge number of metrics for each OneAgent-monitored host in your environment. Depending on the types of technologies you’re running on individual hosts, the average number of metrics is about 500 per computational node. Running metric queries on a subset of entities for live monitoring and system overviews.

Metrics 224