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Dynatrace unveils Security Analytics to elevate threat detection, forensics, and incident response

Dynatrace

A traditional log-based SIEM approach to security analytics may have served organizations well in simpler on-premises environments. As our experience with MOVEit shows, IoCs that remained hidden in logs alone quickly revealed themselves with observability runtime context data, such as metrics, traces, and spans.

Analytics 211
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Dynatrace extends contextual analytics and AIOps for open observability

Dynatrace

The result is that IT teams must often contend with metrics, logs, and traces that aren’t relevant to organizational business objectives—their challenge is to translate such unstructured data into actionable business insights. How can we optimize for performance and scalability? Does a certain issue impact a specific service?

Analytics 246
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Boost DevOps maturity with observability and a data lakehouse

Dynatrace

They’re unleashing the power of cloud-based analytics on large data sets to unlock the insights they and the business need to make smarter decisions. From a technical perspective, however, cloud-based analytics can be challenging. That’s especially true of the DevOps teams who must drive digital-fueled sustainable growth.

DevOps 177
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What is log management? How to tame distributed cloud system complexities

Dynatrace

Log management is an organization’s rules and policies for managing and enabling the creation, transmission, analysis, storage, and other tasks related to IT systems’ and applications’ log data. Metrics, logs , and traces make up three vital prongs of modern observability. Comparing log monitoring, log analytics, and log management.

Systems 180
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Perform 2023 Guide: Organizations mine efficiencies with automation, causal AI

Dynatrace

In what follows, we explore some key cloud observability trends in 2023, such as workflow automation and exploratory analytics. From data lakehouse to an analytics platform Traditionally, to gain true business insight, organizations had to make tradeoffs between accessing quality, real-time data and factors such as data storage costs.

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

Dynatrace

Dynatrace has developed the purpose-built data lakehouse, Grail , eliminating the need for separate management of indexes and storage. All data is readily accessible without storage tiers, such as costly solid-state drives (SSDs). No storage tiers, no archiving or retrieval from archives, and no indexing or reindexing.

Retail 230
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Causal AI use cases for modern observability that can transform any business

Dynatrace

The logs, metrics, traces, and other metadata that applications and infrastructure generate have historically been captured in separate data stores, creating poorly integrated data silos. Data lakehouses combine a data lake’s flexible storage with a data warehouse’s fast performance.