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The value of business events: How IT can increase business agility

Dynatrace

Business events are a special class of events, new to Business Analytics; together with Grail, our data lakehouse, they provide the precision and advanced analytics capabilities required by your most important business use cases. What are business events? This diagram shows a few examples of business events.

Analytics 217
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Extract metrics from business events to increase the value of business analytics

Dynatrace

Observability fault lines The monitoring of complex and dynamic IT systems includes real-time analysis of baselines, trends, and anomalies. To close these critical gaps, Dynatrace has defined a new class of events called business events.

Analytics 190
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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. In cloud-native environments, there can also be dozens of additional services and functions all generating data from user-driven events.

Systems 178
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New analytics capabilities for messaging system-related anomalies

Dynatrace

Messaging systems can significantly improve the reliability, performance, and scalability of the communication processes between applications and services. An example of a critical event-based messaging service for many businesses is adding a product to a shopping cart. Your messaging systems at a glance with the new Queues list.

Analytics 176
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What is security analytics?

Dynatrace

Teams can then act before attackers have the chance to compromise key data or bring down critical systems. Improved compliance A better understanding of data security across multiple applications and environments provides a unified view of events and information. Why is security analytics important?

Analytics 220
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How Red Hat and Dynatrace intelligently automate your production environment

Dynatrace

Integration with Red Hat Event-Driven-Ansible will also leverage Red Hat’s flexible rulebook system to map event data, such as problem categories or vulnerability identification, to the correct job template. Two implementation scenarios cover different use cases and needs.

DevOps 275
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Measuring the importance of data quality to causal AI success

Dynatrace

Traditional analytics and AI systems rely on statistical models to correlate events with possible causes. Causal AI is a different approach that goes beyond event correlations to understand the underlying reasons for trends and patterns. Fragmented and siloed data storage can create inconsistencies and redundancies.