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Unmatched scalability and security of Dynatrace extensions now available for all supported technologies: 7 reasons to migrate your JMX and Python plugins

Dynatrace

focused on technology coverage, building on the flexibility of JMX for Java and Python-based coded extensions for everything else. Python code also carries limited scalability and the burden of governing its security in production environments and lifecycle management. Dynatrace Extensions 1.0 Extensions 2.0

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Understand and replay Android app crashes with Session Replay

Dynatrace

From banking and retail to healthcare and government, nearly all industries have experienced a dramatic shift to mobile computing over the last decade. Another nifty Session Replay feature is the ability to capture custom events—events that are not typically captured by default—irrespective of whether the codebase is Java or Kotlin.

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Log auditing and log forensics benefit from converging observability and security data

Dynatrace

(for example, query topology to cross reference entity information to narrow down attacked services to those that are running Java) To what extent could we be compromised? for example, collate which and how many Java applications were attacked) Did we lose any critical data?

Analytics 185
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Citrix monitoring with Dynatrace: Easily observe your entire Citrix ecosystem

Dynatrace

Citrix is critical infrastructure For businesses operating in industries with strict regulations, such as healthcare, banking, or government, Citrix virtual apps and virtual desktops are essential for simplified infrastructure management, secure application delivery, and compliance requirements.

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OTel contributor Q&A: Dynatrace works to ensure enterprise readiness for OpenTelemetry

Dynatrace

This is why Dynatrace has a dedicated team working on OpenTelemetry that has been deeply involved since the beginning of the project, with members on both the Governance Committee and the Technical Committee. Contributor insights: An interview with OpenTelemetry committee members. years ago.

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Data Movement in Netflix Studio via Data Mesh

The Netflix TechBlog

The paradigm spans across methods, tools, and technologies and is usually defined in contrast to analytical reporting and predictive modeling which are more strategic (vs. CDC events can also be sent to Data Mesh via a Java Client Producer Library. tactical) in nature. Currently Iceberg sink is appended only.

Big Data 253
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The top eight DevSecOps trends in 2022

Dynatrace

Log4Shell enables an attacker to use remote code execution to engage with software that uses the Java logging library Log4j versions 2.0 This approach enables teams to apply automation to their testing, delivery, deployment, and governance. and 2.14.1. Unlike MLOps, AIOps doesn’t require training of data.