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Best practices for Fluent Bit 3.0

Dynatrace

Fluent Bit is a telemetry agent designed to receive data (logs, traces, and metrics), process or modify it, and export it to a destination. However, you can also use Fluent Bit as a processor because you can perform various actions on the data. Ask yourself, how much data should Fluent Bit process? What’s new in Fluent Bit 3.0

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How Red Hat and Dynatrace intelligently automate your production environment

Dynatrace

A tight integration between Red Hat Ansible Automation Platform, Dynatrace Davis ® AI, and the Dynatrace observability and security platform enables closed-loop remediation to automate the process from: Detecting a problem. With DQL, the workflow trigger to initiate a required automation and remediation process can be defined.

DevOps 278
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Evolving from Rule-based Classifier: Machine Learning Powered Auto Remediation in Netflix Data…

The Netflix TechBlog

We have deployed Auto Remediation in production for handling memory configuration errors and unclassified errors of Spark jobs and observed its efficiency and effectiveness (e.g., For efficient error handling, Netflix developed an error classification service, called Pensive, which leverages a rule-based classifier for error classification.

Tuning 210
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Java memory optimizations: 3x Jenkins performance improvement with Dynatrace

Dynatrace

In the rest of the blog I walk you through the steps that allows our teams to identify a “memory hungry” Jenkins plugin and how the removal of this no longer needed plugin resulted in an overall 3x improvement of Jenkins performance. As a negative side effect, the team ran into a JVM bug which they could workaround though.

Java 254
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The history of Grail: Why you need a data lakehouse

Dynatrace

As a result, we created Grail with three different building blocks, each serving a special duty: Ingest and process. Ingest and process with Grail. This starts with a highly efficient ingestion pipeline that supports adding hundreds of petabytes daily. High-performance analytics—no indexing required. Retain data.

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Conducting log analysis with an observability platform and full data context

Dynatrace

With more automated approaches to log monitoring and log analysis, however, organizations can gain visibility into their applications and infrastructure efficiently and with greater precision—even as cloud environments grow. ” A data warehouse, on the other hand, is an efficient and fast option for querying data.

Analytics 187
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Dynatrace observability is now available for Red Hat OpenShift on the IBM® Power® architecture

Dynatrace

By leveraging the Dynatrace Operator and Dynatrace capabilities on Red Hat OpenShift on IBM Power, customers can accelerate their modernization to hybrid cloud and increase operational efficiencies with greater visibility across the full stack from hardware through application processes.