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Automating DevOps practices fuels speed and quality

Dynatrace

Still, while DevOps practices enable developer agility and speed as well as better code quality, they can also introduce complexity and data silos. More seamless handoffs between tasks in the toolchain can improve DevOps efficiency, software development innovation, and better code quality. Gaining speed without sacrificing quality.

DevOps 290
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New continuous compliance requirements drive the need to converge observability and security

Dynatrace

I realized that our platforms unique ability to contextualize security events, metrics, logs, traces, and user behavior could revolutionize the security domain by converging observability and security. Collect observability and security data user behavior, metrics, events, logs, traces (UMELT) once, store it together and analyze in context.

Analytics 289
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Helping customers unlock the Power of Possible

Dynatrace

The Dynatrace platform automatically captures and maps metrics, logs, traces, events, user experience data, and security signals into a single datastore, performing contextual analytics through a “power of three AI”—combining causal, predictive, and generative AI. What’s behind it all? The result?

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

Dynatrace

They now use modern observability to monitor expanding cloud environments in order to operate more efficiently, innovate faster and more securely, and to deliver consistently better business results. Check out the guide from last year’s event. A data lakehouse eliminates team silos and delivers faster, high-quality insights.

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Introducing Impressions at Netflix

The Netflix TechBlog

Collecting Raw Impression Events As Netflix members explore our platform, their interactions with the user interface spark a vast array of raw events. These events are promptly relayed from the client side to our servers, entering a centralized event processing queue.

Tuning 166
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RabbitMQ vs. Kafka: Key Differences

Scalegrid

RabbitMQ is designed for flexible routing and message reliability, while Kafka handles high-throughput event streaming and real-time data processing. Kafka is optimized for high-throughput event streaming , excelling in real-time analytics and large-scale data ingestion. What is RabbitMQ? What is Apache Kafka?

Latency 147
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Transform data into insights with Dynatrace Dashboards and Notebooks

Dynatrace

Our latest enhancements to the Dynatrace Dashboards and Notebooks apps make learning DQL optional in your day-to-day work, speeding up your troubleshooting and optimization tasks. This efficient method allows you to easily browse and identify the appropriate metrics; adding them to your notebooks and dashboards requires just a single click.