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Efficient SLO event integration powers successful AIOps

Dynatrace

For instance, consider how fine-tuned failure rate detection can provide insights for comprehensive understanding. Please refer to How to fine-tune failure detection (dynatrace.com) for further information. Data Explorer “test your Metric Expression” for info result coming from the above metric.

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Crucial Redis Monitoring Metrics You Must Watch

Scalegrid

You will need to know which monitoring metrics for Redis to watch and a tool to monitor these critical server metrics to ensure its health. Redis returns a big list of database metrics when you run the info command on the Redis shell. You can pick a smart selection of relevant metrics from these.

Metrics 130
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Automate complex metric-related use cases with the Metrics API version 2

Dynatrace

Dynatrace collects a huge number of metrics for each OneAgent-monitored host in your environment. Depending on the types of technologies you’re running on individual hosts, the average number of metrics is about 500 per computational node. Running metric queries on a subset of entities for live monitoring and system overviews.

Metrics 226
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Why applying chaos engineering to data-intensive applications matters

Dynatrace

Such frameworks support software engineers in building highly scalable and efficient applications that process continuous data streams of massive volume. Kafka Streams takes longer to recover from failures and presents volatile behavior. Recovery time of the throughput metric. Recovery time of the latency p90.

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Announcing enterprise-grade observability at scale for your OpenTelemetry custom metrics

Dynatrace

As the application owner of an e-commerce application, for example, you can enrich the source code of your application with domain-specific knowledge by adding actionable semantics to collected performance or business metrics. New OpenTelemetry metrics exporters provide the broadest language support on the market.

Metrics 151
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Machine Learning for Fraud Detection in Streaming Services

The Netflix TechBlog

We present a systematic overview of the unexpected streaming behaviors together with a set of model-based and data-driven anomaly detection strategies to identify them. Data Featurization A complete list of features used in this work is presented in Table 1. The features mainly belong to two distinct classes.

C++ 312
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Migrating Critical Traffic At Scale with No Downtime?—?Part 2

The Netflix TechBlog

Our previous blog post presented replay traffic testing — a crucial instrument in our toolkit that allows us to implement these transformations with precision and reliability. By tracking metrics only at the level of service being updated, we might miss capturing deviations in broader end-to-end system functionality.

Traffic 279