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Site reliability done right: 5 SRE best practices that deliver on business objectives

Dynatrace

By automating and accelerating the service-level objective (SLO) validation process and quickly reacting to regressions in service-level indicators (SLIs), SREs can speed up software delivery and innovation. The growing amount of data processed at the network edge, where failures are more difficult to prevent, magnifies complexity.

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Implementing service-level objectives to improve software quality

Dynatrace

When organizations implement SLOs, they can improve software development processes and application performance. Stable, well-calibrated SLOs pave the way for teams to automate additional processes and testing throughout the software delivery lifecycle. Best practices for implementing service-level objectives. Reliability.

Software 259
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How Dynatrace boosts production resilience with Site Reliability Guardian

Dynatrace

To ensure high standards, it’s essential that your organization establish automated validations in an early phase of the software development process—ideally when code is written. These examples can help you define your starting point for establishing DevOps and SRE best practices in your organization.

DevOps 183
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Data Reprocessing Pipeline in Asset Management Platform @Netflix

The Netflix TechBlog

Hence we built the data pipeline that can be used to extract the existing assets metadata and process it specifically to each new use case. Existing data got updated to be backward compatible without impacting the existing running production traffic. For asynchronous processing, events are sent to Apache Kafka topics to be processed.

Media 237
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Automated observability, security, and reliability at scale

Dynatrace

As software development grows more complex, managing components using an automated onboarding process becomes increasingly important. Configuration as Code supports all the mechanisms and best practices of Git-based workflows, including pull requests, commit merging, and reviewer approval.

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Crucial Redis Monitoring Metrics You Must Watch

Scalegrid

Key Takeaways Critical performance indicators such as latency, CPU usage, memory utilization, hit rate, and number of connected clients/slaves/evictions must be monitored to maintain Redis’s high throughput and low latency capabilities. It can achieve impressive performance, handling up to 50 million operations per second.

Metrics 130
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Build and operate multicloud FaaS with enhanced, intelligent end-to-end observability

Dynatrace

For example, to handle traffic spikes and pay only for what they use. Scale automatically based on the demand and traffic patterns. Higher latency and cold start issues due to the initialization time of the functions. The elasticity of serverless services helps organizations scale as needed.