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Why applying chaos engineering to data-intensive applications matters

Dynatrace

Stream processing One approach to such a challenging scenario is stream processing, a computing paradigm and software architectural style for data-intensive software systems that emerged to cope with requirements for near real-time processing of massive amounts of data. We designed experimental scenarios inspired by chaos engineering.

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Site reliability engineering: Six SRE trends to unleash DevOps innovation

Dynatrace

Site reliability engineering (SRE) continues to gain popularity as organizations embrace hybrid cloud strategies and IT automation at scale. By applying software engineering principles to operations and infrastructure practices, SRE enables organizations to streamline and automate IT processes. Dynatrace news.

DevOps 147
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Five-nines availability: Always-on infrastructure delivers system availability during the holidays’ peak loads

Dynatrace

The nirvana state of system uptime at peak loads is known as “five-nines availability.” In its pursuit, IT teams hover over system performance dashboards hoping their preparations will deliver five nines—or even four nines—availability. How can IT teams deliver system availability under peak loads that will satisfy customers?

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LISA2019 Linux Systems Performance

Brendan Gregg

Systems performance is an effective discipline for performance analysis and tuning, and can help you find performance wins for your applications and the kernel. However, most of us are not performance or kernel engineers, and have limited time to study this topic.

Systems 144
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Why Tcl is 700% faster than Python for database benchmarking

HammerDB

Python is a popular programming language, especially for beginners, and consequently we see it occurring in places where it just shouldn’t be used, such as database benchmarking. We use stored procedures because, as the introductory post shows, using single SQL statements turns our database benchmark into a network test).

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Measuring the importance of data quality to causal AI success

Dynatrace

Traditional analytics and AI systems rely on statistical models to correlate events with possible causes. It removes much of the guesswork of untangling complex system issues and establishes with certainty why a problem occurred. That’s where causal AI can help. Causal AI is particularly effective in observability. Timeliness.

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Implementing service-level objectives to improve software quality

Dynatrace

Instead, they can ensure that services comport with the pre-established benchmarks. SLOs can be a great way for DevOps and infrastructure teams to use data and performance expectations to make decisions, such as whether to release and where engineers should focus their time. SLOs improve software quality. SLOs aid decision making.

Software 272