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Bringing Software Engineering Rigor to Data

DZone

In software engineering, we've learned that building robust and stable applications has a direct correlation with overall organization performance. The data community is striving to incorporate the core concepts of engineering rigor found in software communities but still has further to go. Posted with permission.

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Why applying chaos engineering to data-intensive applications matters

Dynatrace

The jobs executing such workloads are usually required to operate indefinitely on unbounded streams of continuous data and exhibit heterogeneous modes of failure as they run over long periods. Recovery time of the throughput metric. Performance is usually a primary concern when using stream processing frameworks.

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Sequential A/B Testing Keeps the World Streaming Netflix Part 1: Continuous Data

The Netflix TechBlog

Spot the Difference Can you spot any difference between the two data streams below? These observations are from a particular type of A/B test that Netflix runs called a software canary or regression-driven experiment. Can you spot any differences in the statistical distributions between the two data streams?

Testing 239
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Conducting log analysis with an observability platform and full data context

Dynatrace

Modern organizations ingest petabytes of data daily, but legacy approaches to log analysis and management cannot accommodate this volume of data. At Dynatrace Perform 2023 , Maciej Pawlowski, senior director of product management for infrastructure monitoring at Dynatrace, and a senior software engineer at a U.K.-based

Analytics 193
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How Red Hat and Dynatrace intelligently automate your production environment

Dynatrace

Problem remediation is too time-consuming According to the DevOps Automation Pulse Survey 2023 , on average, a software engineer takes nine hours to remediate a problem within a production application. Challenges organizations face in using observability and security data to drive automation. In-context topology identification.

DevOps 290
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Experimentation is a major focus of Data Science across Netflix

The Netflix TechBlog

Here we describe the role of Experimentation and A/B testing within the larger Data Science and Engineering organization at Netflix, including how our platform investments support running tests at scale while enabling innovation. Curious to learn more about other Data Science and Engineering functions at Netflix?

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Key Application Performance Metrics From the Viewpoint of a Statistician-Turned-Developer

DZone

Now that you’ve deployed your code, it’s time to monitor it, collect data, and analyze your metrics. The first step to gather this type of data is application monitoring. The first step to gather this type of data is application monitoring. Once you have data though, it’s important to analyze it correctly.

Metrics 246