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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.

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Automated Testing in Data Engineering: An Imperative for Quality and Efficiency

DZone

In the data-driven landscape of today, automation has become indispensable across industries, not just to maximize efficiency but, more importantly, to ensure quality. This holds true for the critical field of data engineering as well. Automated testing methodologies are now imperative to deliver speed, accuracy, and integrity.

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Unlock the Power of DevSecOps with Newly Released Kubernetes Experience for Platform Engineering

Dynatrace

Platform engineering is on the rise. According to leading analyst firm Gartner, “80% of software engineering organizations will establish platform teams as internal providers of reusable services, components, and tools for application delivery…” by 2026. Automation, automation, automation.

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How platform engineering and IDP observability can accelerate developer velocity

Dynatrace

As organizations look to expand DevOps maturity, improve operational efficiency, and increase developer velocity, they are embracing platform engineering as a key driver. Platform engineering: Build for self-service Self-service deployment is a key attribute of platform engineering. “It makes them more productive.

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Automating Success: Building a better developer experience with platform engineering

Dynatrace

When it comes to platform engineering, not only does observability play a vital role in the success of organizations’ transformation journeys—it’s key to successful platform engineering initiatives. The various presenters in this session aligned platform engineering use cases with the software development lifecycle.

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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. IT pros need a data and analytics platform that doesn’t require sacrifices among speed, scale, and cost. What is a data lakehouse?

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Incremental Processing using Netflix Maestro and Apache Iceberg

The Netflix TechBlog

by Jun He , Yingyi Zhang , and Pawan Dixit Incremental processing is an approach to process new or changed data in workflows. The key advantage is that it only incrementally processes data that are newly added or updated to a dataset, instead of re-processing the complete dataset.