Development Scalability Software Software Engineering 
Dynatrace
NOVEMBER 3, 2023
With growing multicloud complexity and the need for organization-wide scalability, self-service and automation capabilities have become increasingly essential for developer productivity. In response to this shift, platform engineering is growing in popularity. The result is a cloud-native approach to software delivery.
DZone
JANUARY 19, 2024
In the dynamic world of online services, the concept of site reliability engineering (SRE) has risen as a pivotal discipline, ensuring that large-scale systems maintain their performance and reliability.
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DZone
SEPTEMBER 19, 2021
We are well aware of what is meant by system scalability. System scalability is about maintaining the SLA of the system as the user base continues to grow and as the user activity continues to rise. However, to build highly successful products, this is not the only type of scalability that we should worry about. Introduction.
Dynatrace
MAY 23, 2024
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. However, we noticed that GPT 3.5
Dynatrace
NOVEMBER 7, 2023
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.
The Netflix TechBlog
JUNE 6, 2022
By Astha Singhal , Lakshmi Sudheer , Julia Knecht The Application Security teams at Netflix are responsible for securing the software footprint that we create to run the Netflix product, the Netflix studio, and the business. Our customers are product and engineering teams at Netflix that build these software services and platforms.
The Netflix TechBlog
AUGUST 11, 2020
These methods can provide rich information for decision making, such as in experimentation platforms (“XP”) or in algorithmic policy engines. We want to amplify the effectiveness of our researchers by providing them software that can estimate causal effects models efficiently, and can integrate causal effects into large engineering systems.
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