Dynatrace
DECEMBER 27, 2022
Instead, they can ensure that services comport with the pre-established benchmarks. When organizations implement SLOs, they can improve software development processes and application performance. SLOs improve software quality. SLOs promote automation. SLOs minimize downtime.
HammerDB
JANUARY 5, 2019
Some opinions claim that “Benchmarks are meaningless”, “benchmarks are irrelevant” or “benchmarks are nothing like your real applications” However for others “Benchmarks matter,” as they “account for the processing architecture and speed, memory, storage subsystems and the database engine.”
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Dynatrace
NOVEMBER 11, 2020
Dynatrace OneAgent deployment and life-cycle management are already widely considered to be industry benchmarks for reliability and efficiency. In this article we’ll share highlights about two increments that are likely to fall into the “barely noticeable” category. Dynatrace news. No problem.
Testsigma
MAY 19, 2020
Software testing is the process of finding bugs or discrepancies in a software. Even though the job of a software tester is to catch errors committed by developers, software testers themselves are not immune to errors. Check the article here on how to choose the right test automation tool. Introduction.
Percona
APRIL 12, 2023
We’re equally convinced on both counts, but in this blog article, we’re focusing on why MongoDB is not open source. Let’s start with this: MongoDB is accurately referred to as source-available software. We agree with the OSI’s determination that any software under the SSPL cannot be open source.
Scalegrid
DECEMBER 6, 2023
Traditional self-managed ones give organizations full control over their database infrastructure, such as picking the software and scaling it up. These databases require significant time commitment along with necessary technical skills plus hardware & software costs, all of which are without dedicated team assistance.
ACM Sigarch
OCTOBER 12, 2020
have pointed out in their recent Science article (“There’s plenty of room at the Top”) — and cited by Hennessy in his Turing lecture — a naive implementation of matrix-matrix multiply in Python runs between 100x and 60,000x slower than their counterparts written in a “metal” language (highly optimized C).
Expert insights. Personalized for you.
269 
Let's personalize your content