ISO 25010 helps you to ensure that your software is high quality. Read along or jump ahead to the section that interests you the most.

Testing in production simply means testing new code changes in production, with live traffic, in order to test the system’s reliability, resiliency, and stability. It helps teams solve bugs and other issues faster, as well as effectively analyze the performance of newly released changes. Its overall purpose is to expose problems that can’t be identified in non-production environments for reasons that may include not being able to mimic the concurrency, load, or user behavior.

As COVID-19 vaccinations continue to roll out, organizations across industries prepare to return employees to the workplace. It’s a journey fraught with anxieties, uncertainty, and unknowns. In a recent poll of Appian webinar participants, a mere 17% reported that their companies feel fully prepared to reopen facilities, and only 51% said they felt even “somewhat” prepared. The good news is that a safe return to the workplace is not uncharted territory.

MongoDB and MySQL are similar is some ways, but they also have some obvious differences. Perhaps the most obvious one is that MongoDB is a NoSQL database, while MySQL only responds to commands written in SQL. Potential users may want to examine MongoDB vs. MySQL in the areas of performance and speed. The following article will help you understand the differences, as well as the pros and cons of each database.

In this article, I will be focusing on the contribution that a multi-cloud strategy has towards these value drivers, and address a question that I regularly get from clients: Is there a quantifiable benefit to a multi-cloud deployment? That question is typically being asked when I explain the ability to leverage container technology that offers a consistent deployment environment across multiple clouds and form factors (public, private, or hybrid cloud).

Mastercard is one of the world’s top payment processing platforms, with more than 700 million cards in use worldwide. In the US, nearly 40% of American adults hold a Mastercard-branded card. And the company is going from strength to strength; despite a dip in valuation of more than a third when the pandemic hit, the company has doubled in value three times in the last nine years, recently reaching a market capitalization of more than $350B dollars.

In a press release delivered today, Unravel Data announced its appearance on CRN’s Big Data 100 list for 2021. Unravel’s entry appears in the Data Management and Integration category. Also featured in this category are other rising stars such as Confluent, Fivetran, Immuta, and Okera, all of whom spoke at new industry conference DataOps Unleashed, held in March.

TL;DR: Yes, you can do it. And no, you don’t need a separate tool for it. “Reverse ETL” is a fairly recent addition to the data engineer’s dictionary. While you can read articles upon articles about it (there’s a pretty good ‘primer’ in the Memory Leak blog), it can be summarized as being the art and science of taking data from your data warehouse and sending it somewhere other than BI - generally into other tools and systems where it becomes operational.

With the ever-increasing demand to deploy high quality and reliable software faster, automation has become an integral part of modern software development. By seamlessly integrating your existing test automation efforts with Zephyr Scale, you can benefit from faster releases, tighter collaboration, and better visibility of your testing processes. Read on to discover why and how you can unify test management and automation in a single tool with Zephyr Scale.

We’ve updated 7 more integrations to now support custom date range functionality in Databox.