Historically, software development and quality assurance were one and the same. If you built it, you also tested it. But then software grew up, and as it got more and more complex, dev and QA needed to split up in order to do their job right. But instead of these two teams remaining close friends, they grew far apart. Each in their own world, operating in different environments, using their own workflows, speaking different languages.

In our previous article, The Future of the Modern Data Stack, we examined the motivations of the modern data stack, its current state, and looked optimistically into the future to see where it is headed. If you’re new to the modern data stack, we highly recommend giving the aforementioned article a read. A question we often get from new adopters of the modern data stack is “What tech should we be looking into?”.

We live in a world that is increasingly digital and data-driven. We have wearable devices to monitor our steps, heart rate, and sleep quality. We have Alexa, Siri, or Google ready to take our shopping orders, play our playlist, share the latest news. We can see what shows are trending on Netflix and curated suggestions for us based on what we watched. We cannot get away from data. Where does data live? Who has access to them? What do they do with them?

Exception handling tools with limited features often have a short lifetime. Thankfully, getting detailed exceptions with Retrace is a great choice for development teams, especially when compared to ELMAH. Let’s take a look.

As enterprises develop complex hybrid and multi-cloud environments to support futuristic use cases, a new report published by Big Data Quarterly (BDQ) aims to educate IT decision-makers and practitioners on the most up-to-date solutions and strategies for modern data management in hybrid and multi-cloud environments.

The term “Design Pattern” describes a well-known and battle-tested solution to a problem that developers tend to encounter again and again when developing software. Design patterns are conceptual and can be implemented in any programming language. Design patterns generally fit into one of the following three different categories depending on the problem they address: In this blog post I’m going to cover a pattern from each of these categories in depth.

You got through all the hurdles getting the data you need; you worked hard training that model, and you are confident it will work. You just need to run it with a more extensive data set, more memory and maybe GPUs. And then...well. Running your code at scale and in an environment other than yours can be a nightmare. You have probably experienced this or read about it in the ML community. How frustrating is that? All your hard work and nothing to show for it.

I’ve had the pleasure to participate in a few Commercial Lines insurance industry events recently and as a prior Commercial Lines insurer myself, I am thrilled with the progress the industry is making using data and analytics. However, I do not think Commercial Lines insurance gets the credit it deserves for the industry-leading role it has played in analytics. Commercial Lines truly is an “uber industry” with respect to data.

The pandemic has been unkind to many retailers, with customer expectations shifting seemingly overnight. During the great recession of 2008–2009, e-commerce grew, and brick-and-mortar retail declined. As the economic recovery took hold, that trend continued while off-price, discount, and emerging players succeeded by appealing to new consumer demands. For example, in January 2020, fast-paced home delivery was perceived as a unique selling point offered only by select stores.

Natural language query (NLQ) is fundamentally a feature designed to democratize self-service analytics, and help make it more pervasive throughout the organization.