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.

Interested in learning about different technologies and methodologies, such as Databricks, Amazon EMR, cloud computing and DataOps? A good place to start is reading articles that give tips, tricks, and best practices for working with these technologies. Here are some of our favorite articles from experts on cloud migration, cloud management, Spark, Databricks, Amazon EMR, and DataOps!

Not all apps are made the same. Native mobile apps, web mobile apps, hybrid mobile apps and progressive web apps (PWAs) are different in many ways. When choosing the right type of app for your business, you’ll want to carefully consider the advantages and disadvantages of each, then make sure you use the right testing approach. Below, we explain how these four types of apps vary, how to choose the right app type for your business, and what that means for your testing strategy.

Erin Byrne is a Senior Customer Solutions Engineer at Qlik and shares her practical advice to successfully divest your SAP systems. Erin has worked with SAP systems for over 20 years, has built more than 100 SAP systems and managed in excess of 30 divestiture projects.

The latest United Nations IPCC report paints a sobering picture. Climate change looks likely to accelerate in all regions as we approach the critical global warming threshold of 1.5°C. Such an uptick in temperatures will increase sea level rise and intensify the frequency and magnitude of extreme weather events. For cities, these changes will make governance more difficult in nearly every respect.

Node.js v17.0.0, the latest major version of the popular JavaScript runtime, has just been released. It supersedes v16 in the Current release line of the runtime. V16 is now in line to be promoted to the long-term support (LTS) channel on October 26, 2021, as it’s an even-numbered release. Despite being a relatively minor update, this release brings several refinements to the runtime, including more promisified APIs, JavaScript engine upgrades, and OpenSSL 3.0 support.