There comes a time in every developer’s life (or daily routine, we’re not here to judge) where they have to go and fix a bug. Back in the days when I used to be a developer, I distinctly remember how each time I would go face to face with a bug, my favorite method to fix it was to add log lines. I mean, why not, right?

Thanksgiving holiday is upon us. For many of our customers, this is one of the most important periods of the year, with more than 189.6 million U.S. shoppers buying up bargains from Thanksgiving day through Cyber Monday last year. For them and for us, it’s crucial that internal systems can handle high traffic volume without downtime or performance degradation.

The device landscape is as vast as it is complex. With at least 63,000 possible device profiles reported—a number growing at almost 20% per year—the scale of device fragmentation is staggering. New models, operating systems, browsers, screen resolutions, etc., make it extremely difficult for web and app developers to deliver a consistently flawless user experience across all combinations.

Given the competitive value of analytics and rapid adoption rates across industries, you can’t afford a subpar analytics program. In the late 90s, Oakland Athletics general manager Billy Beane used data to discover undervalued talent and assemble a perennial playoff-caliber team, and he did so on a shoestring budget compared to Major League Baseball’s heavy hitters. Beane’s pioneering use of data analytics became the subject of the bestselling book Moneyball.

Many analytics programs struggle to assimilate data from numerous and unpredictable sources, but automated ELT offers a solution. Why do so many businesses struggle to establish successful analytics programs? A lack of data is not the problem. Data volumes — from hundreds of cloud applications to millions of IoT endpoints — are exploding across organizations and industries.

Implementing a modern, cloud-based analytics stack doesn’t have to be hard — you can do it in three steps, actually. Implementing a modern data stack (MDS) — data integration tool, cloud data warehouse and business intelligence platform — is the best way to establish a successful analytics program as data sources and data volumes multiply.

Let’s talk about SPAs. It all starts from a blank page which is subsequently filled with HTML and JavaScript. If we take PHP pages as an example, they already come bundled with the server, which is an advantage in terms of performance, right? For situations like these, server-side rendering frameworks (such as Next.js) come to the rescue. They process the code on the server-side to pre-fill the HTML result page with something (if not the whole page) before it reaches the browser.

Today’s business world is relentless, and competition is intense. The digital landscape is continually shifting across many industries, creating an unprecedented demand for companies to innovate, experiment, and deliver capabilities faster. A robust digital transformation process speeds up business activities, competencies, and models while providing as much value as possible to customers. However, organizations must be cautious in their digital investment strategies.

I met Matthew in New York City about a year ago. We sat in a private conference room and he told me the story of his pharma startup. A small group of researchers set out to solve the black-box enigma of certain kinds of vicious cancers. There are so many cancers, so their vision was to focus on especially heinous ones. Fast forward to their recent FDA approval of their “Hail Mary” procedure and treatment methodology for stage-four patients of a particular cancer.

Unravel provides full-stack coverage and a unified, end-to-end view of everything going on in your environment, plus recommendations from our rules-based model and our AI engine. Unravel works on-premises, in the cloud, and for cloud migration.