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.

Campaign Group metrics for LinkedIn Ads are now available in Databox, giving you more granular insights into the performance of your campaigns.

Previously Loadero had the same amount of compute power assigned to each participant in every test, but applications that our customers test are different and have different demands for end user machine resources. That is why we added compute units to give our customers better flexibility in test creation. This also allows to run much larger load tests as well as optimize cost of running the tests for the applications that don’t use much compute power.

After having announced Kuma 1.0 GA with over 70+ new features and improvements (and Kuma 1.0.1 this week), we are finally happy to announce a new major version of Kong Mesh that includes all the latest Kuma features – and more – in a fully supported enterprise package. With Kong Mesh 1.1 we can now deploy the most advanced enterprise service mesh in production across every cloud and private datacenter, on both Kubernetes and virtual machines.