The difference between consuming data and actioning it often comes down to one thing: effective data visualization. Case in point? The John Snow’s famous cholera map. In 1854, John Snow (no, not that one) mapped cholera cases during an outbreak in London. Snow’s simple map uncovered a brand new pattern in the data—the cases all clustered around a shared water pump.

Developing high-quality apps involves pressure to make tradeoffs on speed, quality, and features to meet deadlines for release. This tension between speed and quality comes to a head with QA: you need a functional product but can’t afford weeks of turnaround time. You can’t skip QA: the true cost of software bugs – the direct cost of mitigating the defects and the indirect cost of decreased consumer trust – is extraordinary.

Data mesh is being talked about a lot to describe the way data is managed across the organization. But what does it really mean for your organization’s data management strategy and how can its framework support your business needs and drive data pipeline success? On a high level, data mesh is about connecting and enabling data management across distributed systems.

Data modeling is the process of organizing your data into a structure, to make it more accessible and useful. Essentially, you’re deciding how the data will move in and out of your database, and mapping the data so that it remains clean and consistent. ThoughtSpot can take advantage of many kinds of data models, as well as modeling languages. Since you know your data best, it’s usually a good idea to spend some time customizing the modeling settings.

Recently, conversations have been increasing around OpenTelemetry; it is gaining more and more momentum in Node.js development circles, but what is it? How can we take advantage of the key concepts and implement them in our projects? Of note, NodeSource is a supporter of OpenTelemetry, and we have recently implemented full support of the open-source standard in our product N|Solid. It allows us to make our powerful Node.js insights accessible via the protocol.

Optical Character Recognition (OCR) automates extracting text from visual assets such as PDFs and images. This tutorial teaches how to use Amazon Textract and AWS Lambda to build an OCR service.

Every financial professional understands that the numbers matter a great deal when it comes to reporting financial results. Accuracy, consistency, and timeliness are important. Those same professionals also know that there’s substantive meaning behind those numbers and that it’s important to tell the stories that lend additional depth and context to the raw financial statements.

State management is a very controversial topic in the Flutterverse, with many holding strong opinions on it. In the last few weeks, my Twitter feed has been overwhelmed with threads about state management, covering the entire spectrum of opinions, from GetX/BLoC/Riverpod fanatics on one side to a few rare enlightened ones on the other side who recommend not using any state management at all. While I personally enjoyed almost all of them, the impact on the Flutter community has been quite severe.

Chris Tozzi reviews the most popular JavaScript test automation frameworks and presents several things to consider as you select the best option for you and your organization. JavaScript consistently ranks as the world’s most popular programming language among developers. It should come as no surprise then that a variety of JavaScript test automation frameworks are available to help developers test JavaScript apps.

Modern applications are constantly evolving, and with the advent of integrated DevOps toolchains applications can be built and deployed at ever-increasing velocities. This puts pressure on quality assurance teams to increase their velocity as well. With NeoLoad RealBrowser technology, we now offer a rapid way to record and immediately execute user paths to validate the performance of application builds as they move through their pipeline.