Systems | Development | Analytics | API | Testing

Automated testing has emerged as a prominent practice that leverages machines (compute power, scripts, machine learning, genAI) to validate the quality of a software application. With the promise of increased efficiency, broadened test coverage, and accelerated software delivery, automated testing has become a cornerstone initiative for modern software engineering teams.

Visionaries from Capgemini, Databricks and Fivetran lay out the data quality imperative for implementing enterprise AI applications.

When WebSockets were first announced in 2011, they had some teething problems. As a result of these teething problems, only the most daring and experimental developers would rely solely on WebSockets for realtime communication. For most of us, it became a necessity to implement a fallback protocol in case the WebSocket connection can’t be established.

One of the main value propositions of the Moesif product is the fact that it works so well with a wide variety of tools. One such tool – Logstash – is an incredibly powerful solution for managing events and logs. Understanding how to pull Logstash logs into Moesif is a critical function for any business utilizing Logstash at scale. Today, we’re going to look at how to log API calls from Logstash using the HTTP Output plugin for import into Moesif.

Helping companies leverage their data to make better decisions and improve their performance is something we strive for at Databox. Until recently, this type of analysis has only been reserved for larger enterprises with dedicated teams and budgets for complex tools. Fortunately, recent innovations are reshaping this landscape.

Next year, finance teams utilizing SAP will face unparalleled pressure due to a confluence of economic challenges. The demand for actionable insights will grow amidst a landscape marred by economic recession and escalating inflation rates. These adversities heighten the necessity for finance professionals to adeptly translate raw data into strategic guidance.

Big data is more crucial to businesses than ever, especially with the rise of artificial intelligence (AI) and machine learning technologies across multiple industries. The expanding internet of things (IoT) plus the multiple digital engagement points between businesses and customers create huge swathes of data and new challenges in how to manage this glut of information.

With its simplicity, flexibility, and cost-efficient characteristics, Amazon Simple Storage Service (Amazon S3) cloud object storage has become the preferred platform for collecting, analyzing, and retaining today’s growing mountain of diverse and disjointed enterprise data. And as Amazon Web Services (AWS) continues to grab market share in the hyperscale IaaS/PaaS/SaaS marketplace, organizations of every size are leveraging Amazon S3 to underpin a variety of use cases, such as.

Retail media is the topic everyone is talking about in the retail and consumer goods industry. And for good reason: the $45 billion U.S. retail media market is surging as retailers capitalize on the consumer shift to ecommerce while offering advertisers access to their unique audiences and data insights. Many retailers developed their own retail media networks over the last few years, from digital marketplaces and department stores to commerce intermediaries.

The rapid development and deployment of software applications largely rely on the power of APIs. These Application Programming Interfaces act as the glue holding together various components, enabling efficient communication and data exchange between them. From mobile apps to web services and enterprise systems, APIs are ubiquitous, playing a critical role in the seamless functioning of modern software architecture – but its important to take internal API security risks seriously.