‍McKinsey’s latest research projects that generative AI could contribute between $2.6 trillion and $4.4 trillion annually across various sectors. Experts have also observed that integrating AI-driven automation, threat detection, and low-code platforms redefines next-gen software development. Whether it is code generation, bug fixing, or even designing a new digital component, generative AI is seeping into all product engineering processes.

IoT is driving the digital metamorphosis in the banking industry. The surge in internet-enabled devices, like smartphones, tablets, and smartwatches, signifies an increase in opportunities for fintech software development to scale their business through the Bank of Things (BoT). IoT technologies are enabling real-time connections, making banking more efficient and customer-centric.

At Confluent, we are committed to enhancing our product to meet the evolving needs of our customers. In the last year we’ve seen substantial increases in both UI users and UI usage. With such high growth, we’ve been looking for ways to improve experiences where we can. Based on internal and external validation, the Confluent Cloud Homepage was specifically identified as an area that could be improved.

In the ever-evolving landscape of software testing, the transition from manual to automated testing is a journey filled with misconceptions, challenges, and significant rewards. Recently, I had the privilege of hosting a conversation with two industry experts, David DiCocco and Alex Martins, who shared their personal experiences and insights on this topic.

At Snowflake, we are committed to helping customers derive meaningful insights from data with simplicity and speed. That’s why we’re thrilled to announce the general availability of our enhanced RANGE-based window frame, which will help accelerate time-series analytics. For customers across industries, the ability to perform time-series analytics is crucial. This often involves calculating rolling statistics that remain robust despite gaps or nonuniform time-series data.

Developing high-quality products involves careful definition and tracking of both functional and non-functional requirements (NFRs). But what exactly are non-functional requirements? And what's the best way to keep track of them? Let’s dive into what non-functional requirements are and how they compare to functional requirements, review some common types of non-functional requirements with examples, learn best practices for writing them, then discuss how to track and manage them.

Privacy and security are top priorities for Unravel and our customers. At Unravel, we help organizations better understand and improve the performance, quality, and cost efficiency of their data and AI pipelines. As a data business, we appreciate the scope and implications of privacy and security threats. This data sheet provides details to help information security (InfoSec) teams make informed decisions. Specifically, it includes: For additional details, please reach out to our security experts.

Testing tools in AI and data automation have evolved to offer sophisticated features that ensure the quality of the end product and reduce its time to market. With reports suggesting 50% of manual testing being replaced by automated, these tools can help with various aspects of software testing, including unit testing, performance testing, and security testing. They can be integrated practically anywhere across the CI/CD pipeline for continuous testing and shift left testing.

What comes to mind when you think of an outdated website? Your answer will depend on how far back you’ve seen the internet, but it could range anywhere from glossy interfaces to the default blue, Times New Roman links. But even a few years of age could put your website behind the competition, even if it doesn’t look like MySpace or the first version of IMDB. Its audience and goals could outgrow its design, especially if you don’t make small changes often.

Most developers who upload mobile apps to the App Store or Play Market have likely encountered unexpected bugs in new releases. To address this issue, a new build with the fix must be created and checked again by the platform where they are uploading. They also have to wait for the approval process again for the new version to be released. Unfortunately, this entire process takes a considerable amount of time, but there is an alternative way.