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.

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.

When using MySQL and InnoDB you will inevitably run into lock timeouts sometime, somewhere. We have recently started seeing this with some of our Shopware 6 customers in their storefronts or worker queues, so I was reminded to go back to 2017 in our codebase when we put a fix in place. In our case, this happened on tables that were constantly written to from many different sources in the code base.

What comes to mind when you think of an outdated website?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.

Here’s a scenario: You’re a QA tester building automated tests for a new online email app. One test case requires you to confirm that the email reply button is working correctly. The challenge is that the email thread updates dynamically without changing its web address URL, complicating the process by limiting the effectiveness of using CSS selectors alone. So what else can you do? The answer: It’s time to bring in XPath selectors.

‍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.

New BigQuery continuous queries execute continuously processing SQL statements as events arrive, for insights that are always up to date.

Machine learning APIs provide developers with powerful tools to integrate complex algorithms and models into applications without building them from scratch. These APIs simplify the development process by offering pre-trained models and standardized methods for different tasks. These include image recognition, natural language processing, and predictive analytics. This accessibility democratizes machine learning so that developers of varying expertise can leverage cutting-edge technology efficiently.

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.

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.