Systems | Development | Analytics | API | Testing

Welcome to the latest entry in Yellowfin Japan’s long-running ‘How to?’ blog series. This series of blogs aims to provide a step-by-step guide of how to get the most out of your Yellowfin BI experience, such as how to create your own Yellowfin dashboards. Catch up on the series below.

API versioning is essential for managing changes without breaking your system. In microservices, where APIs are the backbone of communication, versioning ensures stability, backward compatibility, and smooth updates. Here’s a quick breakdown of what you need to know.

Software release cycles are accelerating. In fact, 85% of organizations now release at least once per month, with a 51% increase in automated testing spend last year alone. Yet API quality still breaks down. Why? APIs sit at the center of this acceleration, but as velocity increases, many organizations face a persistent challenge.

If your BI and analytics tool isn’t powered by Agentic RAG AI, you’re missing out on advanced AI capabilities that enhance efficiency and visibility into data. Whereas agentic AI can work autonomously and without human intervention, RAGI AI combines the best information retrieval methods available with the power of AI. The result is deep knowledge of organizational data that improves over time.

Let’s start with DevOps, the buzzword that changed how we think about building and shipping software. These days, every college student and other professional wants to become a DevOps engineer. If you are an aspiring DevOps engineer or already working as a DevOps engineer, this blog will help you understand the difference between Platform engineering and Devops Platform engineering is really changing every company’s perspective on developing platforms.

Business intelligence platforms analyze vast amounts of data, requiring visualization engines that balance performance, flexibility, and ease of use. Traditional charting libraries treat each chart type as a distinct entity, requiring separate logic and code for each. This approach leads to code duplication, limited reusability, and reduced maintainability. Additionally, it’s cumbersome to effectively layer or combine visual elements due to these libraries’ rigid composability.

Everyone thinks AI is getting cheaper. But is it really? At first glance, the economics of AI seem to be improving for everyone. Thanks to continued model optimization and advances in hardware, the cost of running LLMs (also known as inference) is steadily decreasing. Developers today can access incredibly powerful models at a fraction of what it cost just a year ago. But there’s a catch.

Another day, another API. Fueled by AI-assisted coding and agile workflows, the speed of innovation has never been higher. But for the compliance team? It’s panic mode. Every new API must follow a minefield of internal rules: security protocols, naming conventions, reuse policies, documentation standards. And while the dev team is flying forward, compliance is stuck doing manual reviews, chasing specs, and untangling inconsistencies often after the code is already written.

SAP’s business practices impact how enterprises access and integrate their data. This is the first blog in a multi-part series exploring how to access and best leverage your SAP data.

AI failures rarely look like crashes. More often, they appear as confident but incorrect answers, subtle bias, or culturally inappropriate responses.