Systems | Development | Analytics | API | Testing

Google Cloud Serverless for Apache Spark, now available in BigQuery is an open-source, zero-ops, high performance, and unified analytics platform.

We are proud to announce a new integration between MLRun, the open-source AI orchestration framework, and NVIDIA NeMo microservices, by extending NVIDIA Data Flywheel Blueprint. This integration streamlines training, evaluation, fine-tuning and monitoring of AI models at scale, ensuring high-performance, low latency and lowering costs while significantly reducing the manual effort required through intelligent automation.

Mobile apps aren’t just another digital touchpoint anymore – they’re the front line. Whether you’re banking, booking a ride, ordering dinner, or catching up on emails, it’s happening on your phone. But when things go wrong, customers don’t wait around. According to PwC, 32% of customers would stop doing business with a brand they love after just one bad experience.

In today’s software landscape, APIs are more than just a technical interface, they’re the connective tissue of modern digital products. Organizations embracing API-first strategies aren’t just producing better APIs – they’re fueling faster innovation, scalable architectures, and smoother collaboration across teams. But transitioning to an API-first strategy isn’t always a smooth ride.

Want to speed up AI projects? DreamFactory's RBAC-secured APIs reduce development time by 85% while ensuring top-level security. Here's how.

The information in this blog post is based on a real-life scenario shared by a user on our Katalon Community forum and is intended to inspire peer-to-peer discussion and collaboration. Please always test solutions thoroughly before implementing them in a production environment. Feel free to continue the discussion here.

Most companies believe they own their customer data. Most are wrong. Data is your most powerful asset for fueling decisions, improving customer experiences, and providing a competitive edge. But if your customer, marketing, or product teams rely on third-party analytics tools, there’s a great chance you don’t actually own your data. It’s processed, stored, and sometimes even monetized by vendors who decide your access and control levels.

In the age of AI, the hunger for fresh, reliable data to power machine learning (ML) models and real-time analytics is insatiable. Yet, organizations frequently hit roadblocks when trying to bridge their operational data in motion, typically flowing through Apache Kafka, with their data at rest in data lakehouses. On one side, you have the data streaming platform, the central nervous system managing the real-time flow of business events.

Have you ever looked at your code and asked yourself, "Who wrote this mess??" And suddenly you realized it is none other than you. I’ve faced this situation a lot—your own code seems like a mess if you review it after 2 or 3 months. Do you know the reason why? Yes, it’s because there is no refactoring in the code In this blog, we’ll explore what code refactoring is, why it’s important, and walk through a few examples.

The rapid acceleration of AI adoption is transforming how enterprises design their data infrastructure, driving the need for robust, scalable, and energy-efficient solutions. At Hitachi Vantara, we’re building the future of AI storage by collaborating with NVIDIA to close the gap between data and AI compute. Our mission: help organizations unlock faster, smarter insights with an AI-ready data pipeline.