Systems | Development | Analytics | API | Testing

AI-Ready DataOps: Rethinking MDS for LLMs

AI is changing how data teams operate. Is your pipeline ready? Today, data isn't just powering insights, it's fueling real-time decisions and AI/ML models. That means teams now face stricter requirements around data freshness, reliability, orchestration, and delivery speed. In this webinar, Hugo Lu, Founder & CEO at Orchestra will explore what it really means to build AI-first data operations & how leading data teams are adapting their infrastructure, workflows, and tooling to support this new era of model-driven development.

How Iceberg Powers Data and AI Applications at Apple, Netflix, LinkedIn, and Other Leading Companies

Apache Iceberg is transforming how organizations build and manage their data infrastructure, enabling lakehouse architectures that combine the best of data lakes and data warehouses. In this blog, we look at five real-world implementations demonstrate Iceberg's versatility and the advantages it brings to modern data management challenges. Learn more about Data Lakehouses.

What Can Go Wrong? Understanding Risk & Failure Modes in Agentic AI

Agentic AI systems don’t fail like traditional software - they hallucinate facts, pursue the wrong goals, overuse tools, and forget context. These failures look “correct” to traditional test cases, but feel dangerously wrong to users. One team tested an AI support bot - it passed every check, but in production, it gave refund advice that violated company policy. Not a code error. A reasoning failure.

AI-Powered REST API Security and Management with DreamFactory

Modern innovation demands fast, secure, and flexible access to data. But when organizations deal with scattered databases and strict security policies, manual API development slows everything down. The solution? Automate how APIs are built, secured, and managed—using AI and open-source tools like DreamFactory.

Why Exploratory Testing thrives with AI

Software is now shipped faster than ever and testing evolved beyond rigid scripts and predefined steps. One approach that has always embraced adaptability, critical thinking, and curiosity is exploratory testing: the process of learning, designing, and executing tests simultaneously — often uncovering issues that traditional testing might miss. As Artificial Intelligence (AI) becomes more embedded in the software development lifecycle, many wonder: will AI replace exploratory testing?

Beyond RAG: Secure, Agent-Based Access to Enterprise Data

Struggling with secure, real-time enterprise data access? RAG (Retrieval-Augmented Generation) systems are popular but often fall short in handling dynamic data, security, and compliance. Enter agent-based systems - designed to securely connect AI to live databases, APIs, and ERP systems while enforcing strict permissions and audit trails. Key Takeaways: RAG systems lack granular security, real-time updates, and detailed compliance tracking.

Top 20 Ai Testing Tools In 2025 | Free & Open Source

The complexity of software continues to increase as teams adopt microservices, APIs, and cloud-native architectures. Manual testing is no longer able to keep up with the speed of continuous releases. QA teams are dealing with increasing pressure to not only assure software quality but to do so in a shorter cycle time. This is where AI Testing Tools can provide a solution. AI-powered testing takes advantage of machine learning, predictive analytics, and self-healing features.

From Building at Pinterest and Airbnb to Revolutionizing AI at Kumo.AI

Join us for expert insights on how data is rewriting the rules and driving the future of innovation with Dr. Vanja Josifovski, CEO and Founder of Kumo.AI. In this episode, Dr. Vanja shares his approach to enterprise AI and reveals why a "data-forward organization" is the key to success. He also explains why integration speed is now the new competitive edge, and makes a compelling case for rebuilding outdated systems to achieve a 10x advantage.