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Modern Software Development For Today's Systems & Applications
Real-Time AI at Scale: The New Demands on Enterprise Data Infrastructure
Real-time AI is transforming how businesses process and use data, demanding faster, more reliable, and scalable infrastructure. Unlike older batch processing systems, real-time AI provides instant insights for applications like fraud detection, personalized recommendations, supply chain adjustments, and predictive maintenance. However, scaling these systems introduces challenges like managing massive data streams, ensuring low latency, and maintaining security.
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?
Confluent unlocks scalable real-time agentic AI with Streaming Agents
Enterprises can quickly move from prototype to production-ready AI agents with Streaming Agents.
From Documents to Decisions: The Power of AI Document Processing
How to make your enterprise data accessible, usable, and insightful.
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.