85% of enterprises are running agentic AI on a data foundation that isn't ready
Enterprises are pouring millions into agentic AI. New findings from Fivetran's “Agentic AI readiness index 2026” reveal why most won’t see the return.
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Modern Software Development For Today's Systems & Applications
Tips for upgrading Python/Django versions in existing apps
Python is a robust and powerful programming language. In addition to machine learning, Python can be used for tasks such as web scraping, image processing, scientific computing, and much more. A framework such as Django, which is built on top of Python, enables you to build beautiful web applications—top websites such as Dropbox, Instagram, and YouTube use Django.
Custom Warehouse Management System: Features, Architecture, Tech Stack & Development Guide (2026)
A warehouse doesn’t fail all at once. It slips. Warehouse operations have changed faster than the systems running them. That gap is showing up in subtle ways. Delays during peak hours, inventory mismatches across channels, and increasing reliance on manual interventions to keep workflows moving. Not failures, but friction. At a market level, the shift is clear.
How to scale Gen AI to billions of rows in BigQuery at a fraction of the cost
For many, running generative AI over massive datasets has felt out of reach due to costs and slow processing times. Others settle for traditional ML techniques that require specialized skill sets and often deliver lower-quality results. With optimized mode for BigQuery AI functions, you can now get LLM-quality results at a fraction of the cost and at BigQuery speeds. In this video, we’ll show you how BigQuery uses model distillation and embeddings to process massive datasets, reducing query latency and token consumption.
Automatically Provision Infrastructure for Kafka Migrations
KCP automatically generates custom Terraform modules, allowing you to provision your entire target infrastructure and networking in just a few minutes for Kafka migrations.
Enterprise AI Infrastructure Security Series - 7) Monitoring & Auditing
In this final video of our enterprise AI security series, we cover ClearML's monitoring and audit trail capabilities — the visibility layer that ties everything together. We walk through the platform's operational dashboards, task-level audit surfaces, cost attribution, and external integration points, showing how ClearML delivers live operations and compliance-ready audit out of the box.
Why RBAC Isn't Enough: Real Tenant Isolation in Kubernetes AI Environments
Role-based access control is essential, but it’s not isolation. When multiple AI teams share a Kubernetes cluster, RBAC controls what they can do; it doesn’t control what they can reach, what they can see, or what happens when something goes wrong in a neighboring workload. This is the first post in our four-part series on Kubernetes Security for Enterprise AI Environments.
Is Oracle API Gateway Reaching the End of the Road? What to Do Next.
Last Updated: May 2026 Oracle API Gateway (OAG), the product that grew out of Oracle's 2012 acquisition of Vordel, has been on a long deprecation path. With Oracle steering customers away from on-premises OAG and toward newer cloud-based offerings, technical decision makers are facing a familiar question: stay on a product without a future, or pick a replacement that fits where the business is actually going?
From Kafka Chaos to Control: A Practical Guide to Governing Real-Time Data
Most engineering teams adopt Apache Kafka for one simple reason: it works. It scales effortlessly, it is incredibly reliable, and it powers real-time systems across almost every industry. But as your Kafka usage expands across different teams, regions, and external consumers, success creates a brand new problem. Kafka is a massive data firehose, and without the right nozzle, it quickly becomes unmanageable.
Building a Secure, Scalable AI Infrastructure with Kong and Akamai: A Technical Introduction
As organizations transition from experimental AI to production-grade systems, they often face a fragmented landscape of unmanaged LLM providers, complex tool integrations, and escalating security risks. This infrastructure gap leaves AI applications vulnerable to sophisticated threats like prompt injection and data exfiltration, necessitating a unified stack that secures the edge while streamlining the data plane..