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.
How I used AI agents to optimize product operations
Conversational analytics leveraging Fivetran, Jira, and Claude saves analytical effort and boosts productivity.
AI post-training: Finetuning using PEFT and DPO on Cloudera AMP
Post-training is rapidly becoming a critical phase of enterprise AI development. To get reliable output from an AI model, organizations must align its terminology (e.g., abbreviation) to fit their specific use cases. But getting started shouldn't require heavy computing resources—you can quickly train an open-source model right on your local device. In this tutorial, we sit down with the ASAP_DPO_Finetuning Cloudera AMP to demonstrate exactly how to align a language model to specific industry standards—in this case, Oil & Gas abbreviations.
How to Connect Power BI to Amazon DataZone (Without a JDBC Bridge)
Amazon DataZone is a powerful data management service that lets teams catalog, discover, and govern data across AWS environments. But when it comes to connecting your BI tools, options are limited. Data teams trying to connect Power BI to Amazon Datazone often hit the same wall when every guide, forum thread, and AWS doc points you toward a JDBC bridge or driver. However, Power BI doesn’t speak JDBC natively, which quietly costs data teams time, stability, and patience.
Integrating AI Into Apache Kafka Architectures: Patterns and Best Practices
Adding large language models (LLMs) and artificial intelligence (AI) to real-time event streams comes down to one thing: picking the right boundary between data transport and model compute. Where you run inference determines your system's resilience, latency, and cost. This article is for data engineers, streaming architects, and developers who want to add AI capabilities to their Apache Kafka event backbone without destabilizing production consumer groups or blowing through API rate limits.
Why Real-Time Stream Processing Beats Batch ETL for AI Data Freshness in 2026
AI has evolved fast. We've gone from static, predictive models to dynamic, interactive agents. But most organizations still run data pipelines that haven't kept up. Consider what’s happening in modern AI architecture. Teams deploy high-performance engines like large language models (LLMs) and real-time fraud detectors, then feed them data that's hours or days old.
Turning Virtualization Modernization Into Business Outcomes
As enterprises navigate rising virtualization costs and increasing infrastructure complexity, many are rethinking their approach to modernization. One organization leading this transformation is Alior Bank, a forward-looking financial institution that successfully modernized its IT environment to improve agility, resilience, and cost efficiency.
Secrets, Credentials, and the Kubernetes Attack Surface in AI Environments
Every AI workload needs credentials: cloud storage keys, model registry tokens, database passwords, and API keys for external services. How those credentials are managed in Kubernetes determines whether they stay secret or become the entry point for a serious breach. ClearML Vaults addresses this directly by separating credential ownership from credential use at the platform level. This is the second post in our four-part series on Kubernetes Security for Enterprise AI Environments.
Not All "Drill-Down" Analytics Is Created Equal
Many analytics platforms claim to support deep exploration. But in practice, “drill-down” often means navigating predefined reports—not actually querying your data. That distinction becomes clear when you look at how tools like Google Analytics 4, Piwik PRO, or Dataroid approach analysis.What “Drill-Down” Really MeansIn most analytics tools, drill-down refers to clicking deeper into dashboards—filtering segments, breaking down charts, or switching views.
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.