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By Katie Gdula
Companies everywhere are rushing to deploy AI models to outpace the competition, but they are running headfirst into a brutal reality check: an AI model is only as brilliant as the data feeding it, and most data simply isn't AI-ready. The high-value, real-time data required to power these models doesn’t live in a pristine, pre-formatted cloud data warehouse. It is generated in the physical world on factory floors, inside hospital rooms, and at point-of-sale terminals.
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By Dennis Duckworth
For years, the mandate for System Integrators (SIs) was clear: lead the "cloud-first" migration. The promise was lower costs, greater agility, and seamless innovation. But for many enterprise customers, that promise remains unfulfilled. Instead of agility, organizations have inherited a complex, fragmented data estate. Data is siloed across on-premises legacy systems and multiple public clouds, creating governance headaches and inflating infrastructure costs.
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By Ron Pick
Spend enough time in the data management world, and you’ll quickly encounter a flood of terminology: semantic layers, knowledge graphs, unified metadata, governance fabrics, data meshes, and, of course, agentic AI. Most organizations know these aspects matter, yet many still struggle to understand how they fit together. The problem with traditional data architecture is that it is often treated as purely technical.
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By Navita Sood
The lakehouse architecture was developed with the mission to combine the unstructured scale of the data lake with the structured performance of the data warehouse. This shift unified enterprise data and delivered the first true "single source of truth". But in 2026, the mission has expanded.
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By Charu Anchlia
Autonomous agents act toward complex goals without requiring human direction at each step. In enterprise environments, deploying these agents introduces a more exacting set of challenges: they must navigate heterogeneous data systems; satisfy compliance, audit, and data sovereignty mandates; and keep all data within the organization's operational boundary.
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By Jeremiah Morrow
In today's state, local, and education (SLED) environments—especially higher education—budgets are under constant scrutiny, and the demand for data excellence is constant. That means doing more with fewer resources. One high-impact change to your data workflows that can transform the quality of your data and AI while lowering costs is automating and documenting data lineage.
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By Stephen Catanzano
Enterprise interest in generative and agentic AI has accelerated dramatically over the past two years. Organizations across industries are exploring how AI agents, intelligent assistants, and automation can improve productivity, streamline operations, and unlock insights from growing volumes of enterprise data. Yet as enthusiasm grows, so do questions around cost, security, and operational complexity.
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By Ron Pick
In the current enterprise technology landscape, we’re witnessing an industry-wide scramble. As organizations shift from monolithic architectures to complex environments leveraging heterogeneous infrastructures, cloud-based data platforms are hitting a visibility—i.e., observability—wall. Their response has been a wave of reactive, multi-billion-dollar acquisitions designed to "bolt-on" the observability that they lack natively.
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By Blake Tow
For the better part of a decade, the enterprise technology mandate was simple: “cloud first,” or more pointedly “cloud only.” Modernizing meant moving to the public cloud, and on-premises architecture was viewed as legacy infrastructure to be maintained until it could eventually be migrated. Fast forward to today, that narrative has shifted dramatically, with AI as the major catalyst.
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By Cloudera
With the integration of Trino, Cloudera SDX, and Cloudera Octopai Data Lineage, Cloudera arms enterprises with seamless access and control of their data, anywhere, automating workflows and boosting efficiency.
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By Cloudera, Inc.
Now that AI coding tools have put development capabilities into more hands, prototypes are becoming business-critical applications almost overnight. Shanea Leven sees an opportunity for a new generation of builders, provided the infrastructure around their applications keeps pace. Shanea explains how organizations can give developers and new technical employees room to build while maintaining the standards required for enterprise software..
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By Cloudera, Inc.
Learn how to build an end-to-end, real-time edge-to-AI data pipeline to tackle critical enterprise challenges like credit card fraud detection. In this demo, Diby Malakar (Product Lead for Data in Motion) demonstrates how to process an average of 5,000 transactions per second in low hundreds of milliseconds to detect fraud instantly. Discover how Cloudera’s Data in Motion suite enables application developers to ingest, govern, and enrich streaming edge data to power instant AI model inference and live analytics.
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By Cloudera, Inc.
Stop burning budgets on AI for AI's sake. With billions pouring into data centers, many enterprises are experiencing massive buyer's remorse. How do you ensure your company is part of the 10% actually driving results? Guest Mark Ritcey breaks down.
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By Cloudera, Inc.
Something major is coming for our customers and partners... Michelle Hoover shares how Cloudera and our partner ecosystem are helping simplify data and AI across environments while driving stronger business value and outcomes. What You'll Learn.
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By Cloudera, Inc.
Most enterprise AI use cases still aren't delivering measurable value. So what separates the projects that work from the ones that quietly disappear? For Mark Ritcey, the answer comes down to disciplined execution. AI programs need a clear business problem and an organization prepared for how the technology changes the way work gets done. In this episode of The AI Forecast, Paul Muller sits down with Mark Ritcey, Vice President of AI and Automation Delivery at Latentbridge and lecturer on AI and machine learning, to examine the decisions that shape enterprise AI success.
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By Cloudera, Inc.
Relying strictly on public cloud AI APIs often leads to unpredictable token costs and data residency concerns. In this demo, Peter Ableda (Product Lead for Cloudera AI) demonstrates how the Cloudera AI Inference service enables enterprise-grade private AI behind your firewall—whether on-premises or in a Virtual Private Cloud (VPC). Learn how bringing AI directly to your data ensures total model control, cost predictability, and maximum data security.
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By Cloudera, Inc.
Did you know Cloudera's marketing operations team is using AI to automate workflows and eliminate manual processes? In this video, Kelly Sutter shares how Cloudera empowers marketing teams to unify data, uncover customer insights faster, and optimize marketing analytics with trusted enterprise AI. Discover how bringing AI to your data makes marketing more connected, responsive, and effective.
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By Cloudera, Inc.
Most enterprise AI projects stall when teams try to move experiments into production—where costs, governance, data security, and scale all get real. In this demo, see how Cloudera AI Inference helps turn foundation models into secure, governed, production-ready AI services. You’ll learn how to: Chapters: Subscribe to stay ahead of the curve with the latest in data strategy, open architectures, and enterprise AI innovations.
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By Cloudera, Inc.
Watch deep-dive presentations and live demos from industry experts as they unpack next-generation data frameworks built from the ground up for multimodal AI, autonomous data agents, and cross-cloud architectures. Key Technical Highlights Inside: AI Lakehouse Meetup - Bay Area 15th July 2026 Cloudera SanJose Office A must-watch technical guide for data engineers, platform architects, and MLOps teams!
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By Cloudera, Inc.
How AI is transforming clinical trial operations—without compromising critical data. Biopharmaceutical companies are routinely slowed down by siloed datasets and complex workflows. While artificial intelligence promises to accelerate drug development, clear up decision-making opacities, and build dependencies, handling highly sensitive medical data requires ironclad protection.
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By Cloudera
Enterprises require fast, cost-efficient solutions to the familiar challenges of engaging customers, reducing risk, and improving operational excellence to stay competitive. The cloud is playing a key role in accelerating time to benefit from new insights. Managed cloud services that automate provisioning, operation, and patching will be critical for enterprises to leverage the full promise of the cloud when it comes to time to value and agility.
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By Cloudera
The adoption of cloud computing in the financial services sector has grown substantially in the past three years on a global basis. Diversification of risk is always a key concern for financial institutions and the seeming safety of having a single cloud provider is not being properly measured from a systemic risk and operational risk perspective.
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By Cloudera
This white paper provides a reference architecture for running Enterprise Data Hub on Oracle Cloud Infrastructure. Topics include installation automation, automated configuration and tuning, and best practices for deployment and topology to support security and high availability.
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By Cloudera
A cloud-based analytics platform needs to be easy, unified, and enterprise-grade to meet the demands of your business. This white paper covers how Cloudera's machine learning and analytics platform complements popular cloud services like Amazon Web Services (AWS) and Microsoft Azure, and enables customers to organize, process, analyze, and store data at large scale...anywhere.
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By Cloudera
In the wake of the global financial crisis, the world has become much more interconnected and immensely more complex. As a result, you can no longer simply look at the past as an indicator of future trends. The financial services industry needs real-time insights into numerous interacting variables to make informed decisions.
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Cloudera delivers the modern platform for machine learning and analytics optimized for the cloud. Imagine having access to all your data in one platform. The opportunities are endless. We enable you to transform vast amounts of complex data into clear and actionable insights to enhance your business and exceed your expectations.
The right products for the job:
- Enterprise Data Hub: Operate with confidence—thanks to comprehensive security and governance—while at the same time enabling unrivaled self-service performance at extreme scale. All in an enterprise-grade solution that lets you run anywhere, on-premises or in hybrid- and multi-cloud environments.
- Data Science Workbench: Accelerate machine learning from research to production with the secure, self-service enterprise data science platform built for the enterprise.
- Data Warehouse: A modern data warehouse that delivers an enterprise-grade, hybrid cloud solution designed for self-service analytics.
- Data Science & Engineering: Cloudera Data Science provides better access to Apache Hadoop data with familiar and performant tools that address all aspects of modern predictive analytics.
- Altus Cloud: The industry’s first machine learning and analytics cloud platform built with a shared data experience.
The world’s leading organizations choose Cloudera to grow their businesses, improve lives, and advance human achievement.