Cloudera

Palo Alto, CA, USA
2008
  |  By Robert Hryniewicz
The world of Generative AI (GenAI) is rapidly evolving, with a wide array of models available for businesses to leverage. These models can be broadly categorized into two types: closed-source (proprietary) and open-source models. Closed-source models, such as OpenAI’s GPT-4o, Anthropic’s Claude 3, or Google’s Gemini 1.5 Pro, are developed and maintained by private and public companies.
  |  By Wim Stoop
At a time when artificial intelligence (AI) and tools like generative AI (GenAI) and large language models (LLMs) have exploded in popularity, getting the most out of organizational data is critical to driving business value and carving out a competitive market advantage. To reach that goal, more businesses are turning toward hybrid cloud infrastructure – with data on-premises, in the cloud, or both – as a means to tap into valuable data.
  |  By Jeff Healey
Hadoop. The first time that I really became familiar with this term was at Hadoop World in New York City some ten or so years ago. There were thousands of attendees at the event – lining up for book signings and meetings with recruiters to fill the endless job openings for developers experienced with MapReduce and managing Big Data. This was the gold rush of the 21st century, except the gold was data.
  |  By Joe Rodriguez
Of all of the emerging tech of the last two decades, artificial intelligence (AI) is tipping the hype scale, causing organizations from all industries to rethink their digital transformation initiatives asking where it fits in. In Financial Services, the projected numbers are staggering. According to a recent McKinsey & Co.
  |  By Robert Hryniewicz
More than two-thirds of companies are currently using Generative AI (GenAI) models, such as large language models (LLMs), which can understand and generate human-like text, images, video, music, and even code. However, the true power of these models lies in their ability to adapt to an enterprise’s unique context. By leveraging an organization’s proprietary data, GenAI models can produce highly relevant and customized outputs that align with the business’s specific needs and objectives.
  |  By Venkat Rajaji
A constant flow of breaking news from the data lakehouse space is making notable tech headlines this week. On Tuesday, Databricks announced that it will acquire Tabular, a data management company founded by the creators of Apache Iceberg, Ryan Blue, Daniel Weeks, and Jason Reidfor. The deal was for an unconfirmed sum, but some reports suggest that amount to be between $1B and $2B (and allegedly outbidding Snowflake).
  |  By Remus Lim
Recently, Cloudera, alongside OCBC, were named winners in the“Best Big Data and Analytics Infrastructure Implementation” category at The Asian Banker’s Financial Technology Innovation Awards 2024. This recognition underscores the importance of trusted data when building AI and generative AI (GenAI) models and serves as a testament to the impact that reliable data can have in real world use cases.
  |  By Anthony Behan
It’s not a surprise that in today’s challenging economic landscape, rising costs pose a significant threat to the telecommunications industry. Consider that in 2022, Bain Capital was predicting that Telcos would grapple with increased personnel and escalating operating costs due to inflation. And here we are.
  |  By Robert Hryniewicz
We are excited to announce a tech preview of Cloudera AI Inference service powered by the full-stack NVIDIA accelerated computing platform, which includes NVIDIA NIM inference microservices, part of the NVIDIA AI Enterprise software platform for generative AI. Cloudera’s AI Inference service uniquely streamlines the deployment and management of large-scale AI models, delivering high performance and efficiency while maintaining strict privacy and security standards.
  |  By Dipto Chakravarty
In an era where artificial intelligence (AI) is reshaping enterprises across the globe—be it in healthcare, finance, or manufacturing—it’s hard to overstate the transformation that AI has had on businesses, regardless of industry or size. At Cloudera, we recognize the urgent need for bold steps to harness this potential and dramatically accelerate the time to value for AI applications.
  |  By Cloudera
We have bundled the collective Cloudera support teams, implementation skill and experience into our Observability platform resulting in our framework called validations. This framework offers a clear and detailed view of potential issues within your Cloudera Environment enabling you to fix it internally. Based on years of Cloudera support experience, we identified over 400 and growing issues our customers have encountered frequently, and provide remedies. This framework also provides detailed information to provide to our support team should this issue require a more complex solution.
  |  By Cloudera
Managing and forecasting cluster resource consumption costs is a complex task. Inefficient resource allocations and usage can lead to budget overruns and unexpected expenses. The challenge lies in gaining comprehensive insights into your resource consumption across different regions, departments, and user groups. It's also crucial for accurate financial planning. Cloudera Observability provides powerful financial governance capabilities to tackle these challenges effectively by providing unparalleled insight and control over your resource consumption and costs.
  |  By Cloudera
Firas Yasin, Global Alliance Manager of AI/ML at RedHat, introduces the RedHat and Cloudera partnership. Firas shares that customers are often missing the combination of security, scalability and support when deploying open-source solutions for their end-to-end data lifecycles. In this video, Firas highlights that together with RedHat OpenShift and Cloudera Data Platform, customers can achieve security and scalability through the joint solution, in addition to catalyzing on RedHat and Cloudera’s unrivaled support offerings.
  |  By Cloudera
Cloudera Observability provides the ability to define system rules and automate the appropriate action when those rules are broken through Auto Actions. This prevents for example that any one , query or job monopolizes the system, thereby impacting overall system performance.
  |  By Cloudera
Introduction to Apache Airflow: A brief overview for both beginners and enthusiasts. Best Practices and Use Cases: Learn from industry experts about optimizing your workflows and real-world use cases.
  |  By Cloudera
Unlock data potential with Cloudera's Open Data Lakehouse powered by Apache Iceberg. Break silos, centralize security, and accelerate AI, BI, and machine learning projects. Collaboration made efficient. Learn more at cloudera.com.
  |  By Cloudera
Ozone enables ingest, processing, exploration, efficient iterative training, and fine-tuning of LLMs that rely on huge structured and unstructured datasets. This demo illustrates that. We have deployed a CML AMP chatbot that uses an LLM, augmented with an existing knowledge base. The knowledge base is stored in Ozone and retrieved over S3.
  |  By Cloudera
No matter where you are in your data journey, Cloudera and AWS can help maximize your insights – providing flexibility, scale, and governance.
  |  By Cloudera
Join Ehrar Jameel, Head of Data and Analytics, as he demystifies the concept of data strategy in this enlightening snippet from our Art of Data Leadership series. In this segment, Ehrar delves into the fundamental question: What is a data strategy? Ready to delve deeper into the world of data leadership? Click here for the full Art of Data Leadership playlist and gain invaluable insights from Ehrar and other industry experts.
  |  By Cloudera
As organizations look to decrease cloud costs and run more efficiently, Cloudera DataFlow 2.6 introduced several improvements like Zookeeper-less deployments, new storage profiles, improved suspend behavior and vertical scaling.
  |  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.
  |  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.
  |  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.
  |  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.
  |  By Cloudera
The Modern Platform for Machine Learning and Analytics Optimized for Cloud.
  |  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.

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.