Systems | Development | Analytics | API | Testing

Getting Started With Cloudera Open Data Lakehouse on Private Cloud

Cloudera recently released a fully featured Open Data Lakehouse, powered by Apache Iceberg in the private cloud, in addition to what’s already been available for the Open Data Lakehouse in the public cloud since last year. This release signified Cloudera’s vision of Iceberg everywhere. Customers can deploy Open Data Lakehouse wherever the data resides—any public cloud, private cloud, or hybrid cloud, and port workloads seamlessly across deployments.

Unleash cloud-native analytics and AI on-premises with Cloudera

Unlock the power of your on-premises data with Cloudera for private cloud. Harness cloud-native agility, flexibility, and cost efficiency within your private open data lakehouse for unparalleled access and control over your data. Build a foundation of secure, accurate, and trusted data for precise business insights and of course, trusted AI. Unleash the full potential of your data with Cloudera's Private Cloud Data Services.

The Value of an Enterprise Data Warehouse

Enterprise Data Warehouses (EDW) have emerged as a pivotal component for businesses striving to harness the power of data analytics and business intelligence. As technology advances, the complexity and volume of data sets have surged, accentuating the role of an EDW. This guide offers a deep dive into the intricacies of the Enterprise Data Warehouse, its significance, functionality, and the considerations for its implementation.

The Evolution of Search: How Multi-Modal LLMs Transcend Vector Databases

As we venture deeper into the data-driven era, the traditional systems we have employed to store, search, and analyze data are being challenged by revolutionary advancements in Artificial Intelligence. One such groundbreaking development is the notable advent of Large Language Models (LLMs), specifically those with Multi-Mod[a]l abilities (e.g., Image & Audio).

Rev Up Your Lakehouse: Lap the Field with a Databricks Operating Model

In this fast-paced era of artificial intelligence (AI), the need for data is multiplying. The demand for faster data life cycles has skyrocketed, thanks to AI’s insatiable appetite for knowledge. According to a recent McKinsey survey, 75% expect generative AI (GenAI) to “cause significant or disruptive change in the nature of their industry’s competition in the next three years.” Next-gen AI craves unstructured, streaming, industry-specific data.

Snowflake and Partners Develop Award-Winning Solution to Give Telecoms and Consumers the Power to Reduce Carbon Emissions with Generative AI

In the age of climate consciousness, industries worldwide are grappling with the urgent need to reduce their carbon footprints. One industry that has come under increased scrutiny is telecommunications, where Scope 3 emissions, or the indirect emissions that occur in a company’s value chain that the company has no direct control over, alone account for a staggering 85% of a typical telecom company’s carbon footprint.

Accelerate the Data Analytics Life Cycle with Unravel

Organizations want to get faster value from AI/ML. In order to do that, they need to go through a data lifecycle -- from data ingestion, curation and refinement, to production data pipeline development and deployment, and then model creation and model deployment. With this in mind, Unravel is hosting a live event to help you quickly go from start to finish. This is your opportunity to learn how you can leverage Unravel’s purpose-built AI to accelerate your full data lifecycle.