LLMs are the foundation of gen AI applications. To effectively operationalize and de-risk LLMs and ensure they bring business value, organizations need to consider not just the model itself, but the supporting infrastructure, including GPUs and operational frameworks. By optimizing them to your use case, you can ensure you are using an LLM that is the right fit to your needs.

In the last year, we’ve seen the explosion of AI in the enterprise, leaving organizations to consider the infrastructure and processes for AI to successfully—and securely—deploy across an organization. As we head into 2025, it’s clear that next year will be just as exciting as past years. Here, Cloudera experts share their insights on what to expect in data and AI for the enterprise in 2025.

In the fast-paced world of data, where volume, variety, and velocity are constantly pushing boundaries, organizations are facing unprecedented challenges in integrating and harnessing data at scale effectively. Gartner just published the 2024 Magic QuadrantTM for Data Integration Tools, which recognized Confluent as a Challenger. Previously, Confluent was positioned as a Niche player in the 2023 Magic Quadrant for Data Integration Tools.

AUSTIN, TEXAS, USA, December 13, 2024 - Yellowfin, a leading global provider of business intelligence (BI) and analytics solutions, has officially launched version 9.14, bringing customers a host of significant improvements aimed at enhancing user experience, reporting, and REST API functionality.

Yellowfin’s mission has always been to simplify analytics and make actionable insights easily accessible for everyone. With the release of Yellowfin 9.14, we’re focusing on providing developers with the tools to integrate our advanced analytics into their software seamlessly, expand integration capabilities, and provide a significant step forward in making embedded business intelligence (BI) more accessible and impactful.

As organizations mature in their execution of data and AI initiatives, a burning question remains: How do we measure the effectiveness of our teams and our impact on the business? This isn’t the perennial “What’s my data worth?” dilemma often asked rhetorically and answered theoretically. Today’s challenge is concrete: to define and track the metrics used to justify continued investment in data and AI innovation.

In today’s fast-paced technological landscape, staying ahead means more than just keeping up with the latest trends—it requires a fundamental shift in how businesses operate in increasingly digital spaces. AppDirect, a digital-native company at the forefront of innovation, has fully embraced this digital paradigm, aligning itself with modern business approaches that enhance both operational efficiency and customer experience.

As a data engineer who has designed and managed ETL (Extract, Transform, Load) processes, I've witnessed firsthand the transformative impact of cloud-based solutions on data integration. Amazon Web Services (AWS) offers a suite of tools that streamline ETL workflows, enabling mid-market companies to move the big data to data stores such as Snowflake, data lake from different sources depending on use cases.

As a data engineer who has worked on building and managing various technical aspects of data pipelines over the years, I've navigated the intricate landscape of data integration, transformation, and analysis. In mid-market companies, where data-driven decision-making is pivotal, constructing efficient and reliable database pipelines allows you to store data in cloud data warehouses and carry out better data analysis or machine learning models.