The rise of AI brings unprecedented opportunities, deeply rooted in the transformative power of data. As organizations strive for smarter, faster outcomes—whether accelerating product launches, streamlining supply chains, improving customer experiences, or enhancing workforce productivity—they must address five key needs for enterprise AI adoption: Move/ Transform - To effectively adopt AI, companies need to bring together and transform data of all types from all sources.

After Twitter moved from Ruby to Scala in 2009, the story was born that Ruby on Rails can’t scale. The story goes that it lacks robustness, is a memory hog, and lacks the concurrency features you need to grow an application. This has been the prevailing wisdom for over a decade. And then along came Shopify, showing that, as Lutke says, Ruby on Rails is a framework that can process billions of events per day and evidently does scale. Ruby on Rails is an excellent candidate for scaling.

Google regularly updates its developer policies to make sure Google Play is a safe and trustworthy platform for everyone. While this is unquestionably necessary and essential to protect users and their data, keeping up with the latest changes to ensure applications remain safe and compliant can feel a little overwhelming for developers.

There are many ways AI can improve government case management processes. Examples include: AI capabilities often depend on a large language model (LLM), an advanced AI system that can understand, analyze, interpret, and generate human language. It uses deep learning techniques to predict and produce coherent text based on input prompts. A large language model trained on government agency data is capable of tasks like text extraction, translation, summarization, and conversational responses.

Ably was proud to celebrate our 8th birthday this year, and we’ve enjoyed reflecting on some of our achievements since launching in 2016. When we launched, we believed that realtime interactions would underpin our everyday digital experiences, rather than just augment them. And so we set out on a mission to build the definitive realtime experience platform.

Market data analytics has always been a classic use case for Apache Kafka. However, new technologies have been developed since Kafka was born. Apache Flink has grown in popularity for stateful processing with low latency output. Streamlit, a popular open source component library and deployment platform, has emerged, providing a familiar Python framework for crafting powerful and interactive data visualizations. Acquired by Snowflake in 2022, Streamlit remains agnostic with respect to data sources.

Apps are the way to democratize AI: to make it accessible to everyone and streamline customers’ experiences with faster time to insights. According to a recent IDC survey, AI applications is currently the largest category of AI software, accounting for roughly one-half of the market’s overall revenue in 2023.

Businesses that are best able to leverage data have a significant competitive advantage. This is especially true in financial services, an industry in which leading organizations are in constant competition to develop the most responsive, personalized customer experiences. Often, however, legacy infrastructure, data silos, and batch systems introduce significant technical hurdles.

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.

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.