Author: Andrew Sellers

While generative AI is driving the need for stronger data governance, it can also help to meet that need.

RAG must be implemented in a way that provides accurate and up-to-date information and in a governed manner that can be scaled across apps and teams.

In the past, technology served as a supportive function for business. Over time, it has become the business itself. A similar shift is happening with data streaming—data streaming is now a critical foundation of modern business. And this year is an inflection point for data streaming platforms

I’ve worked with artificial intelligence for nearly 20 years, applying technologies spanning predictive modeling, knowledge engineering, and symbolic reasoning. AI’s tremendous potential has always felt evident, but its widespread application always seemed to be just a few more years away.

As companies increase their use of real-time data, we have seen the proliferation of Kafka clusters within many enterprises. Often, siloed application and infrastructure teams set up and manage new clusters to solve new use cases as they arise. In many large, complex enterprises, this organic growth

While the promise of AI has been around for years, there’s been a resurgence thanks to breakthroughs across reusable large language models (LLMs), more accessible machine learning models, more data than ever, and more powerful GPU capabilities. This has sparked organizations to accelerate their AI

Today, use of data streaming technologies has become table stakes for businesses. But with data streaming technologies, patterns, and best practices continuing to mature, it’s imperative for businesses to stay on top of what’s new and next in the world of data streaming.

Real-time AI is the future, and AI/ML models have demonstrated incredible potential for predicting and generating media in various business domains. For the best results, these models must be informed by relevant data.

This year, we crossed an important threshold: data streaming is now considered a business requirement for organizations across many industries. Findings from the 2023 Data Streaming Report show that 72% of the 2,250 IT leaders surveyed are using data streaming to power mission-critical systems.

Experienced technology leaders know that adopting a new technology can be risky. Often, we are unable to distinguish between those investments that will be transformational and those that won’t be worthwhile. This post examines how one can decide if event streaming makes sense for them.

Change data capture (CDC) converts all the changes that occur inside your database into events and publishes them to an event stream. You can then use these events to power analytics, drive operational use cases, hydrate databases, and more. The pattern is enjoying wider adoption than ever before.

Over the last decade, there’s been a massive movement toward digitization. Enterprises are defining their business models, products, and services to innovate, thrive, and compete by being able to quickly discover, understand, and apply their data assets to power real-time use cases.