Author: Andrew Sellers

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.