It’s a truism that global supply chains are complex. The process of sourcing raw materials, transforming them into finished products, and distributing them to customers encompasses numerous systems (e.g., ERPs, WMSs, and TMSs). All systems within “the supply chain” are trending in the same direction; they’re aiming to be more efficient, resilient, and agile. Various technological developments have facilitated this directional trend.

This post was originally published on Medium on Nov. 20, 2024. The rise of agentic AI has fueled excitement around agents that autonomously perform tasks, make recommendations, and execute complex workflows blending AI with traditional computing. But creating such agents in real-world, product-driven environments presents challenges that go beyond the AI itself.

As we step into the new year, it’s the perfect time to reflect on the exciting advancements Confluent made in 2024. Our Q4 launch wrapped up the year with a host of powerful features paving the way for even more innovation in 2025. This launch is all about delivering private networking and enhanced security across the data streaming platform.

This article was originally published on The New Stack on Dec. 19, 2024. Imagine driving a car with a headset that only updates your view every five minutes instead of providing a continuous video stream. How long would it take before you crashed? While this type of batch processing clearly doesn’t work in the real world, it's how many systems operate today. Batch processing, born out of outdated technology constraints, forces applications to rely on static, delayed data.

In building the next generation of web agents, we need the simplest, fastest way to extract web data at scale for production use cases. And for every new generative AI (GenAI) application, developers and businesses need reliable data to power the underlying models. But getting that data in a usable, trustworthy format? That’s where things get complicated.

Interest in AI has surged since 2020 and has dominated conversations across headlines and boardrooms ever since. So it’s unsurprising that business development has followed suit — 81% of IT leaders listed AI and machine learning as an important or top priority in their 2024 budgets, according to survey results in Confluent’s Data Streaming Report. But is all this attention and investment leading to a near-term future where AI is ubiquitous and functions as intended?

In Part I of this blog series, we laid the foundation for understanding how data fuels AI and why having the right data at the right time is essential for success. We explored the basics of AI, including its reliance on structured and unstructured data, and how streaming data can help unlock its full potential.

In this final part of our blog series, we bring everything together to unlock the full potential of AI with real-time data streaming and event-driven architecture (EDA). In Part I, we explored how data fuels AI, laying the foundation for understanding AI’s reliance on fresh, relevant information.

Welcome to this three-part blog series where we explore how to transform generative AI (GenAI) into a powerful, real-time solution that drives business success.

Integrating OpenAI with BigQuery unlocks powerful possibilities for AI-powered data analysis and advanced insight generation. This guide explores how the OpenAI BigQuery integration enables businesses to transform their data warehouses into intelligent analytics powerhouses.