Systems | Development | Analytics | API | Testing

Confluent recently released its 2025 Data Streaming Report: Moving the Needle on AI Adoption, Speed to Market, and ROI. The report found that data streaming is delivering business value with 44% of IT leaders, driving up to 5x or more return on their data streaming investments. Explore the 2025 Data Streaming Report That said, as companies continue to expand their data streaming use cases, many struggle with nontechnical hurdles around scaling, setting up operations, and hitting organizational silos.

The advancement and widespread availability of new artificial intelligence (AI) capabilities—through platforms like the Databricks Data Intelligence Platform and Mosaic AI—has completely reset expectations for engineering teams across every industry. Business now moves at a new pace, demanding rapid delivery of intelligent, real-time applications—instead of slowly stitched-together systems solving problems defined and scoped months prior.

In the age of AI, the hunger for fresh, reliable data to power machine learning (ML) models and real-time analytics is insatiable. Yet, organizations frequently hit roadblocks when trying to bridge their operational data in motion, typically flowing through Apache Kafka, with their data at rest in data lakehouses. On one side, you have the data streaming platform, the central nervous system managing the real-time flow of business events.