The world of data is converging. The traditional divide between batch processing for historical analytics and stream processing for real-time insights is becoming increasingly blurry. Businesses demand architectures that handle both seamlessly. Enter the “Streamhouse” - an evolution of the Lakehouse concept, designed with streaming as a first-class citizen.

Today, we’ll introduce three key open-source technologies shaping this space: Apache Paimon™, Fluss, and Apache Iceberg. While each has unique strengths, their true power lies in how they can be integrated to build robust, flexible, and performant data platforms.

Stream processing technology is becoming more and more popular with companies big and small because it provides superior solutions for many established use cases such as data analytics, ETL, and transactional applications, but also facilitates novel applications, software architectures, and business opportunities. Beginning with traditional data infrastructures and application/data development patterns, this post introduces stateful stream processing and demonstrates to what extent it can improve the traditional development patterns. A consulting company can partner with her clients on their journeys of adopting stateful stream processing, and it can bring huge opportunities. Those opportunities are summarised at the end.