“Turning the database inside-out” was Martin Kleppmann’s approach to better understanding stream processing at scale. It introduced materialized views on the stream providing a database experience to streaming workloads. But there is another perspective to this idea: the database. From the database perspective, all the systems and features that support streaming workloads already exist in the database. So why not start streaming adoption from there?

**Why Should Someone Care?**
Streaming solutions face adoption challenges due to its inherent complexities. Real-time data, continuous streams, and distributed systems introduce intricacies that can be daunting for organizations. The need for handling events in sequence, managing out-of-order data, ensuring fault tolerance, and orchestrating complex workflows all contribute to the difficulty in adopting stream processing. Additionally, adapting existing infrastructures to accommodate the dynamic nature of streaming data often requires a significant shift in mindset and technology. Overcoming these hurdles demands a thorough understanding of the unique demands of streaming and a strategic approach to implementation.

**Technical Details Covered:**
- Materialized views
- IVM - incremental view maintenance
- Write ahead logs
- Stream databases
- HTAP databases
- Consistency issues with stream processing

**Takeaways:**
- Attendees can expect to gain an understanding of the strategic importance of getting developers to adopt streaming and real-time analytics by providing a familiar database experience.

- A solution model for real-time analytical use cases.

- All databases are streaming databases.