Introduction: Kafka Alone Is Not Enough
Many engineers adopt Apache Kafka to process real-time data. However, Kafka is a pipeline that 'delivers' data, not a database that 'queries' and 'stores' it. To instantly aggregate real-time stock prices or detect anomalies from a second ago and query them with SQL, a Streaming DBMS is essential. This post goes beyond simply letting data flow to provide an in-depth analysis of next-generation streaming DB technologies that execute SQL on flowing data to derive immediate insights, along with 2025 trends.
Deepening Core Principles: Reinterpreting Materialized Views
In traditional RDBMS, queries run on static data, but in Streaming DBMS, queries exist as 'Long-running' states, and data passes through them.
Continuous Query and Incremental Processing
The heart of a Streaming DBMS is Incremental Processing. Instead of recalculating the entire dataset every time new data arrives, it calculates only the changed part (Delta) to update the result. This guarantees millisecond (ms) latency even with millions of events per second (EPS).
Real-time Nature of Materialized Views
Modern streaming DBs like Materialize or RisingWave offer Real-time Materialized Views. Since complex Joins or Aggregations are always kept up-to-date, applications can instantly get the latest aggregated results by simply executing SELECT * FROM view. This is a revolutionary approach that eliminates the complexity of cache management.
2025 Trend: Integration of Stream and Batch
The buzzword in 2025 data engineering is the 'Completion of Kappa Architecture'. In the past, the Lambda architecture, which separated the 'Speed Layer' for real-time processing and the 'Batch Layer' for accuracy, was mainstream. Now, the trend is to perform both past data reprocessing (Backfill) and real-time processing with a single streaming engine.
In particular, the concept of a Streaming Warehouse combined with Apache Flink is emerging. As data warehouses like Snowflake and BigQuery strengthen their streaming capabilities, the stereotype that "analysis is only possible a day later" is being broken.
Practical Application: Integration with CDC (Change Data Capture)
The most effective way to use streaming DBs in practice is to receive changes (CDC) from existing RDBMS as a stream and process them in real-time.
- Eliminating Real-time ETL: Instead of complex Airflow batch jobs, transform data with SQL within the streaming DB and sink it.
- Microservices Data Synchronization: It is optimal for implementing the CQRS pattern, where DB changes in the 'Order Service' are streamed and reflected in the 'Delivery Service' DB in real-time.
Expert Insight
💡 Data Engineer's Note
Tip for Tech Adoption: "Don't try to stream everything." Streaming is expensive. While essential for 'Fraud Detection' or 'Inventory Management' requiring second-level decisions, batch processing is still efficient for daily reports. Define the business's 'Data Freshness' requirements first.
Future Outlook: In the future, LLMs (Large Language Models) will be directly connected to streaming data. If you ask, "Summarize the anomalies in our factory right now," 'Streaming RAG' technology, where AI analyzes real-time log streams to answer, will become commonplace.
Conclusion: Dropping Anchor in Flowing Data
If past data management was fishing in 'stagnant water (Data Lake)', Streaming DBMS is casting a net in a 'flowing river (Data Stream)'. If data doesn't create value the moment it's generated, it becomes dead data. Engineers who understand Kafka, Flink, and the latest Streaming DB technologies will be the protagonists of the 2025 data market.