The Next 5 Years of Stream Processing: Predictions

Stream processing in 2026-2031 will be shaped by SQL-first interfaces, disaggregated state (S3), AI agent integration, open table format convergence (Iceberg), and the commoditization of real-time data. Here are specific predictions for each trend.

Overview

Stream processing in 2026-2031 will be shaped by SQL-first interfaces, disaggregated state (S3), AI agent integration, open table format convergence (Iceberg), and the commoditization of real-time data. Here are specific predictions for each trend.

Key Points

• Streaming databases like RisingWave provide sub-second data freshness with SQL
• PostgreSQL compatibility means existing tools and skills transfer directly
• S3-based state storage provides durability, elastic scaling, and cost efficiency
• Native CDC eliminates middleware for database-to-streaming pipelines
• Iceberg integration bridges real-time serving with historical analytics

Architecture

The core pattern in all streaming architectures is:

Sources (CDC, Kafka) → Streaming Database (RisingWave) → Serving (PG protocol) + Storage (Iceberg)

This provides both real-time views (sub-second, via materialized views) and historical analytics (via Iceberg + Trino/DuckDB).

Implementation

-- The universal streaming pattern
CREATE SOURCE data_stream (...) WITH (connector='kafka', ...);
CREATE MATERIALIZED VIEW real_time_view AS SELECT ... FROM data_stream ...;
-- Query: SELECT * FROM real_time_view; (always fresh, sub-100ms)

Frequently Asked Questions

How does this relate to RisingWave?

RisingWave is a PostgreSQL-compatible streaming database that handles the processing and serving layers. It's open source (Apache 2.0), stores state on S3, and supports native CDC — making it the simplest path to production streaming.

Do I need Kafka for this?

Not always. RisingWave supports native CDC from PostgreSQL and MySQL without Kafka. For event-driven architectures with multiple consumers, Kafka remains valuable as a central event bus.