AI + Materialized Views

Materialized Views for AI Agents

Materialized views are the practical foundation for agent-ready data. RisingWave maintains pre-computed, always-current SQL views that agents query in milliseconds — no "digital twin" abstraction needed, just SQL you already know.

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Pre-Computed

Results

Agents read stored results, not raw data. Complex joins and aggregations run continuously in the background

Always

Current

Incrementally maintained views — no REFRESH command, no schedule, no staleness window

Standard SQL

Interface

CREATE MATERIALIZED VIEW and SELECT — every PostgreSQL tool and driver works out of the box

Composable

Data Products

Build views on top of views. Layer aggregations, filters, and joins for different agents

Comparison

How do RisingWave materialized views compare to "digital twin" platforms?

Some vendors market proprietary abstractions as "digital twins" — but underneath, they are materialized views with vendor-specific APIs and pricing. RisingWave provides the same continuously-updated data representations using standard SQL, open protocols, and transparent pricing.

Factor	"Digital Twin" Platforms	RisingWave Materialized Views
Query Language	Proprietary API/DSL	Standard PostgreSQL SQL
Data Freshness	Incremental (varies)	Incremental (sub-second)
Pricing Model	Per-view / compute-hour	Transparent resource-based
Lock-in Risk	High (proprietary protocol)	None (PostgreSQL protocol)
Tool Ecosystem	Vendor-specific connectors	Any PostgreSQL client
Learning Curve	New abstractions to learn	SQL you already know

→No proprietary APIs or DSLs to learn — standard PostgreSQL SQL throughout
→No vendor lock-in — any PostgreSQL client connects to RisingWave
→Transparent resource-based pricing instead of per-view or per-query fees
→Sub-second incremental updates without manual REFRESH commands

Why Materialized Views

Why are materialized views the ideal data layer for AI agents?

AI agents need fast, predictable access to pre-computed data. Materialized views deliver exactly this — SQL-defined data products that are always current, queryable in milliseconds, and accessible through standard PostgreSQL protocol. No custom APIs, no vendor lock-in, no learning curve.

Pre-Computed Results

Agents read stored results, not raw data. Complex joins and aggregations run continuously in the background, not at query time

Always Current

RisingWave incrementally maintains views as source data changes — no REFRESH command, no schedule, no staleness window

Standard SQL Interface

Define views with CREATE MATERIALIZED VIEW, query with SELECT. Every PostgreSQL tool and driver works out of the box

Composable Data Products

Build views on top of views. Layer aggregations, filters, and joins to create purpose-built data products for different agents

Getting Started

How do you build agent-ready data products with materialized views?

Building agent-ready data products follows a straightforward pattern: create sources from your streaming data, define materialized views that compute the data shape agents need, and let agents query those views via PostgreSQL. Each view becomes a self-describing, always-fresh data product.

→Connect streaming sources with CREATE SOURCE — Kafka, PostgreSQL CDC, S3, or HTTP endpoints
→Define materialized views with SQL that joins, aggregates, and filters data into agent-ready shapes
→Layer views on views to build complex data products from simple, testable components
→Query from any agent framework using standard PostgreSQL drivers — LangChain, CrewAI, custom code
→Monitor view freshness and performance through built-in system catalogs

Related Resources

Incremental Materialized Views →Context Engineering Database →Real-Time Data for AI Agents →

Frequently Asked Questions

What is a materialized view in the context of AI agents?

How are RisingWave materialized views different from traditional database views?

Do I need to learn a proprietary query language?

Can materialized views replace a 'digital twin' platform for agent data?

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