Materialized Views Are the Only Digital Twin Your AI Agent Needs

Introduction

Your AI agent needs fresh, pre-computed data to make decisions. It needs to query business entities (customers, orders, inventory) without scanning raw tables every time. It needs answers in milliseconds, not minutes. Every architect building agent-ready data infrastructure has this same set of requirements.

A growing number of vendors have started calling this a "digital twin." The term, borrowed from industrial IoT where it describes a virtual replica of a physical system, now gets applied to something far simpler: a pre-computed, always-current SQL view of your business data. Materialize has built an entire product narrative around this framing, publishing a series of blog posts arguing that AI agents need digital twins as an intermediary data layer.

The underlying technology is not new. What these vendors describe, a governed, always-fresh representation of business entities that agents can query on demand, is a materialized view maintained by incremental view maintenance (IVM). This is a well-studied database technique with decades of academic research behind it. In this post, we will break down the "digital twin" narrative, show exactly how materialized views deliver the same capabilities, and explain why a PostgreSQL-compatible streaming database like RisingWave is the practical choice for building your agent data layer.

What Are "Digital Twins" in the Context of AI Agents?

The term "digital twin" has a legitimate origin. In manufacturing and industrial IoT, a digital twin is a virtual model of a physical asset, a jet engine, a wind turbine, a factory floor, that mirrors the real thing in real time. Sensor data flows in, the model updates, and engineers use it for simulation, monitoring, and predictive maintenance. This is a well-defined concept with clear technical meaning.

The new marketing definition

In the context of AI agents, vendors have repurposed the term to mean something different. Materialize defines a digital twin as "an exact, always-current model of relevant business entities and their relationships, expressed in the language of the company: customers, orders, suppliers, routes, rather than low-level tables." They position it as a semantic abstraction layer that gives agents access to pre-computed, governed data products.

Strip away the branding and you get a clear technical description: pre-computed query results that update incrementally as source data changes, exposed through a SQL interface that agents can query. That is a materialized view. More specifically, it is a set of materialized views maintained through incremental view maintenance.

Call it what it is

There is nothing wrong with the underlying idea. Giving AI agents access to pre-computed, always-fresh business entities is sound architecture. The problem is wrapping a known database concept in novel terminology to create the impression of a proprietary breakthrough. IVM has been studied in database research since the 1990s. The PostgreSQL community has an active IVM extension project. Multiple streaming databases, including RisingWave and Materialize, implement it as their core execution model.

When someone tells you that your agents need a "digital twin platform," ask yourself: do I need a new category of infrastructure, or do I need materialized views that stay fresh?

How Do Materialized Views Deliver the Same Capabilities Without the Complexity?

Every feature that the "digital twin" narrative advertises maps directly to a well-known materialized view capability. Here is a side-by-side comparison:

The technical equivalence is not approximate. It is exact. A "digital twin of a customer" is a CREATE MATERIALIZED VIEW statement that joins customer data with their orders, support tickets, and account status.

SQL example: building a "customer digital twin" with RisingWave

Here is the concrete example. Suppose your agent needs an always-fresh, unified view of each customer, combining profile data, recent orders, and support ticket counts. In Materialize's language, this would be a "customer digital twin." In RisingWave, it is three SQL statements.

First, define your source tables. These can ingest directly from Kafka, PostgreSQL CDC, or other connectors:

-- Source: customer profiles from PostgreSQL CDC
CREATE TABLE customers (
    customer_id INT PRIMARY KEY,
    name VARCHAR,
    email VARCHAR,
    tier VARCHAR,
    created_at TIMESTAMPTZ
) WITH (
    connector = 'kafka',
    topic = 'postgres.public.customers',
    properties.bootstrap.server = 'broker:9092'
) FORMAT DEBEZIUM ENCODE JSON;

-- Source: order events from Kafka
CREATE TABLE orders (
    order_id INT PRIMARY KEY,
    customer_id INT,
    amount DECIMAL,
    status VARCHAR,
    ordered_at TIMESTAMPTZ
) WITH (
    connector = 'kafka',
    topic = 'orders',
    properties.bootstrap.server = 'broker:9092'
) FORMAT PLAIN ENCODE JSON;

-- Source: support tickets from Kafka
CREATE TABLE support_tickets (
    ticket_id INT PRIMARY KEY,
    customer_id INT,
    priority VARCHAR,
    status VARCHAR,
    created_at TIMESTAMPTZ
) WITH (
    connector = 'kafka',
    topic = 'support.tickets',
    properties.bootstrap.server = 'broker:9092'
) FORMAT PLAIN ENCODE JSON;

Now create the materialized view. This is the "digital twin":

CREATE MATERIALIZED VIEW customer_360 AS
SELECT
    c.customer_id,
    c.name,
    c.email,
    c.tier,
    COUNT(DISTINCT o.order_id) AS total_orders,
    COALESCE(SUM(o.amount), 0) AS lifetime_spend,
    MAX(o.ordered_at) AS last_order_at,
    COUNT(DISTINCT CASE WHEN st.status = 'open' THEN st.ticket_id END) AS open_tickets,
    COUNT(DISTINCT CASE WHEN st.priority = 'high' AND st.status = 'open'
        THEN st.ticket_id END) AS urgent_tickets
FROM customers c
LEFT JOIN orders o ON c.customer_id = o.customer_id
LEFT JOIN support_tickets st ON c.customer_id = st.customer_id
GROUP BY c.customer_id, c.name, c.email, c.tier;

Your AI agent queries it like any PostgreSQL table:

SELECT * FROM customer_360 WHERE customer_id = 42;

Expected output:

 customer_id |   name    |       email        | tier  | total_orders | lifetime_spend | last_order_at             | open_tickets | urgent_tickets
-------------+-----------+--------------------+-------+--------------+----------------+---------------------------+--------------+----------------
          42 | Jane Park | jane@example.com   | gold  |           17 |        4235.50 | 2026-03-28 14:22:00+00:00 |            2 |              1

That query returns in single-digit milliseconds because the result is pre-computed and incrementally maintained. Every time a new order arrives, a ticket is opened, or a customer profile changes, RisingWave updates only the affected rows in customer_360. No batch jobs. No scheduled refreshes. No "digital twin platform."

Note: SQL syntax verified against RisingWave documentation. Targets RisingWave 2.x.

Incremental view maintenance is not a breakthrough

IVM is the technique that makes this work. Instead of re-executing the full query when source data changes, the database computes only the delta: how does this new row, update, or deletion affect the existing materialized result? This is a well-known optimization studied extensively in database research. The foundational work on maintaining views incrementally dates back decades, and modern implementations (including both RisingWave and Materialize) build on this established theory.

Materialize's implementation uses Differential Dataflow, a framework developed by their co-founder Frank McSherry. RisingWave implements IVM through its own streaming execution engine with a shared-nothing, cloud-native architecture. Both achieve the same outcome: views that stay fresh as data changes. The difference is not in what they compute, but in how they are licensed, deployed, and priced.

Why Is a Streaming Database the Practical Choice for Agent-Ready Data?

If the core technology is the same, the decision comes down to practical concerns: compatibility, licensing, deployment flexibility, and cost.

PostgreSQL compatibility means agents work out of the box

AI agents interact with databases through standard tooling: LangChain's SQL agent, LlamaIndex query engines, or direct psycopg2 / pg driver connections. RisingWave speaks the PostgreSQL wire protocol. Any tool that connects to PostgreSQL connects to RisingWave without modification, without proprietary SDKs, and without vendor-specific adapters.

This matters because agent frameworks evolve fast. You do not want your data layer tied to a specific SDK that may not keep pace with the latest agent orchestration patterns. A standard PostgreSQL interface is the most portable choice.

Apache 2.0 vs. BSL licensing

RisingWave is licensed under Apache 2.0, the most permissive open-source license in common use. You can run it, modify it, embed it in your product, and deploy it anywhere with no restrictions.

Materialize uses the Business Source License (BSL 1.1), which converts to Apache 2.0 after four years. Until then, there are usage restrictions. Their free Community Edition caps at 24 GB memory and 48 GB disk. For production workloads, you need an Enterprise license.

For organizations building agent infrastructure that may run for years, licensing is not a minor detail. It determines whether you own your data layer or rent it.

Deployment flexibility vs. vendor lock-in

RisingWave runs as a single binary for development, scales across Kubernetes clusters for production, and is available as a fully managed cloud service (RisingWave Cloud). You choose your deployment model based on your requirements, not the vendor's business model.

This flexibility is particularly relevant for agent workloads where you may need to run the data layer close to your agent infrastructure, whether that is in your own VPC, on-premises, or across multiple cloud providers.

Cost comparison

The economics are straightforward. With an Apache 2.0 streaming database, you pay for compute and storage. With a proprietary "digital twin platform," you pay for compute, storage, and the platform margin.

Both products implement IVM. Both give you fresh materialized views. The difference is that one is open source with no strings attached, and the other requires you to evaluate license terms, edition tiers, and vendor pricing before you commit.

For a detailed technical comparison, see RisingWave vs. Materialize.

FAQ

What is a digital twin in the context of AI agents?

In the AI agent context, a digital twin refers to a pre-computed, always-current representation of business entities (customers, orders, inventory) that agents can query in real time. Despite the novel terminology, this is functionally identical to a set of incrementally maintained materialized views exposed through a SQL interface. The term originates from industrial IoT, where it describes a virtual replica of a physical asset.

How do materialized views replace a digital twin platform?

Materialized views provide the same core capabilities: real-time data freshness, semantic abstraction through SQL queries, and pre-computed results for fast agent reads. A streaming database like RisingWave maintains these views incrementally using IVM, updating only the rows affected by each data change. This eliminates the need for a separate "digital twin" product category.

Is incremental view maintenance the same technology behind digital twins?

Yes. Incremental view maintenance (IVM) is the core technique that both "digital twin platforms" and streaming databases use to keep pre-computed query results fresh. When source data changes, IVM computes only the delta rather than re-executing the full query. This is established database technology, not a proprietary innovation. Both RisingWave and Materialize implement IVM as their primary execution model.

When should I choose an open-source streaming database over a proprietary platform?

Choose an open-source streaming database when you need deployment flexibility (self-hosted, cloud, or hybrid), want to avoid license restrictions on production usage, need native connectors beyond Kafka, or require features like vector search for RAG-based agents. RisingWave's Apache 2.0 license means no usage caps, no conversion timelines, and no vendor lock-in.

Conclusion

The "digital twin for AI agents" narrative describes a real need: agents require fast access to fresh, pre-computed business data. But the solution is not a new product category. It is a materialized view, maintained incrementally, exposed through a standard SQL interface.

Key takeaways:

• What vendors call a "digital twin" is a set of materialized views maintained by incremental view maintenance (IVM), a well-studied database technique.
• You can build the same "customer 360" or "order digital twin" with a single CREATE MATERIALIZED VIEW statement in any streaming database.
• PostgreSQL compatibility ensures your agent framework (LangChain, LlamaIndex, or raw SQL) works without proprietary SDKs.
• Apache 2.0 licensing gives you unrestricted deployment, modification, and embedding rights, with no conversion timelines or edition tiers.
• The choice between vendors comes down to licensing, deployment flexibility, and cost, not the underlying technology.

Before you invest in a "digital twin platform," try building the same thing with standard SQL. You may find that the only twin you need is a well-designed materialized view.

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