Materialize Alternatives in 2026: Open Source Options for Streaming SQL

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        "text": "The main Materialize alternatives for streaming SQL in 2026 are RisingWave (Apache 2.0, adds vector support, built-in CDC, official MCP server), Apache Flink with a serving layer (complex but fully open source), and ksqlDB (Kafka-native only). RisingWave is the most direct alternative: it shares the same core concept (incremental SQL materialized views over streaming data) but is Apache 2.0 licensed, includes native CDC connectors, vector search, and AI integration via an official MCP server."
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        "text": "Materialize uses the Business Source License (BSL), which is source-available but not open source. The BSL restricts commercial use without a license agreement after a four-year conversion period. It is different from Apache 2.0 or MIT licenses, which allow unrestricted commercial use. RisingWave is licensed under Apache 2.0, which is a true open source license with no commercial restrictions."
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        "text": "Both RisingWave and Materialize implement streaming SQL with incremental materialized views and PostgreSQL wire protocol compatibility. Key differences: RisingWave is Apache 2.0 licensed (Materialize uses BSL), RisingWave has native CDC connectors for PostgreSQL, MySQL, MongoDB, and SQL Server without Debezium (Materialize supports fewer), RisingWave has built-in vector support and an official MCP server for AI agent integration (Materialize does not), and RisingWave can be self-hosted without restrictions. Materialize has a longer track record and strong correctness guarantees."
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        "text": "Yes, migration is relatively straightforward because both systems use PostgreSQL-compatible SQL and the same core concept: CREATE MATERIALIZED VIEW over streaming sources. Most SQL from Materialize works in RisingWave with minor syntax adjustments. The main migration steps are: recreate source definitions (RisingWave and Materialize have similar but not identical source syntax), recreate materialized views (usually direct port), and update connection strings (both use the PostgreSQL wire protocol, so drivers do not change)."
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        "text": "Streaming SQL lets you define queries over continuously arriving data using standard SQL syntax. Instead of running a query on a snapshot and getting a static result, a streaming SQL materialized view continuously updates as new data arrives. The result is always current without re-running the full query. This matters because it replaces custom streaming code (Flink jobs, Spark Streaming) with declarative SQL that any data engineer can write and maintain."
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Materialize built something genuinely new. In 2019, when most data teams were running overnight batch jobs to populate dashboards, Materialize showed that you could run SQL queries that stayed continuously up to date as data arrived from Kafka. The concept: incremental materialized views, maintained automatically, queryable like a normal database. No batch jobs. No Lambda architecture. Just SQL that was always fresh.

That idea was correct, and it influenced the entire streaming SQL category. By 2026, several systems implement the same concept. Some have caught up to Materialize on correctness. Some have surpassed it on licensing, ecosystem breadth, and feature set.

This article compares Materialize with its alternatives honestly, including what Materialize does well and where switching makes sense.

What Materialize Built and Why It Mattered

Materialize was founded in 2019 by Frank McSherry and Arjun Narayan. McSherry had previously developed Timely Dataflow and Differential Dataflow at Microsoft Research, two systems designed for maintaining incrementally updated computations over changing data. Materialize built a PostgreSQL-compatible SQL interface on top of that research foundation.

The result was a system that could answer questions like "how many active sessions are there right now" or "what is the current fraud rate by payment method" by maintaining the answer continuously, not computing it on demand. For use cases where freshness is critical and query volume is high, this is fundamentally better than running analytical queries against a transactional database or refreshing a materialized view on a schedule.

Materialize also made the correct bet on PostgreSQL compatibility. By speaking the PostgreSQL wire protocol, Materialize worked with existing BI tools, ORMs, and dashboards. Your team did not need to learn a new query language. You used psql, Metabase, or Grafana exactly as you would with a regular PostgreSQL database.

These decisions mattered. Materialize deserves credit for demonstrating that streaming SQL materialized views were practical, not just theoretical.

Why Teams Look for Alternatives in 2026

Teams evaluating Materialize alternatives in 2026 cite a consistent set of concerns. None of these are dealbreakers for every team, but they are real.

The licensing question

Materialize uses the Business Source License (BSL). This license makes source code available to read and modify, but restricts commercial use without a license agreement. After four years, each version converts to an Apache 2.0-equivalent license, but the current version is always under BSL.

The BSL is not deceptive. Materialize is transparent about it. But it is not open source in the OSI sense, and it matters to teams with legal requirements to use only true open source software. Organizations with open source policies that require Apache 2.0 or MIT licensing cannot use Materialize without a commercial agreement.

RisingWave is Apache 2.0. You can run it in production commercially, modify it, redistribute it, and embed it in other products without a license agreement.

CDC source breadth

Materialize supports Kafka as its primary streaming source and PostgreSQL CDC for database change capture. This covers many use cases, but not all. Teams with MySQL, MongoDB, or SQL Server sources need workarounds: typically running Debezium to route changes through Kafka before they reach Materialize.

RisingWave has built-in CDC connectors for PostgreSQL, MySQL, MongoDB, and SQL Server. No Debezium required, no Kafka intermediary needed for CDC.

AI and vector integration

In 2023, AI integration was an edge case. In 2026, it is a core requirement. AI agents need access to fresh data. Retrieval-augmented generation (RAG) systems need embeddings stored near the data they represent. Feature stores need sub-second freshness.

Materialize does not have built-in vector support or embedding functions.

RisingWave has:

• Native vector column types with HNSW indexing
• openai_embedding() function for computing embeddings directly in SQL
• An official MCP server (risingwavelabs/risingwave-mcp) that exposes RisingWave data and materialized views to AI agents

This gap matters more in 2026 than it would have three years ago.

Why 2026 Is Different

Three forces have shifted the streaming SQL landscape since Materialize launched.

The MCP protocol changed how AI agents consume data. The Model Context Protocol (MCP) standardized the way AI agents connect to data sources. A database with an MCP server becomes natively accessible to any AI agent running in Claude, Cursor, or any MCP-compatible host. RisingWave's official MCP server means an AI agent can query live materialized views directly, without a custom integration layer. This is a new capability that did not exist at Materialize's founding.

Vector search became a database feature, not a separate system. In 2023, teams built separate vector databases (Pinecone, Weaviate, Qdrant) alongside their streaming systems. In 2026, the expectation is that your database handles vectors. RisingWave's HNSW index and openai_embedding() let you store, index, and search embeddings in the same system that maintains your streaming materialized views. This is a significant architectural simplification.

Apache Iceberg became the standard lakehouse format. Data lakehouses built on Iceberg need streaming ingest. Teams running Materialize for real-time serving often need a parallel Iceberg pipeline for historical analysis and ML training. RisingWave's built-in Iceberg sink lets one system serve both the real-time serving layer and the historical data lake.

Comparison Table: RisingWave vs Materialize

Feature-by-Feature Comparison with SQL Examples

Defining a streaming source

Both systems use SQL to define sources, but the syntax differs slightly.

Materialize:

-- Materialize: Kafka source
CREATE SOURCE orders_source
FROM KAFKA CONNECTION kafka_conn (TOPIC 'orders')
FORMAT JSON
INCLUDE TIMESTAMP AS event_time;

-- Materialize: PostgreSQL CDC source
CREATE SOURCE pg_source
FROM POSTGRES CONNECTION pg_conn (PUBLICATION 'mz_publication')
FOR TABLES (public.orders, public.customers);

RisingWave:

-- RisingWave: Kafka source
CREATE SOURCE orders_source (
    order_id BIGINT,
    customer_id BIGINT,
    total DECIMAL,
    event_time TIMESTAMPTZ
) WITH (
    connector = 'kafka',
    topic = 'orders',
    properties.bootstrap.server = 'kafka:9092',
    scan.startup.mode = 'latest'
) FORMAT PLAIN ENCODE JSON;

-- RisingWave: PostgreSQL CDC source (no Debezium needed)
CREATE SOURCE pg_cdc_source WITH (
    connector = 'postgres-cdc',
    hostname = 'prod-db.internal',
    port = '5432',
    username = 'cdc_user',
    password = '${PG_PASSWORD}',
    database.name = 'ecommerce',
    slot.name = 'risingwave_slot'
);

CREATE TABLE orders FROM SOURCE pg_cdc_source TABLE 'public.orders' (
    id BIGINT PRIMARY KEY,
    customer_id BIGINT,
    total DECIMAL,
    status VARCHAR,
    updated_at TIMESTAMPTZ
);

The concepts are identical: define a source, specify the format, optionally map tables. The syntax is different enough that migration requires updating source definitions, but the mental model transfers directly.

Defining a materialized view

This is where both systems are most similar, because both use standard SQL.

Materialize:

-- Materialize
CREATE MATERIALIZED VIEW revenue_by_status AS
SELECT
    status,
    COUNT(*) AS order_count,
    SUM(total) AS total_revenue
FROM orders
GROUP BY status;

RisingWave:

-- RisingWave (identical SQL in this case)
CREATE MATERIALIZED VIEW revenue_by_status AS
SELECT
    status,
    COUNT(*) AS order_count,
    SUM(total) AS total_revenue
FROM orders
GROUP BY status;

For simple aggregations and joins, the SQL is often identical. Both systems use PostgreSQL-compatible syntax and support the same SQL constructs: GROUP BY, JOIN, window functions, subqueries.

Differences emerge in specific features: Materialize has temporal filter semantics tied to its Timely Dataflow consistency model; RisingWave has watermark-based time windows and different handling of out-of-order events. For most real-world use cases, these differences do not surface.

Window functions and time-based aggregations

Materialize:

-- Materialize: tumbling window over Kafka timestamps
CREATE MATERIALIZED VIEW orders_per_minute AS
SELECT
    date_trunc('minute', event_time) AS window_start,
    COUNT(*) AS order_count,
    SUM(total) AS revenue
FROM orders
GROUP BY date_trunc('minute', event_time);

RisingWave:

-- RisingWave: tumbling window with explicit watermark handling
CREATE MATERIALIZED VIEW orders_per_minute AS
SELECT
    window_start,
    COUNT(*) AS order_count,
    SUM(total) AS revenue
FROM TUMBLE(orders, event_time, INTERVAL '1' MINUTE)
GROUP BY window_start;

RisingWave uses the TUMBLE table function for time-windowed aggregations, which is explicit about the window semantics. Materialize's approach uses standard GROUP BY with time truncation and Materialize's internal temporal logic handles window boundaries.

Vector search (RisingWave only)

This feature exists only in RisingWave. It has no Materialize equivalent.

-- RisingWave: store product embeddings computed from Kafka events
CREATE MATERIALIZED VIEW product_embeddings AS
SELECT
    product_id,
    product_name,
    category,
    openai_embedding(
        model => 'text-embedding-3-small',
        input => product_name || ' ' || category || ' ' || description
    ) AS embedding
FROM products;

-- Create HNSW index for fast approximate nearest neighbor search
CREATE INDEX ON product_embeddings USING hnsw (embedding vector_cosine_ops);

-- Find the 10 most similar products to a query
SELECT product_name, category
FROM product_embeddings
ORDER BY embedding <=> openai_embedding(
    model => 'text-embedding-3-small',
    input => 'wireless noise-canceling headphones'
)
LIMIT 10;

This pattern replaces a separate vector database. Product embeddings are computed automatically as new products arrive in the Kafka stream, indexed for fast similarity search, and queryable with the same psql connection you use for everything else.

Other Alternatives: Apache Flink and ksqlDB

Apache Flink

Flink is the most powerful streaming processing framework available. It is Apache 2.0 licensed, has a large community, and can express almost any streaming computation via its Java and Scala APIs or Flink SQL.

Flink is not a direct Materialize alternative because it does not include a serving layer. Flink processes data and writes results to an output store (PostgreSQL, Kafka, Elasticsearch). You then query that output store. This means Flink for analytics requires: Flink cluster plus a separate database for serving. Two systems instead of one.

Flink SQL is also less expressive than RisingWave's PostgreSQL-compatible SQL. Flink SQL covers common patterns but lacks the full PostgreSQL function library. Teams with SQL-first analytics workflows typically find RisingWave easier to work with than Flink SQL.

Flink is the right choice when you need custom Java operators, complex event processing (CEP), or the broadest ecosystem integration. It is not the right choice when you want the simplicity of "connect to it like a database."

ksqlDB

ksqlDB is Confluent's streaming SQL system built on top of Kafka Streams. It is Kafka-native: it can only process data that flows through Kafka. You cannot use ksqlDB with PostgreSQL CDC directly or with non-Kafka sources.

ksqlDB is a reasonable choice for teams deeply embedded in the Confluent ecosystem who want SQL-based stream processing without leaving Kafka. For teams evaluating Materialize alternatives for broader use cases, ksqlDB's Kafka-only constraint is usually a dealbreaker.

Migration Guide: Moving from Materialize to RisingWave

Migration is straightforward because both systems share the same mental model. Here is the practical path.

What transfers directly

• All SQL logic in materialized views (GROUP BY, JOIN, window functions, subqueries)
• PostgreSQL client connections (drivers and connection libraries do not change)
• BI tool integrations (Metabase, Grafana, Superset all connect via PostgreSQL protocol)
• dbt models (dbt connects to both systems via the PostgreSQL connector)

What requires updating

Source definitions. Materialize and RisingWave use different syntax for defining sources. You will need to rewrite source DDL. The concepts map 1:1, but the specific keywords and parameters differ (as shown in the examples above).

Sink definitions. If you use Materialize's sink syntax for writing to Kafka or other destinations, you will need to rewrite those as RisingWave sinks.

Temporal filter patterns. Materialize has specific patterns for temporal filters using mz_now(). RisingWave uses watermarks and NOW(). If your views use these features heavily, review each view.

Migration steps

1. Inventory all sources, materialized views, and sinks in your Materialize deployment.
2. Run RisingWave alongside Materialize in staging (both listen on PostgreSQL port, use different hosts).
3. Recreate sources in RisingWave syntax. Test that the source receives events.
4. Recreate materialized views one by one. Compare output between Materialize and RisingWave for the same input.
5. Update connection strings in applications and BI tools to point to RisingWave.
6. Decommission Materialize after validation.

The most time-intensive step is validating output equivalence, not writing the SQL. Both systems should produce the same results for the same inputs given the same query logic.

When to Stay on Materialize

This comparison is honest. There are real reasons to stay on Materialize.

You have significant existing investment. If your team has spent a year building and tuning Materialize deployments, the migration cost is real. Switching for switching's sake is not a good reason. Evaluate the total cost of migration against the benefits of switching.

You rely on Materialize Cloud's managed experience. Materialize Cloud is a mature managed service. If you want a hosted, fully managed streaming SQL database and your organization can accept BSL licensing, Materialize Cloud is a reasonable choice. RisingWave Cloud is newer but growing.

You depend on Timely Dataflow's specific consistency guarantees. Materialize's consistency model, built on Timely Dataflow and Differential Dataflow, has specific properties around linearizability and consistency that have been studied academically and tested in production. If your use case has unusual correctness requirements that you have specifically validated against Materialize's consistency model, switching requires re-validating those properties in RisingWave.

Your Kafka sources are your only sources. If you only need Kafka as a source and PostgreSQL CDC, Materialize and RisingWave have similar coverage. The CDC breadth advantage of RisingWave only matters if you have MySQL, MongoDB, or SQL Server sources.

Key Takeaways

Materialize built a genuinely good product and pioneered a category. Streaming SQL materialized views are now a proven concept, and Materialize deserves credit for proving it.

In 2026, the reasons to evaluate alternatives are concrete:

• Licensing: Materialize BSL versus RisingWave Apache 2.0. This matters for organizations with open source policies and for teams who want the option to self-host and modify without a commercial agreement.
• CDC source breadth: RisingWave has native CDC for MySQL, MongoDB, and SQL Server in addition to PostgreSQL. Materialize requires Kafka (via Debezium) for those sources.
• AI integration: RisingWave's vector support, openai_embedding(), and official MCP server are purpose-built for AI workloads in 2026. Materialize does not have these features.
• Iceberg integration: RisingWave sinks directly to Apache Iceberg. Materialize does not have a built-in Iceberg sink.
• Cost: Self-hosted RisingWave has no licensing cost. Materialize's commercial terms apply to managed cloud and to production use under BSL at scale.

For teams building new streaming SQL systems in 2026, RisingWave is worth evaluating seriously. For teams migrating from Materialize, the SQL migration is manageable and the operational benefits of Apache 2.0 licensing and broader CDC support are real. For teams with significant Materialize investment and no pressing reason to switch, staying put is a legitimate choice.

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