RisingWave vs Materialize vs ksqlDB: Best Streaming Databases 2026

Why Self-Host a Streaming Database?

Modern applications generate continuous streams of data — click events, sensor readings, financial transactions, log entries. Traditional batch databases cannot keep up with the need for sub-second insights on data that is still in motion. Streaming databases solve this by treating data as an infinite, continuously updating stream. Instead of running queries against static tables, you define materialized views that incrementally update as new events arrive, delivering real-time analytics with standard SQL.

Self-hosting a streaming database gives you full control over data residency, eliminates vendor lock-in from managed services, and reduces costs at scale. All three tools covered here — RisingWave, Materialize, and ksqlDB — are open-source, self-hostable, and designed for real-time data processing. They sit between traditional databases and stream processing frameworks like Apache Flink, offering a simpler SQL-native interface for continuous computation.

What Is a Streaming Database?

A streaming database is a system that processes data in motion using incremental computation. Unlike a traditional RDBMS that stores data at rest and runs queries over the full dataset, a streaming database:

• Ingests events continuously from sources like Kafka, Redpanda, or direct file/HTTP inputs
• Maintains materialized views that update incrementally with each new event
• Supports standard SQL — no custom DSL or programming required
• Delivers low-latency results — typically sub-millisecond query responses on live data

The key architectural difference from stream processors (like Apache Flink or Beam) is that streaming databases handle state management, windowing, and incremental computation internally. You write SQL; the engine handles the rest. For a comparison of traditional stream processing frameworks, see our Apache Flink vs Bytewax vs Apache Beam guide.

RisingWave

RisingWave is an open-source streaming database built in Rust by RisingWave Labs. It launched in 2021 as a fork of Materialize and has since diverged significantly, adding native Kafka integration, built-in connectors, and a distributed architecture optimized for cloud-native deployments.

Key characteristics:

• Written in Rust for performance and memory safety
• Distributed architecture with separate compute and storage layers
• Built-in connectors for Kafka, Redpanda, PostgreSQL, MySQL, S3, and more
• Supports tumbling, hopping, and session windows
• Compatible with PostgreSQL wire protocol — any PostgreSQL client works
• 8,900+ GitHub stars, active development with 200+ contributors

RisingWave Architecture

RisingWave separates compute and storage:

• Frontend (CN): Parses SQL, generates execution plans, serves queries
• Compute Node (CN): Executes stream processing tasks
• Meta Node: Coordinates cluster state and scheduling
• Storage Layer: Writes checkpoints to object storage (S3, MinIO) or local disk

This architecture allows independent scaling of compute and storage, making it cost-effective for workloads with varying throughput.

RisingWave Docker Compose Setup

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version: '3.8'

services:
  risingwave-standalone:
    image: ghcr.io/risingwavelabs/risingwave:latest
    ports:
      - "4566:4566"
    command: "standalone --meta-opts=\"--advertise-addr 0.0.0.0:5690\" --compute-opts=\"--advertise-addr 0.0.0.0:5688\" --frontend-opts=\"--advertise-addr 0.0.0.0:4566\" --compactor-opts=\"--advertise-addr 0.0.0.0:6660\""
    environment:
      - RUST_BACKTRACE=1
    volumes:
      - risingwave-data:/data
    deploy:
      resources:
        limits:
          memory: 4G

  kafka:
    image: apache/kafka:latest
    ports:
      - "9092:9092"
    environment:
      KAFKA_NODE_ID: 1
      KAFKA_PROCESS_ROLES: broker,controller
      KAFKA_LISTENERS: PLAINTEXT://0.0.0.0:9092,CONTROLLER://0.0.0.0:9093
      KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://kafka:9092
      KAFKA_CONTROLLER_LISTENER_NAMES: CONTROLLER
      KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: CONTROLLER:PLAINTEXT,PLAINTEXT:PLAINTEXT
      KAFKA_CONTROLLER_QUORUM_VOTERS: 1@kafka:9093
      KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1

volumes:
  risingwave-data:

Connect to RisingWave using any PostgreSQL client:

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psql -h localhost -p 4566 -d dev -U root

-- Create a source from Kafka
CREATE SOURCE clickstream (
    user_id BIGINT,
    event_type VARCHAR,
    page_url VARCHAR,
    ts TIMESTAMPTZ
) WITH (
    connector = 'kafka',
    topic = 'clickstream-events',
    properties.bootstrap.server = 'kafka:9092',
    scan.startup.mode = 'earliest'
) FORMAT PLAIN ENCODE JSON;

-- Create a materialized view
CREATE MATERIALIZED VIEW hourly_active_users AS
SELECT
    user_id,
    COUNT(*) AS event_count,
    window_start
FROM TUMBLE(clickstream, ts, INTERVAL '1 HOUR')
GROUP BY user_id, window_start;

Materialize

Materialize is a streaming database built in Rust, originally developed by Frank McSherry and the team behind differential dataflow. It pioneered the concept of a streaming database that uses incremental computation via differential dataflow, enabling highly efficient stateful stream processing with SQL.

Key characteristics:

• Built on differential dataflow for incremental computation
• PostgreSQL-compatible wire protocol
• Strong consistency guarantees with exactly-once processing semantics
• Sources for Kafka, PostgreSQL (CDC via Debezium), S3, Kinesis, and PubSub
• Commercial open-source (BSL license — converts to Apache 2.0 after 3 years)
• 6,200+ GitHub stars
• Single-binary deployment (simpler operational model than RisingWave)

Materialize Architecture

Materialize uses a single-process architecture (with optional cluster separation):

• Environmentd: The main process that manages computation and state
• Storage Layer: Local disk or cloud storage (S3-compatible)
• Kafka/Postgres Sources: Direct CDC ingestion with no intermediate processing layer

Unlike RisingWave’s distributed compute/storage split, Materialize runs as a unified process that handles ingestion, computation, and query serving. This simplifies deployment but can limit horizontal scaling for very large workloads.

Materialize Docker Compose Setup

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version: '3.8'

services:
  materialize:
    image: materialize/materialized:latest
    ports:
      - "6875:6875"
    environment:
      MZ_LOG: info
    volumes:
      - materialize-data:/data
    command: >
      materialized
      --data-dir /data
      --listen-addr 0.0.0.0:6875
    deploy:
      resources:
        limits:
          memory: 4G

  kafka:
    image: apache/kafka:latest
    ports:
      - "9092:9092"
    environment:
      KAFKA_NODE_ID: 1
      KAFKA_PROCESS_ROLES: broker,controller
      KAFKA_LISTENERS: PLAINTEXT://0.0.0.0:9092,CONTROLLER://0.0.0.0:9093
      KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://kafka:9092
      KAFKA_CONTROLLER_LISTENER_NAMES: CONTROLLER
      KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: CONTROLLER:PLAINTEXT,PLAINTEXT:PLAINTEXT
      KAFKA_CONTROLLER_QUORUM_VOTERS: 1@kafka:9093
      KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1

volumes:
  materialize-data:

Usage:

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# Connect via psql
psql -h localhost -p 6875 -U materialize

-- Create a Kafka source
CREATE SOURCE clickstream
  FROM KAFKA BROKER 'kafka:9092' TOPIC 'clickstream-events'
  FORMAT JSON;

-- Create a materialized view
CREATE MATERIALIZED VIEW user_activity_summary AS
SELECT
    (data->>'user_id')::BIGINT AS user_id,
    COUNT(*) AS total_events,
    MAX(data->>'ts')::TIMESTAMPTZ AS last_seen
FROM clickstream
GROUP BY user_id;

-- Query the live view (results update as new events arrive)
SELECT * FROM user_activity_summary ORDER BY total_events DESC LIMIT 10;

ksqlDB

ksqlDB is the streaming SQL engine built on top of Apache Kafka, developed by Confluent. It provides a SQL interface for building stream processing applications directly on Kafka topics, eliminating the need to write Java/Scala stream processing code.

Key characteristics:

• Built on Apache Kafka — leverages Kafka’s partitioning, replication, and durability
• Java-based, runs as a standalone server or embedded in Kafka Connect
• Supports streams (unbounded) and tables (bounded) abstractions
• Built-in windowing: tumbling, hopping, session, and sliding windows
• Apache 2.0 licensed (fully open-source)
• Part of the Confluent ecosystem but runs standalone
• 299+ GitHub stars (Confluent-managed repo)

ksqlDB Architecture

ksqlDB sits directly on Kafka topics:

• ksqlDB Server: Processes SQL queries, manages stream state via Kafka internal topics
• Kafka Cluster: Provides partitioning, replication, and durability
• State Stores: RocksDB-based local state for aggregation and joins, backed by Kafka changelog topics

Unlike RisingWave and Materialize, ksqlDB does not have its own storage engine — it uses Kafka as both the input source and the persistence layer for materialized views (stored as Kafka topics). This means ksqlDB benefits from Kafka’s horizontal scalability but requires a full Kafka cluster to operate.

ksqlDB Docker Compose Setup

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version: '3.8'

services:
  zookeeper:
    image: confluentinc/cp-zookeeper:7.6.1
    environment:
      ZOOKEEPER_CLIENT_PORT: 2181
      ZOOKEEPER_TICK_TIME: 2000

  kafka:
    image: confluentinc/cp-kafka:7.6.1
    depends_on:
      - zookeeper
    ports:
      - "9092:9092"
    environment:
      KAFKA_BROKER_ID: 1
      KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181
      KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: PLAINTEXT:PLAINTEXT,PLAINTEXT_HOST:PLAINTEXT
      KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://kafka:29092,PLAINTEXT_HOST://localhost:9092
      KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1
      KAFKA_TRANSACTION_STATE_LOG_MIN_ISR: 1
      KAFKA_TRANSACTION_STATE_LOG_REPLICATION_FACTOR: 1

  ksqldb-server:
    image: confluentinc/ksqldb-server:0.29.0
    depends_on:
      - kafka
    ports:
      - "8088:8088"
    environment:
      KSQL_BOOTSTRAP_SERVERS: kafka:29092
      KSQL_LISTENERS: http://0.0.0.0:8088
      KSQL_KSQL_LOGGING_PROCESSING_TOPIC_REPLICATION_FACTOR: 1
      KSQL_KSQL_INTERNAL_TOPIC_REPLICAS: 1
      KSQL_KSQL_SERVICE_ID: ksql-streams_

  ksqldb-cli:
    image: confluentinc/ksqldb-cli:0.29.0
    depends_on:
      - ksqldb-server
    entrypoint: /bin/sh
    tty: true

Usage via ksqlDB CLI:

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# Start interactive CLI
docker exec -it ksqldb-cli ksql http://ksqldb-server:8088

-- Create a stream from a Kafka topic
CREATE STREAM clickstream (
    user_id BIGINT,
    event_type VARCHAR,
    page_url VARCHAR,
    ts VARCHAR
) WITH (
    KAFKA_TOPIC = 'clickstream-events',
    VALUE_FORMAT = 'JSON',
    TIMESTAMP = 'ts'
);

-- Create a materialized view (table in ksqlDB)
CREATE TABLE user_activity AS
SELECT
    user_id,
    COUNT(*) AS total_events,
    COUNT_DISTINCT(page_url) AS unique_pages
FROM clickstream
WINDOW TUMBLING (SIZE 1 HOUR)
GROUP BY user_id
EMIT CHANGES;

-- Query with continuous push
SELECT * FROM user_activity EMIT CHANGES;

Feature Comparison

Performance and Scalability

RisingWave excels in horizontal scalability. Its compute-storage separation means you can independently scale compute nodes for heavy workloads while keeping storage on inexpensive object storage. Benchmarks show it handles millions of events per second across multiple compute nodes with low latency. The Hummock storage engine provides efficient incremental checkpointing, reducing recovery time after failures.

Materialize provides the strongest consistency guarantees through differential dataflow. Its incremental computation model means that adding one event to a stream only updates the affected parts of the view, not recomputing from scratch. This gives extremely low tail latency for complex queries. However, its single-process architecture limits horizontal scaling — for massive throughput, you may need to shard across multiple Materialize instances.

ksqlDB inherits Kafka’s scalability. Since materialized views are stored as Kafka topics, the partitioning and replication model scales naturally with the Kafka cluster. However, the Java runtime and Kafka dependency add operational overhead and resource requirements. A minimal ksqlDB deployment (Kafka + ksqlDB) typically needs 8+ GB RAM, compared to 2-4 GB for the Rust-based alternatives.

When to Choose Which

Choose RisingWave when:

• You need the most scalable open-source streaming database
• You want built-in connectors for diverse data sources (Postgres CDC, MySQL, MQTT)
• You prefer Apache 2.0 licensing without time restrictions
• You need independent compute and storage scaling
• You want a PostgreSQL-compatible interface with rich SQL support

Choose Materialize when:

• You prioritize strong consistency and exactly-once semantics
• You want the simplest deployment model (single binary)
• You need incremental computation with the lowest possible latency
• Your workload fits within a single node’s capacity
• You are building real-time feature stores or dashboards

Choose ksqlDB when:

• You already run Apache Kafka and want a SQL interface on top
• You need tight integration with Kafka Connect and Schema Registry
• You prefer the mature Confluent ecosystem
• Your team has existing Kafka operational expertise
• You need Kafka-native persistence for all materialized views

For organizations already using Kafka for event sourcing, our EventStoreDB vs Kafka vs Pulsar comparison provides additional context on choosing the right streaming backbone.

Deployment Considerations

For production deployments, consider these infrastructure requirements:

All three systems integrate well with existing observability stacks. RisingWave and Materialize both expose Prometheus-compatible metrics, making them easy to monitor alongside your existing infrastructure. For database monitoring setups, see our Pgwatch2 vs Percona PMM vs PgMonitor guide.

FAQ

What is the difference between a streaming database and a stream processor?

A stream processor (like Apache Flink or Spark Streaming) is a computation framework that processes events as they flow through a pipeline. You write code or define DAGs to transform data. A streaming database, on the other hand, lets you write standard SQL queries that continuously update as new data arrives. The streaming database handles state management, windowing, and incremental computation internally, making it easier to use but potentially less flexible than a full stream processor.

Can I use RisingWave or Materialize as a replacement for Kafka?

No. RisingWave and Materialize are consumers of streaming data — they ingest from Kafka (or other sources) and provide real-time queryable views on top of that data. They do not replace Kafka’s role as a durable, partitioned event log. ksqlDB is different: it is built directly on Kafka and requires Kafka to function. Think of streaming databases as the analytics layer on top of your event backbone, not the backbone itself.

Which streaming database has the best SQL compatibility?

Both RisingWave and Materialize use the PostgreSQL wire protocol and support a large subset of PostgreSQL SQL. This means you can connect with any PostgreSQL client (psql, DBeaver, pgAdmin) and run familiar SQL queries. ksqlDB uses a SQL-like dialect that is Kafka-specific and not directly compatible with PostgreSQL syntax — some functions and syntax differ from standard SQL.

Do I need a large cluster to run a streaming database?

Not necessarily. All three tools can run on a single machine for development and moderate workloads. Materialize runs as a single binary and can operate with 2 GB RAM. RisingWave’s standalone mode works with 4 GB. ksqlDB requires the most resources since it needs Kafka (with ZooKeeper or KRaft) plus the ksqlDB server — plan for at least 8 GB. For production use with high throughput, RisingWave’s distributed architecture scales most efficiently.

How do streaming databases handle late-arriving data?

Streaming databases use watermarks and windowing to handle out-of-order events. RisingWave supports event-time processing with configurable watermark delays and session windows that close after a period of inactivity. Materialize uses differential dataflow which naturally handles out-of-order events through its timestamp-based computation model. ksqlDB supports event-time and processing-time windows with configurable grace periods for late data. The exact behavior depends on your window configuration and source settings.

Is it possible to export materialized view results to a traditional database?

Yes. RisingWave supports sinks to PostgreSQL, MySQL, Kafka, Redis, and S3, allowing you to push materialized view results to downstream systems. Materialize can export data to S3 or Kafka. ksqlDB materialized views are stored as Kafka topics, which can be consumed by any downstream system or written to a database via Kafka Connect sinks. This pattern is common for powering real-time dashboards or feeding traditional reporting systems.