Streaming SQL Showdown: RisingWave vs Materialize vs ksqlDB

Your team has decided that batch processing is too slow. Dashboards are stale, anomaly alerts arrive minutes after the damage is done, and your users expect data that reflects the last few seconds, not the last few hours. You need a streaming SQL engine.

Three names dominate the conversation in 2026: RisingWave, Materialize, and ksqlDB. All three let you write SQL over continuously changing data. All three support materialized views that update incrementally. But the similarities end there. Under the hood, these systems differ dramatically in architecture, connector ecosystem, state management, deployment flexibility, and total cost of ownership.

This article is a three-way comparison designed to help data engineers and architects pick the right tool. We cover six dimensions in depth, present a comprehensive feature table, and answer the most common questions practitioners ask when evaluating these platforms.

Architecture: Three Different Foundations

Architecture determines everything downstream: how a system scales, how it recovers from failures, how much it costs to operate, and what workloads it handles well. Each of these three engines builds on a fundamentally different foundation.

RisingWave: Cloud-Native Disaggregated Architecture

RisingWave is a distributed streaming database built from the ground up with a cloud-native, disaggregated storage and compute architecture. Four specialized node types handle distinct responsibilities:

• Serving nodes parse SQL, optimize queries, and serve ad-hoc reads with PostgreSQL wire protocol compatibility.
• Streaming nodes execute the continuous dataflow graph, maintaining incremental materialized view state.
• Meta nodes coordinate the cluster, manage metadata, orchestrate checkpointing, and handle job lifecycle.
• Compactor nodes run background compaction on the LSM-tree storage engine (Hummock), optimizing read performance without blocking the streaming pipeline.

All persistent state lives in cloud object storage (S3, GCS, or Azure Blob Storage). Compute nodes are stateless with respect to durable state. This means you can scale compute and storage independently, and failed nodes can be replaced without data loss. RisingWave's Hummock engine was designed specifically for streaming workloads, buffering writes in memory before flushing immutable SSTables to object storage.

Materialize: Timely Dataflow and Differential Dataflow

Materialize is built on Timely Dataflow and Differential Dataflow, research frameworks from Microsoft Research. The core principle behind Differential Dataflow is representing data as collections of diffs rather than full relations. This enables efficient incremental computation where only the changes are propagated through the dataflow graph.

Materialize's architecture splits into three logical layers:

• Storage (Persist) handles data ingestion and durability, backed by blob storage and a consensus service.
• Adapter manages the PostgreSQL-compatible SQL interface, query parsing, and planning.
• Compute runs Timely Dataflow operators that process data incrementally across workers.

Compute nodes hold active state in memory during processing. Materialize uses a Persist layer for durability, but the working set needs to fit in memory for the compute replicas. Scaling happens by resizing cluster replicas or adding more of them.

ksqlDB: Kafka Streams Under the Hood

ksqlDB takes a fundamentally different approach. Rather than building a standalone database, it layers a SQL interface on top of Apache Kafka Streams. ksqlDB is a JVM application that exposes a REST API and translates SQL statements into Kafka Streams topologies.

The architecture is relatively simple:

• ksqlDB server is a JVM process running Kafka Streams internally.
• State is maintained in local RocksDB instances on each server node.
• Kafka serves as the source of truth, the message transport layer, and the mechanism for inter-operator data exchange.

This tight coupling to Kafka means ksqlDB inherits Kafka's strengths (high throughput, durable log) and its constraints (every source and sink must flow through Kafka topics). There is no independent storage layer; if you need data from PostgreSQL or MySQL, it must first land in a Kafka topic via Kafka Connect before ksqlDB can process it.

Architecture Summary

SQL Dialect and PostgreSQL Compatibility

For teams adopting streaming SQL, the learning curve matters. A familiar SQL dialect means faster onboarding, better tooling support, and fewer surprises when writing complex queries.

RisingWave: Full PostgreSQL Compatibility

RisingWave speaks PostgreSQL, both the SQL dialect and the wire protocol. You can connect with psql, DBeaver, Metabase, Grafana, Superset, or any tool that supports PostgreSQL. Standard SQL constructs like JOIN, GROUP BY, WINDOW functions, CTEs, and subqueries all work as expected.

RisingWave extends standard SQL with streaming primitives: CREATE SOURCE for ingesting external data, CREATE SINK for exporting results, and CREATE MATERIALIZED VIEW for defining incrementally maintained views. But these extensions feel natural because they follow PostgreSQL syntax patterns.

-- RisingWave: Standard PostgreSQL-compatible SQL
CREATE MATERIALIZED VIEW order_stats AS
SELECT
    vendor_id,
    COUNT(*) AS total_orders,
    SUM(amount) AS total_revenue,
    AVG(amount) AS avg_order_value
FROM orders
WHERE status = 'completed'
GROUP BY vendor_id;

Materialize: PostgreSQL-Compatible with Extensions

Materialize also implements the PostgreSQL wire protocol and a large subset of the PostgreSQL SQL dialect. You can use the same BI tools and client libraries. The SQL experience is familiar, with CREATE SOURCE, CREATE SINK, and CREATE MATERIALIZED VIEW as the main streaming extensions.

One distinctive feature is SUBSCRIBE, a continuously running query (similar to PostgreSQL's LISTEN/NOTIFY) that streams row-level changes to clients in real time.

-- Materialize: PostgreSQL-compatible SQL
CREATE MATERIALIZED VIEW order_stats AS
SELECT
    vendor_id,
    COUNT(*) AS total_orders,
    SUM(amount) AS total_revenue,
    AVG(amount) AS avg_order_value
FROM orders
WHERE status = 'completed'
GROUP BY vendor_id;

ksqlDB: Custom SQL Dialect

ksqlDB uses its own SQL dialect that is not PostgreSQL-compatible. While it looks like SQL, there are significant differences. ksqlDB introduces two core abstractions, STREAM and TABLE, rather than PostgreSQL's standard table model. Queries use custom syntax for windowing, key handling, and time semantics.

You cannot connect to ksqlDB with psql or standard PostgreSQL tools. You must use the ksqlDB CLI, the REST API, or ksqlDB-specific client libraries.

-- ksqlDB: Custom SQL dialect
CREATE TABLE order_stats AS
SELECT
    vendor_id,
    COUNT(*) AS total_orders,
    SUM(amount) AS total_revenue
FROM orders_stream
WHERE status = 'completed'
GROUP BY vendor_id
EMIT CHANGES;

Note the EMIT CHANGES clause and the requirement to create from a stream rather than a table. ksqlDB also lacks support for complex multi-way joins, certain aggregate functions, and ad-hoc analytical queries that PostgreSQL-compatible systems handle natively.

SQL Dialect Comparison

Source and Sink Support

A streaming SQL engine is only as useful as the data it can ingest and the systems it can feed. Connector breadth determines how well the engine fits into your existing data infrastructure.

RisingWave: Broad, Kafka-Independent Ecosystem

RisingWave does not depend on Kafka. It can ingest data directly from a wide range of sources without an intermediary message broker:

Sources: Kafka, Redpanda, Apache Pulsar, Amazon Kinesis, PostgreSQL CDC, MySQL CDC, Google Pub/Sub, Amazon S3, NATS, and more.

Sinks: Kafka, Apache Iceberg, Delta Lake, PostgreSQL, ClickHouse, Snowflake, Elasticsearch, Amazon S3, Google Cloud Storage, Azure Blob Storage, NATS, MQTT, and more.

The ability to ingest CDC data directly from PostgreSQL or MySQL without routing through Kafka is a significant architectural simplifier. For teams that do not run Kafka, RisingWave removes the need to introduce it just to use streaming SQL.

RisingWave also treats Apache Iceberg as a first-class citizen, hosting an Iceberg REST catalog and supporting streaming writes with exactly-once delivery.

Materialize: PostgreSQL, Kafka, and Webhooks

Materialize supports a focused set of sources and sinks:

Sources: PostgreSQL, MySQL, SQL Server, CockroachDB, MongoDB (via CDC), Kafka, Redpanda, Webhooks, Amazon EventBridge, and Segment.

Sinks: Kafka/Redpanda is the primary sink destination.

Materialize's source support is solid for CDC use cases, with native replication from multiple databases. The webhook source is a useful addition for ingesting data from SaaS platforms. However, the sink side is notably narrow: Kafka is essentially the only supported sink, which means you need additional infrastructure to route processed data to downstream systems like data warehouses or object storage.

ksqlDB: Kafka-Only by Design

ksqlDB is designed exclusively for the Kafka ecosystem:

Sources: Kafka topics (with Kafka Connect for external integrations).

Sinks: Kafka topics (with Kafka Connect for external delivery).

All data must flow through Kafka. If your source is a PostgreSQL database, you need Kafka Connect with a Debezium connector to produce CDC events to a Kafka topic, and then ksqlDB can read from that topic. Similarly, processed results go to Kafka topics, and you need additional Kafka Connect sink connectors to push data to downstream systems.

This is not a limitation if your entire stack is already Kafka-centric. But for teams running diverse infrastructure, the mandatory Kafka dependency adds operational complexity and cost.

Source and Sink Comparison

State Management and Fault Tolerance

Streaming systems are stateful by nature. Materialized views, aggregations, joins, and windows all require the engine to maintain state across events. How each system manages, persists, and recovers that state directly impacts reliability, recovery time, and operational cost.

RisingWave: Checkpoint-Based Recovery with Object Storage

RisingWave uses a checkpoint-based approach where state is periodically snapshotted to object storage via the Hummock engine. The process works as follows:

1. Streaming nodes maintain state in memory and local buffers during processing.
2. At configurable intervals, the meta node coordinates a consistent, distributed checkpoint.
3. Checkpoint data is flushed as immutable SSTables to object storage.
4. On failure, nodes restore from the latest checkpoint and replay input from the checkpoint position.

Because all durable state lives in object storage, recovery does not depend on local disk. A failed node can be replaced with any available compute instance, which fetches its state from S3/GCS. This makes RisingWave well-suited for environments where nodes are ephemeral, such as Kubernetes or spot instances.

Incremental checkpointing reduces overhead: only the state that changed since the last checkpoint is written, keeping checkpoint sizes manageable even for large state.

Materialize: Persist Layer with Active Replication

Materialize durably stores ingested data and operator state in its Persist layer, which uses blob storage and a consensus service. Active replication provides high availability: if a replica fails, another replica can serve reads without downtime.

However, compute replicas hold the active working set in memory. If a compute replica fails, it must rehydrate its in-memory state from the Persist layer, which can take time proportional to the state size. For workloads with large state (multi-way joins across high-cardinality streams), rehydration time can be significant.

ksqlDB: Local State with Kafka-Based Recovery

ksqlDB stores operator state in local RocksDB instances. On failure, state is rebuilt by replaying the underlying Kafka topics from the beginning (or from the last committed offset, depending on configuration).

Recovery time depends on the volume of data in the Kafka topics. For large state, this replay can take a long time. ksqlDB also supports standby replicas that maintain a warm copy of the state store, reducing recovery time at the cost of additional resources.

State management in ksqlDB has known operational challenges. TTL (time-to-live) management for state stores is limited, and state store maintenance for complex aggregations can be difficult to tune.

State Management Comparison

Deployment Options

Where and how you can run a system influences everything from compliance posture to operational overhead.

RisingWave: Maximum Flexibility

RisingWave offers the widest range of deployment options:

• Open-source self-hosted (Apache 2.0 license): Run on your own infrastructure, any cloud, on-premises, or even a laptop for development.
• Kubernetes: Helm charts and a Kubernetes operator are available for production self-managed deployments.
• RisingWave Cloud: A fully managed service with auto-scaling, monitoring, and maintenance handled by the RisingWave team.

The Apache 2.0 license means no restrictions on how you use the software. You can embed it, modify it, run it as part of an internal platform, or build a product on top of it.

Materialize: Cloud-First with Self-Managed Option

Materialize offers two deployment models:

• Materialize Cloud: The primary offering, a fully managed service.
• Self-Managed: Available under a free Community License (capped at 24 GiB memory, 48 GiB disk) or a paid Enterprise License for larger deployments. Requires Kubernetes, a PostgreSQL metadata store, and object storage (S3 or MinIO).

The source code is licensed under the Business Source License (BSL 1.1), which converts to Apache 2.0 after four years. Under BSL, you can view and modify the source code, but you cannot offer Materialize as a competing hosted service.

ksqlDB: Confluent Ecosystem

ksqlDB deployment is tied to the Confluent ecosystem:

• Confluent Cloud: Fully managed ksqlDB as part of the Confluent Cloud platform.
• Confluent Platform (self-managed): Run ksqlDB on your own infrastructure alongside Confluent's Kafka distribution.
• Community (source-available): The ksqlDB source code is available under the Confluent Community License, which is not OSI-approved open source. You can run it internally but cannot offer it as a competing SaaS.

In all cases, ksqlDB requires a running Kafka cluster, which adds to the deployment footprint and operational overhead.

Deployment Comparison

Pricing and Total Cost of Ownership

Cost comparisons for streaming systems are notoriously tricky because the pricing models differ significantly. Here is a breakdown of what each option costs in practice.

RisingWave

• Self-hosted: Free (Apache 2.0). You pay only for infrastructure.
• RisingWave Cloud: Starts at approximately $0.14/hour for a small configuration. Pay-as-you-go with usage-based pricing. No upfront commitments required.

Because RisingWave stores state in object storage, storage costs scale with S3/GCS pricing (typically $0.02-0.03/GB/month), which is dramatically cheaper than keeping state in memory or on attached SSDs.

Materialize

• Self-Managed Community: Free within limits (24 GiB memory, 48 GiB disk).
• Self-Managed Enterprise: Annual commitment priced on memory and disk.
• Materialize Cloud On-Demand: Pay-as-you-go for compute, storage, and networking.
• Materialize Cloud Capacity: Annual prepaid plan with volume discounts.

Materialize follows a credit-based pricing model similar to Snowflake. Costs can scale quickly for memory-intensive workloads because compute replicas hold state in memory.

ksqlDB (Confluent Cloud)

• Confluent Cloud: Billed per Confluent Streaming Unit (CSU) per hour, with a minimum of 4 CSUs. CSU pricing varies by region and cloud provider.
• Total cost: Must include the underlying Kafka cluster cost (ingress, egress, storage, partitions), which is often the larger portion of the bill.
• Self-managed: License cost for Confluent Platform, plus Kafka infrastructure.

A small Confluent Cloud deployment typically starts at $1,000-3,000/month for basic workloads. Enterprise deployments with high throughput can reach $50,000-200,000+/month.

Pricing Comparison

Comprehensive Comparison Table

When Should You Use RisingWave Over Materialize or ksqlDB?

RisingWave is the strongest choice when you need a combination of deployment flexibility, broad connector support, and cost-efficient state management. Specific scenarios where RisingWave excels:

• You want true open source. Apache 2.0 means no license restrictions. Run it anywhere, modify it freely, embed it in your product.
• Your state is large. Because RisingWave stores state in object storage rather than memory, it handles large-state workloads (high-cardinality joins, long time windows) without expensive memory scaling.
• You do not run Kafka. RisingWave ingests directly from PostgreSQL CDC, MySQL CDC, Pulsar, Kinesis, and other sources without requiring a Kafka cluster.
• You need diverse sinks. Delivering results to Iceberg, Snowflake, ClickHouse, or S3 is built in, not bolted on through Kafka Connect.
• You run on Kubernetes or ephemeral infrastructure. Stateless compute nodes and object storage state make RisingWave naturally cloud-native.

When Should You Choose Materialize?

Materialize is a strong option in these scenarios:

• Your workload is memory-bounded and latency-critical. Materialize's in-memory compute model delivers very low latency for workloads where the state fits comfortably in memory.
• You need SUBSCRIBE for change streams. Materialize's SUBSCRIBE command is a powerful primitive for applications that need to react to every change in a materialized view.
• You prefer a fully managed experience and do not need self-hosted flexibility. Materialize Cloud is polished and reduces operational overhead.
• Your sink requirements are limited to Kafka. If your downstream systems already consume from Kafka, the narrow sink support is not a limitation.

When Does ksqlDB Make Sense?

ksqlDB is the right fit in a narrow but important set of situations:

• You are fully invested in the Confluent/Kafka ecosystem. If your team already runs Confluent Cloud or Confluent Platform and your data already lives in Kafka topics, ksqlDB adds a SQL layer without introducing new infrastructure.
• Your processing needs are simple. Straightforward aggregations, filters, and transformations over Kafka streams are ksqlDB's sweet spot.
• You want the simplest possible SQL layer over Kafka. For teams that do not need complex joins, ad-hoc queries, or diverse sinks, ksqlDB's focused scope can be an advantage.

How Does State Storage Affect Streaming SQL Performance and Cost?

State storage strategy is one of the most consequential architectural decisions in a streaming engine. It affects performance, cost, and operational complexity in ways that are not always obvious during initial evaluation.

Object storage (RisingWave): State in S3 or GCS costs roughly $0.02/GB/month. A workload with 500 GB of state costs about $10/month for storage. Compute nodes are stateless, so you can scale them independently and use spot instances.

In-memory (Materialize): The same 500 GB of state requires 500 GB of RAM across compute replicas. At typical cloud memory pricing ($5-10/GB/month for reserved instances), that is $2,500-5,000/month just for memory, not counting compute.

Local RocksDB + Kafka (ksqlDB): State lives on local SSDs (approximately $0.10/GB/month) plus the underlying Kafka cluster stores the same data in topic logs. The total storage cost falls between the other two, but you also pay for Kafka cluster resources.

For workloads with modest state (under 10 GB), the cost differences are negligible. For large-state workloads, the architecture choice can mean 10-100x differences in storage cost alone.

What Are the Key Differences in SQL Support Between These Three Engines?

The most practical difference is PostgreSQL compatibility. RisingWave and Materialize both support the PostgreSQL wire protocol, which means you can use standard database tools, BI platforms, and client libraries without modification. ksqlDB requires its own CLI, REST API, or dedicated client libraries.

Beyond tooling compatibility, SQL expressiveness varies. RisingWave and Materialize support full PostgreSQL window functions, CTEs, subqueries, and complex multi-way joins. ksqlDB supports a subset: TUMBLING, HOPPING, and SESSION windows, basic aggregations, and limited join types. If your streaming queries are complex, ksqlDB's SQL dialect may force you into workarounds or architectural compromises.

Ad-hoc query support is another key differentiator. Both RisingWave and Materialize can serve arbitrary SELECT queries against materialized views with full SQL expressiveness. ksqlDB's pull queries are limited to key-based lookups and simple filters, making it unsuitable as a general-purpose query layer.

Can I Migrate from ksqlDB to RisingWave or Materialize?

Yes, but the migration path depends on what you are migrating from. If your ksqlDB deployment reads from Kafka topics, both RisingWave and Materialize can connect to the same Kafka topics as sources. You can run the new system in parallel, validate output, and switch over gradually.

The main migration work involves rewriting ksqlDB's custom SQL dialect into PostgreSQL-compatible SQL. Most ksqlDB STREAM and TABLE definitions map to CREATE SOURCE and CREATE MATERIALIZED VIEW in RisingWave or Materialize. Window functions and aggregations need syntax adjustments but are conceptually similar.

For teams moving to RisingWave specifically, the migration also opens the door to removing the Kafka dependency entirely if your original data sources support direct ingestion (PostgreSQL CDC, MySQL CDC, Pulsar, Kinesis).

Conclusion

Choosing between RisingWave, Materialize, and ksqlDB comes down to your infrastructure requirements, workload characteristics, and operational preferences:

• RisingWave offers the broadest combination of features: true open source (Apache 2.0), PostgreSQL compatibility, the widest connector ecosystem, cost-efficient object storage state, and deployment flexibility from a single binary to a fully managed cloud. It is the strongest general-purpose choice for teams building new streaming SQL infrastructure.
• Materialize excels in low-latency, memory-bounded workloads with its Timely Dataflow foundation and polished managed service. It is a strong option for teams that prioritize latency above all else and whose state fits in memory.
• ksqlDB is the right tool when your entire data platform is already Kafka-centric and your processing requirements are straightforward. It adds SQL convenience over Kafka with minimal additional infrastructure, but its custom dialect, limited sinks, and Kafka dependency make it the most constrained of the three.

For most teams evaluating streaming SQL in 2026, RisingWave's combination of open-source freedom, architectural efficiency, and connector breadth makes it the most versatile starting point.

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