Kafka Streams vs RisingWave in 2026: When You Need a Database

What Kafka Streams Is (and Is Not)

Kafka Streams is a client library, not a service. That distinction matters more than almost anything else in this comparison.

When you use Kafka Streams, you write Java or Kotlin code that imports the Kafka Streams library, defines processing topology using the Streams DSL (KStream, KTable, KGroupedStream, and related classes), and runs inside your application process. There is no separate cluster, no standalone deployment, no Flink JobManager to manage. Your Java application is the stream processor.

This design gives Kafka Streams genuine advantages. Because it is a library, deployment is as simple as deploying your application. Scaling means running more instances of your application. It integrates naturally with Java dependency injection frameworks, logging systems, and metrics pipelines. If you are a Java shop already operating Kafka, adding Kafka Streams is a smaller operational step than adopting a new service.

Kafka Streams is also genuinely capable. It supports exactly-once processing semantics (a hard guarantee to build correctly from scratch), stateful operations backed by local RocksDB state stores, joins between KStreams and KTables, windowed aggregations, and session windows. For a Java team processing Kafka events with complex business logic, it is a mature and well-tested choice.

The limitations are real, though:

Kafka is a hard dependency. Kafka Streams only works with Kafka. Every source and every sink in a Kafka Streams topology is a Kafka topic. If you want to join against a database table or ingest from a CDC source, you must first get that data into Kafka.

No SQL interface. Every transformation is defined in Java code. A business analyst who wants to understand or modify a streaming pipeline needs to read Java. A data engineer who joins your team from a SQL background faces a steeper onboarding path.

Serving results requires additional infrastructure. When Kafka Streams computes an aggregation, the result goes back to a Kafka topic or sits in a local state store. To serve those results to an application, a dashboard, or an API, you need either to build an Interactive Queries HTTP layer inside your application, publish results to Kafka and consume from there, or sink results to an external database. None of these are automatic.

Changing logic requires a code change and redeployment. If a product manager asks you to change the time window on a streaming aggregation from one hour to thirty minutes, that is a code change, a build, and a deployment.

What RisingWave Is

RisingWave is a streaming database. Not a stream processing framework, not a library. A database, with all that implies: it has a server process, a SQL interface, persistent storage, and a serving layer built in.

The core design decision is that streaming logic is expressed in SQL. You create sources (Kafka topics, CDC streams), define materialized views using standard SQL (extended with streaming-specific constructs), and query those views using the PostgreSQL wire protocol. Any PostgreSQL-compatible client connects to RisingWave on port 4566 and runs queries against live, continuously updated views.

RisingWave is open source under Apache 2.0 and supports managed deployment through RisingWave Cloud.

The most important thing to understand about RisingWave materialized views is that they implement incremental view maintenance. When a new event arrives, RisingWave does not recompute the entire view. It computes only the delta necessary to update the view's result. This means materialized views stay current with sub-second latency without requiring expensive full recomputations.

-- Define a Kafka source
CREATE SOURCE orders (
    order_id BIGINT,
    user_id VARCHAR,
    amount DECIMAL,
    event_time TIMESTAMPTZ
) WITH (
    connector = 'kafka',
    topic = 'orders',
    properties.bootstrap.server = 'kafka:9092'
) FORMAT PLAIN ENCODE JSON;

-- Define a continuously updated materialized view
CREATE MATERIALIZED VIEW orders_per_user_hourly AS
SELECT
    user_id,
    COUNT(*) AS order_count,
    SUM(amount) AS total_amount,
    window_start,
    window_end
FROM TUMBLE(orders, event_time, INTERVAL '1' HOUR)
GROUP BY user_id, window_start, window_end;

-- Query it like a regular table -- results are always fresh
SELECT * FROM orders_per_user_hourly
WHERE user_id = '123'
ORDER BY window_start DESC
LIMIT 10;

Why 2026 Is Different

Three changes in the industry make this comparison more urgent in 2026 than it was in 2022.

SQL became the default language for data work. The overwhelming majority of data teams in 2026 use SQL as their primary language. Data analysts, analytics engineers, and data scientists working in modern data stacks default to SQL. A streaming processing layer that requires Java code creates a hard wall between the data team and the streaming logic, with engineering involvement required for every change.

AI applications need queryable, fresh state. AI agents and ML inference services need to read structured state with low latency. They issue SQL queries, not Kafka consumer poll loops. A streaming pipeline that produces results only into Kafka topics cannot serve an AI agent directly. A streaming database with a PostgreSQL interface can.

The operational complexity of distributed systems became better understood. Teams that built Kafka Streams topologies at scale in 2021 and 2022 accumulated operational experience: state store rebalancing during deployments, standby replicas, consumer group lag management, and the challenge of replaying state after a bug fix. These are solvable problems, but they require expertise. In 2026, many teams are re-evaluating whether that operational complexity is justified for use cases that SQL could address.

When Kafka Streams Is the Right Choice

Kafka Streams is genuinely excellent in specific contexts. It is not the right choice for every stream processing problem, but for the problems it fits, it is hard to beat.

You have a Java microservices architecture. If your engineering team writes Java, your services are Java-based, and you are already running Kafka, Kafka Streams fits naturally into your existing operational model. You do not need a new cluster, new deployment pipelines, or new expertise.

Your processing logic involves complex Java business rules. Some processing logic is difficult to express in SQL but straightforward in Java. Custom deserialization of proprietary binary formats, complex fraud detection rules that depend on external API calls, or event enrichment that requires calling internal services are all examples where Java code in a Kafka Streams application is cleaner than a SQL workaround.

You need exactly-once guarantees in a pure Kafka pipeline. Kafka Streams' exactly-once semantics are well-tested and deeply integrated with the Kafka broker. If your entire pipeline is Kafka-to-Kafka and correctness guarantees are critical, Kafka Streams' EOS is a strong option.

Your streaming logic changes rarely. If the processing topology is defined once during a system design phase and rarely changes, the cost of Java code changes and redeployments is manageable. The benefit of SQL flexibility is less relevant when you are not changing logic frequently.

When RisingWave Is the Better Choice

RisingWave has a sharper value proposition in a different set of scenarios.

Your team is SQL-native. If your data engineers, analytics engineers, and data analysts work primarily in SQL, RisingWave lets them own and modify streaming logic directly. There is no translation step where a data analyst describes a new metric to a Java engineer who implements it in Kafka Streams. The analyst writes the SQL.

You need to query streaming results directly. If the output of your streaming computation needs to be queried by applications, dashboards, or AI agents, RisingWave eliminates the need for an additional serving layer. You do not need to sink results to Redis, Elasticsearch, or a relational database for serving. The materialized view is the serving layer.

You are processing CDC streams. RisingWave has native CDC connectors for PostgreSQL, MySQL, MongoDB, and SQL Server. You can point RisingWave at a running operational database and begin processing change events immediately. With Kafka Streams, you would need to set up Debezium, publish CDC events to Kafka topics, and then consume from those topics.

You are joining streams against dimension tables. RisingWave supports JOIN between live streams and lookup tables (including tables backed by CDC streams) in a single materialized view. The join results update incrementally as either side changes.

Your processing logic changes frequently. Modifying a RisingWave materialized view is a SQL DDL statement. No code, no build, no deployment. If a business stakeholder asks to change a window size or add a new filter condition, a data engineer can make the change in a SQL console.

Feature Comparison Table

Architecture Patterns

Traditional Kafka Streams Architecture

In a classic Kafka Streams deployment, the processing logic lives inside your Java application. Results go back to Kafka or to an external store.

Kafka topics
    |
    v
Java Application (with Kafka Streams library)
    - Reads from input Kafka topics
    - Applies KStream / KTable transformations
    - Writes results to output Kafka topics
    |
    v
Output Kafka topics
    |
    v
Consumer applications / sinks (to Redis, PostgreSQL, etc. for serving)

Every layer in this chain is your responsibility. The consumer application that reads output topics and serves the data to an API must be built and operated by your team.

RisingWave Architecture

With RisingWave, processing and serving are unified in a single system.

Kafka topics / CDC sources
    |
    v
RisingWave (streaming database)
    - Kafka sources, CDC sources
    - Materialized views (SQL-defined, continuously updated)
    - PostgreSQL wire protocol for queries
    |
    v
Applications / Dashboards / AI Agents
    - Query via PostgreSQL protocol
    - Results are always fresh (sub-second latency)

Applications query RisingWave directly using the PostgreSQL protocol. There is no downstream consumer to build.

Code Comparison: Orders Per User in the Last Hour

This is the same computation implemented in both systems.

Kafka Streams (Java):

import org.apache.kafka.streams.*;
import org.apache.kafka.streams.kstream.*;
import java.time.Duration;

StreamsBuilder builder = new StreamsBuilder();

KStream<String, Order> orders = builder.stream(
    "orders",
    Consumed.with(Serdes.String(), orderSerde)
);

KTable<Windowed<String>, Long> orderCounts = orders
    .groupByKey()
    .windowedBy(TimeWindows.ofSizeWithNoGrace(Duration.ofHours(1)))
    .count(Materialized.as("order-counts-store"));

// Results must be published back to Kafka
orderCounts
    .toStream()
    .map((windowedKey, count) -> new KeyValue<>(
        windowedKey.key(),
        new OrderCount(windowedKey.key(), count, windowedKey.window().start())
    ))
    .to("order-counts-per-user", Produced.with(Serdes.String(), orderCountSerde));

// To query the count for a specific user, you need an Interactive Queries endpoint:
// ReadOnlyWindowStore<String, Long> store = streams.store(
//     StoreQueryParameters.fromNameAndType("order-counts-store",
//         QueryableStoreTypes.windowStore())
// );
// Results are not accessible via SQL -- you build the HTTP layer yourself.

RisingWave (SQL):

CREATE MATERIALIZED VIEW orders_per_user_hourly AS
SELECT
    user_id,
    COUNT(*) AS order_count,
    window_start,
    window_end
FROM TUMBLE(orders, event_time, INTERVAL '1' HOUR)
GROUP BY user_id, window_start, window_end;

-- Immediately queryable from any PostgreSQL-compatible client
SELECT * FROM orders_per_user_hourly
WHERE user_id = '123';

The Kafka Streams version requires you to: write Java code, handle serialization and deserialization, manage state stores, publish results to a Kafka topic, and build a serving layer. To change the window from one hour to thirty minutes, you modify the Java code, rebuild, and redeploy.

The RisingWave version is a single SQL statement. To change the window, you drop the materialized view and recreate it.

Both are correct approaches. The choice depends on whether your team wants to express this logic in Java or SQL, and whether you need a built-in serving layer.

Can They Work Together?

Yes. Kafka Streams and RisingWave are not mutually exclusive.

A common pattern is to use Kafka Streams for the parts of your pipeline that genuinely benefit from Java code: complex deserialization, event enrichment that calls external Java services, or business logic that is easier to unit test in Java. The output of that Kafka Streams application goes to a Kafka topic. RisingWave reads that topic and handles SQL-based analytics, aggregations, and serving.

Raw events (Kafka)
    |
    v
Kafka Streams (Java microservice)
    - Complex deserialization
    - External API enrichment
    - Business-rule event validation
    |
    v
Enriched events (Kafka)
    |
    v
RisingWave
    - SQL-based aggregations and joins
    - Materialized views queryable by SQL clients
    - Sinks to Iceberg, PostgreSQL, ClickHouse
    |
    v
Applications, dashboards, AI agents (PostgreSQL protocol)

This architecture uses each tool for what it does best. Kafka Streams handles Java business logic. RisingWave handles SQL analytics and serving.

Operational Considerations

Kafka Streams does not require a separate cluster, which is a genuine operational advantage. Scaling means adding application instances, and Kafka handles partition rebalancing. The downside: state is stored locally in RocksDB, so scaling operations involve state migration. Debugging a Kafka Streams application requires Java debugging skills. Adding new streaming logic requires a code review, merge, and deployment cycle.

RisingWave is a separate service to deploy and operate. RisingWave Cloud removes the operational burden for teams that prefer managed infrastructure. Because streaming logic is SQL, changes can be made without application deployments. RisingWave stores state on S3-compatible object storage, which decouples storage from compute and simplifies scaling.

Summary

Kafka Streams and RisingWave solve the same category of problem -- stateful stream processing -- but with fundamentally different assumptions about who writes the logic and what happens with the results.

Kafka Streams is the right tool when you have a Java microservices architecture, processing logic that benefits from Java code, and you are happy to build your own serving layer. It is mature, well-tested, and operationally simple if you are already running Java applications and Kafka.

RisingWave is the right tool when SQL is your primary language, you need to query streaming results directly, your processing logic changes frequently, or you want native CDC support without a Kafka dependency. It eliminates the gap between stream processing and data serving that Kafka Streams leaves open.

In 2026, with data teams becoming increasingly SQL-native and AI applications requiring fresh, queryable state, the trend is toward databases that unify stream processing and serving. For teams evaluating stream processing today, RisingWave often provides a faster path from raw events to queryable results. For teams already running Kafka Streams in a Java ecosystem that works well, the cost of migration needs to be weighed against the benefits.

The two are also complementary. Kafka Streams for complex Java event processing and RisingWave for SQL analytics and serving is an architecture that uses each tool where it has the clearest advantage.