Question 1

Is RisingWave a replacement for Apache Kafka?

Accepted Answer

RisingWave does not replace Kafka as a message broker. However, RisingWave eliminates the need for a separate stream processing framework (like Flink or Spark Streaming) and a separate serving database on top of Kafka. RisingWave ingests directly from Kafka, processes data with SQL, and serves query results — all in one system.

Question 2

Can RisingWave ingest data from Kafka?

Accepted Answer

Yes. RisingWave natively integrates with Apache Kafka, Confluent Cloud, Amazon MSK, and other Kafka-compatible systems. It consumes from Kafka topics as a source and can also produce to Kafka topics as a sink, with exactly-once semantics.

Question 3

Do I still need Kafka if I use RisingWave?

Accepted Answer

It depends on your use case. If you need a durable message bus for decoupling producers and consumers across many services, Kafka remains valuable. But if you're using Kafka primarily for stream processing and real-time analytics, RisingWave can ingest from upstream sources directly via CDC and its Events API, potentially eliminating the need for Kafka.

Question 4

How does RisingWave compare to Kafka Streams?

Accepted Answer

Kafka Streams is a Java client library embedded in your application, while RisingWave is a standalone distributed database. RisingWave uses SQL instead of Java, scales independently from your application, persists state in S3 instead of Kafka changelogs, and serves queries directly without needing a separate datastore.

Question 5

How does RisingWave compare to Confluent's ksqlDB?

Accepted Answer

Both use SQL for stream processing, but RisingWave is PostgreSQL-compatible, open source under Apache 2.0, and runs independently of Kafka. ksqlDB is tied to the Confluent ecosystem, uses a Confluent Community License, and depends on Kafka for state management. RisingWave supports 50+ connectors beyond Kafka.

Question 6

Is RisingWave more cost-effective than Kafka-based architectures?

Accepted Answer

Yes. A typical Kafka-based streaming architecture requires Kafka brokers, a stream processor (e.g., Flink), and a serving database — each with separate infrastructure costs. RisingWave consolidates the processing and serving layers into one system with S3-based storage, delivering up to 10x cost reduction compared to multi-component architectures.

Question 7

Does RisingWave support exactly-once processing with Kafka?

Accepted Answer

Yes. RisingWave provides exactly-once semantics when consuming from and producing to Kafka. Every event is processed once and only once, even in the event of system failures, through its checkpoint-based recovery mechanism.

Question 8

Can RisingWave handle the same throughput as Kafka?

Accepted Answer

RisingWave is designed for high-throughput stream processing, ingesting millions of events per second. While Kafka excels at raw message throughput and durability as a log, RisingWave is optimized for processing that data — joins, aggregations, windowing, and materialized views — with sub-100ms latency.

	RisingWave	Apache Kafka
Primary purpose	Stream processing + data serving	Message transport + event log
Processing model	Built-in SQL stream processing with materialized views	Requires external processor (Flink, Spark, Kafka Streams)
Query interface	PostgreSQL-compatible SQL	No query interface (ksqlDB available separately via Confluent)
Query serving	Built-in — query materialized views directly with SQL	Not supported — requires a separate serving database
State management	Automatic, persisted in S3-compatible object storage	Kafka Streams: changelogs in Kafka topics; Flink: RocksDB + checkpoints
Exactly-once semantics	Built-in, end-to-end	Supported for producers/consumers; stream processing depends on framework
Data connectors	50+ native sources and sinks (including Kafka, CDC, Iceberg, Snowflake)	Kafka Connect ecosystem (100+ connectors, requires separate infrastructure)
Apache Iceberg	Native integration — ingest, transform, and deliver to Iceberg tables	Requires Kafka Connect Iceberg sink connector
Programming language	SQL + UDFs (Python, Java, JavaScript, Rust)	Java/Scala (Kafka Streams), SQL (ksqlDB), various (Kafka Connect)
Scaling	Dynamic scaling in under 10 seconds, decoupled compute-storage	Partition-based scaling; rebalancing can take minutes to hours
Failure recovery	Seconds (state in S3, no rebuild needed)	Broker recovery depends on replication; stream processor recovery varies
Operational complexity	Single system to deploy and manage	Multiple systems: brokers + ZooKeeper/KRaft + processors + serving DB
License	Apache License 2.0	Apache License 2.0 (Confluent Platform has proprietary components)
Best for	Real-time analytics, monitoring, fraud detection, data enrichment, streaming lakehouse	Event-driven microservices, log aggregation, data integration, message buffering

RisingWave vs Apache Kafka: Stream Processing Beyond Message Transport

Why do teams add RisingWave to their Kafka stack?

How does RisingWave compare to Apache Kafka?

Frequently Asked Questions