RisingWave
RisingWave is a SQL-based streaming database designed for real-time data ingestion and processing, targeting developers and data engineers who need to build event-driven applications and real-time analytics without managing complex stream processing infrastructure.
Publisher review
RisingWave is a SQL-based streaming database designed for real-time data ingestion and processing, targeting developers and data engineers who need to build event-driven applications and real-time analytics without managing complex stream processing infrastructure. It is purpose-built for use cases like monitoring, observability, fraud detection, and live dashboards, where low-latency data transformation and querying are critical. The platform offers both a fully managed cloud service (RisingWave Cloud, currently in Public Preview) and a self-managed option under Apache 2.0, making it accessible to teams that want to avoid operational overhead or retain full control on their own Kubernetes infrastructure. RisingWave was founded in 2021 and positions itself as a simpler alternative to traditional stream processors like Apache Flink, combining stream processing with a familiar SQL interface and persistent storage for stateful computations.
RisingWave ingests data from sources like Kafka, Pulsar, and Kinesis, processes it using SQL-based materialized views and time-window aggregations, and serves results directly via a built-in compute node, eliminating the need for a separate serving layer. Key capabilities include automatic schema evolution, which adapts to changing data structures without manual intervention, and efficient backfilling that recomputes historical results when queries change. The platform supports time travel queries, allowing users to query data as it existed at a past point in time, and offers managed Iceberg support for integrating with data lakehouse architectures. RisingWave Cloud provides a 7-day free trial for the Basic plan, which includes up to 64 cores and core features at $0.227 per RWU per hour, while the Pro plan removes core limits and adds elastic scaling, premium features, and an SLA. Network usage (ingress, egress, and PrivateLink) incurs additional costs, and the Basic plan has limited core limits, making it less suitable for large-scale production workloads without upgrading.
RisingWave competes with established data management platforms like Snowflake, Databricks, and Google Cloud Big Data Analytics, but differentiates by focusing on real-time stream processing rather than batch-oriented analytics or data warehousing. It also faces competition from specialized streaming tools like Segment and SAP Data Intelligence, though RisingWave emphasizes its SQL-native approach and lower operational complexity compared to Apache Flink or Kafka Streams. The platform is listed on AWS, GCP, and Azure marketplaces, and its cloud service includes enterprise governance features like Single Sign-On (SSO), LDAP, granular access controls, and integrated secret management. However, RisingWave is a relatively young product (founded in 2021) and its cloud offering is still in Public Preview, which may concern organizations requiring mature, battle-tested solutions for mission-critical deployments.
The honest trade-offs with RisingWave involve its maturity and ecosystem breadth. While it excels at real-time SQL-based stream processing, it lacks the extensive library of connectors and integrations found in more established platforms like Apache Flink or Kafka. The Basic plan's core limits (up to 64 cores) and additional network costs can surprise teams with high data volumes, and the self-managed option requires Kubernetes expertise and an annual contract. RisingWave's focus on streaming means it is not a general-purpose database or data warehouse, so users needing complex batch analytics or ad-hoc querying on historical data may need to pair it with a separate system like Snowflake or Databricks. Additionally, the platform's documentation and community are still growing, which can slow troubleshooting and adoption for teams without deep streaming experience.
How it works
-
SQL-based streaming database
Processes real-time data using standard SQL, enabling materialized views and time-window aggregations without requiring custom code.
-
Automatic schema evolution
Adapts to changing data schemas automatically, reducing manual intervention when source data structures evolve.
-
Time travel queries
Query data as it existed at a past point in time, useful for debugging and historical analysis.
-
Managed Iceberg support
Integrates with Apache Iceberg for data lakehouse architectures, enabling efficient storage and querying of streaming data.
-
Elastic scaling
The Pro plan offers no core limits and elastic scaling, allowing compute resources to adjust based on workload demand.
-
Integrated secret management
Manages credentials and secrets natively within the platform, reducing reliance on external vaults.
-
Single Sign-On (SSO)
Supports SSO for enterprise authentication, simplifying user access management across the organization.
Strengths and trade-offs
Strengths
- Real-time stream processing with SQL eliminates the need for custom streaming code, reducing development time.
- Cost-effective with pay-as-you-go pricing starting at $0.227 per RWU per hour for the Basic plan.
- Fully hosted or self-managed options provide deployment flexibility, including on-premises via Kubernetes.
- Enterprise governance features include granular access controls, LDAP, and integrated secret management.
Trade-offs
- The Basic plan limits cores to 64, which may not suffice for high-throughput production workloads.
- Additional costs for network usage (ingress, egress, PrivateLink) can increase total expenses unexpectedly.
- The cloud service is in Public Preview, lacking the maturity and SLA guarantees of GA products.
- Limited connector ecosystem compared to established platforms like Apache Flink or Kafka Streams.
Pricing context
Basic plan: $0.227/RWU/hour, up to 64 cores, 7-day free trial. Pro plan: pay-as-you-go or annual contract, no core limits, premium features. Self-managed: annual contract, on-prem/K8s, enterprise optimization.
Getting started with RisingWave
-
Sign up for RisingWave Cloud
Go to the RisingWave Cloud website and create an account. Choose the Basic plan to start a 7-day free trial. Provide your email and set a password, then verify your email to activate the account.
-
Connect a data source
In the RisingWave Cloud console, navigate to the Sources section. Click Add Source and select Kafka, Pulsar, or Kinesis. Enter the connection details such as broker URL, topic name, and authentication credentials to start ingesting streaming data.
-
Define a materialized view
Use the SQL editor to create a materialized view that transforms the incoming stream. Write a CREATE MATERIALIZED VIEW statement with aggregations like SUM or AVG over time windows. Execute the query to persist the computed results.
-
Query real-time results
Run a SELECT statement against the materialized view to retrieve live aggregated data. Use the built-in query interface or connect via a PostgreSQL-compatible client. Verify that the output updates as new data streams in.
-
Set up elastic scaling
If you need more capacity, upgrade to the Pro plan in the billing settings. Configure auto-scaling rules to adjust compute resources based on workload. Monitor usage in the dashboard to ensure performance meets your requirements.
Frequently Asked Questions
What is RisingWave and how does it work for real-time data?
RisingWave is a SQL-based streaming database for real-time data ingestion and processing. It ingests data from sources like Kafka, Pulsar, and Kinesis, then uses SQL materialized views and time-window aggregations to process and serve results directly, eliminating the need for a separate serving layer.
How does RisingWave compare to Apache Flink for stream processing?
RisingWave positions itself as a simpler alternative to Apache Flink, combining stream processing with a familiar SQL interface and persistent storage for stateful computations. It reduces operational complexity by avoiding custom streaming code, but has a smaller connector ecosystem and is less mature than Flink.
What are the main use cases for RisingWave?
RisingWave is designed for event-driven applications and real-time analytics, including monitoring, observability, fraud detection, and live dashboards. It excels where low-latency data transformation and querying are critical, processing streaming data with SQL-based materialized views and time-window aggregations.
What is RisingWave Cloud pricing and what do the plans include?
RisingWave Cloud offers a Basic plan at $0.227 per RWU per hour with up to 64 cores and a 7-day free trial. The Pro plan removes core limits, adds elastic scaling and premium features, and is pay-as-you-go or annual. Network usage incurs additional costs for ingress, egress, and PrivateLink.
Can I run RisingWave on my own infrastructure?
Yes, RisingWave offers a self-managed option under Apache 2.0 that runs on your own Kubernetes infrastructure. This requires Kubernetes expertise and an annual contract, but gives you full control over deployment and data, suitable for teams wanting to avoid cloud dependencies.
What are the main trade-offs of using RisingWave?
RisingWave is a young product founded in 2021, with its cloud service in Public Preview. It lacks extensive connectors compared to Apache Flink or Kafka, and the Basic plan's 64-core limit and additional network costs can surprise high-volume users. It is not a general-purpose database for batch analytics.
Alternatives
How RisingWave compares
Direct head-to-head against 2 competitors. Picked by 7wData.
RisingWave
- Pricing
- Basic plan: $0.227/RWU/hour, up to 64 cores, 7-day free trial. Pro plan: pay-as-you-go or annual contract, no core limits, premium features. Self-managed: annual contract, on-prem/K8s, enterprise optimization.
- Target
- RisingWave is a SQL-based streaming database designed for real-time data ingestion and processing, targeting developers and data engineers who need to build event-driven applications and
- Strength
- Real-time stream processing with SQL eliminates the need for custom streaming code, reducing development time.
- Watch for
- The Basic plan limits cores to 64, which may not suffice for high-throughput production workloads.
Materialize
- Pricing
- Custom/Contact sales
- Target
- Enterprise-grade live data layer
- Deployment
- Cloud, On-prem
- Strength
- Strong consistency guarantees
- Watch for
- Complex setup, higher cost
Apache Flink
- Pricing
- Open-source
- Target
- Real-time stream processing
- Deployment
- Cloud, On-prem
- Strength
- Mature ecosystem, extensive integrations
- Watch for
- Steep learning curve, operational overhead
User reviews
No user reviews yet. Be the first to write one.
Sources
Reporting on this tool draws on these publicly available sources.