The JDBC hosted catalog allows you manage Iceberg tables directly inside RisingWave without running a separate catalog service. When you enable the hosted catalog in an Iceberg connection, RisingWave utilizes its internal metastore (which is typically a PostgreSQL instance) to function as a standard Iceberg JDBC Catalog.
  • Table metadata for tables created with the ENGINE = iceberg clause is stored within two system views in RisingWave: iceberg_tables and iceberg_namespace_properties.
  • This implementation is not a proprietary format; it adheres to the standard Iceberg JDBC Catalog protocol.
  • This ensures that the tables remain open and accessible to external tools like Spark, Trino, and Flink that can connect to a JDBC catalog.

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Step 1. Create a connection

To use the hosted catalog, you create an Iceberg connection and set the hosted_catalog parameter to true. This instructs RisingWave to manage the catalog metadata internally.
Syntax
CREATE CONNECTION connection_name
WITH (
    type = 'iceberg',
    warehouse.path = '<storage_path>',
    <object_storage_parameters>,
    hosted_catalog = true
);
Where <storage_path> and <object_storage_parameters> depend on your chosen storage backend (S3, GCS, or Azure Blob). See the object storage configuration for specific parameter details.

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Step 2. Create your first table

Now that you understand how to configure a connection with the hosted catalog, the next step is to create a table and start streaming data. For a complete, step-by-step guide, please follow our quickstart tutorial.

Quickstart: Create a streaming Iceberg table

This tutorial walks you through creating your first native Iceberg table from scratch using the hosted catalog.

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External access to hosted catalog tables

Since the hosted catalog implements the standard Iceberg JDBC catalog protocol, external tools can access your tables by connecting to RisingWave’s metastore.

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Spark example

import org.apache.spark.sql.SparkSession

val spark = SparkSession.builder()
  .appName("AccessRisingWaveIcebergTables")
  .config("spark.sql.extensions", "org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions")
  .config("spark.sql.catalog.risingwave_catalog", "org.apache.iceberg.spark.SparkCatalog")
  .config("spark.sql.catalog.risingwave_catalog.type", "jdbc")
  .config("spark.sql.catalog.risingwave_catalog.uri", "jdbc:postgresql://risingwave-host:4566/dev")
  .config("spark.sql.catalog.risingwave_catalog.jdbc.user", "root")  
  .config("spark.sql.catalog.risingwave_catalog.jdbc.password", "your_password")
  .config("spark.sql.catalog.risingwave_catalog.warehouse", "s3://my-iceberg-bucket/warehouse/")
  .getOrCreate()

// Query the table created in RisingWave
spark.sql("SELECT * FROM risingwave_catalog.public.user_behaviors").show()

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Trino example

Add this to your Trino catalog configuration: 
# risingwave.properties
connector.name=iceberg
iceberg.catalog.type=jdbc
iceberg.jdbc.url=jdbc:postgresql://risingwave-host:4566/dev
iceberg.jdbc.user=root
iceberg.jdbc.password=your_password
Then query from Trino: 
SELECT behavior_type, COUNT(*) as count
FROM risingwave.public.user_behaviors  
GROUP BY behavior_type;