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External Iceberg tables are managed outside of RisingWave, such as S3 Tables, Snowflake-managed Iceberg tables, Databricks-managed Iceberg tables, or self-managed Iceberg deployments. RisingWave connects to these tables through their catalogs and treats them as data sources or data sinks. image.png

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Ingest data from external Iceberg tables

RisingWave can continuously ingest data from append-only Iceberg tables. It monitors snapshots and automatically loads newly appended data, allowing you to consume the table as a live data stream. For details, see Ingest data from Iceberg tables. Example 
CREATE SOURCE orders_src
WITH (
  connector = 'iceberg',
  warehouse.path = 's3://lakehouse/warehouse',
  database.name = 'sales',
  table.name = 'orders',
  catalog.type = 'glue',
  s3.region = 'us-west-2'
);

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Ad hoc analytics on Iceberg data

After the source is created, you can query it directly with SQL. RisingWave retrieves the current snapshot of the Iceberg table at query time. 
-- Inspect recent orders for a specific product
SELECT order_id, user_id, amount, order_ts
FROM orders_src
WHERE product_id = 12345
ORDER BY order_ts DESC
LIMIT 20;

-- Aggregate daily revenue over the past week
SELECT date_trunc('day', order_ts) AS day, SUM(amount) AS revenue
FROM orders_src
WHERE order_ts >= now() - interval '7 days'
GROUP BY 1
ORDER BY day;
These queries run instantly on the latest Iceberg snapshot, making RisingWave useful for interactive analytics without setting up a separate ETL process.

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Continuous analytics with materialized views

For real-time, incremental analytics, create a materialized view on the Iceberg source. RisingWave automatically keeps the view up to date as new snapshots are committed to the Iceberg table. 
CREATE MATERIALIZED VIEW mv_daily_sales AS
SELECT
  date_trunc('day', order_ts) AS day,
  SUM(amount) AS total_sales,
  COUNT(DISTINCT user_id) AS active_users
FROM orders_src
GROUP BY 1;
The materialized view keeps results up to date as RisingWave ingests new Iceberg snapshots. This approach provides low-latency analytics on Iceberg data while maintaining compatibility with the underlying catalog and storage system.

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Deliver data to external Iceberg tables

RisingWave can deliver query results or materialized view outputs to Iceberg tables. The delivered data remains fully compatible with other Iceberg engines such as Spark, Trino, and DuckDB.
  • Supports append-only, upsert, and force-append-only sink modes
  • Guarantees exactly-once delivery
  • Optional file compaction for efficiency
For details, see Deliver data to Iceberg tables. Example 
CREATE SINK daily_sales_sink FROM mv_daily_sales
WITH (
  connector = 'iceberg',
  type = 'append-only',
  warehouse.path = 's3://lakehouse/warehouse',
  database.name = 'sales',
  table.name = 'daily_sales',
  catalog.type = 'rest',
  catalog.uri = 'http://lakekeeper:8181',
  s3.region = 'us-west-2',
  s3.access.key = '...',
  s3.secret.key = '...',
  enable_compaction = true
);
With this configuration, RisingWave acts as a real-time transformation layer between streaming systems and Iceberg storage.

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Loading modes support

Use this matrix to understand current support for ingesting from and delivering to external Iceberg tables.
Loading modeAppend-onlyMutable
One-time loadingSupportedSupported (both equality and position deletes)
Periodic loadingPlanned for a future release (v2.7)Planned for a future release (v2.7)
Continuous loadingSupportedNo support (Iceberg CDC is immature)
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