RisingWave: Add index page

docs/integrate/etl/index.md

@@ -182,6 +182,15 @@ mysql
 - [Automating recurrent CrateDB queries using Node-RED]
 
 
+## RisingWave
+
+:::{toctree}
+:maxdepth: 1
+
+../risingwave/index
+:::
+
+
 ## Singer / Meltano
 
 - [meltano-target-cratedb]

docs/integrate/risingwave/index.md

@@ -0,0 +1,99 @@
+(risingwave)=
+
+# RisingWave
+
+```{div}
+:style: "float: right; margin-left: 0.5em"
+[![](https://www.risingwave.com/_next/static/media/risingwave-logo-black-text.11ccd229.svg){w=180px}](https://www.risingwave.com/)
+```
+
+[RisingWave] is a stream processing and management platform that allows
+configuring data sources, views on that data, and destinations where
+results are materialized.
+It provides both a Postgres-compatible SQL interface, like CrateDB,
+and a DataFrame-style Python interface.
+
+RisingWave can ingest millions of events per second, continuously join
+and analyze live streams with historical data, serve ad-hoc queries at
+low latency, and persist fresh, consistent results to Apache Iceberg™
+or any other downstream system.
+
+![RisingWave overview](https://github.com/user-attachments/assets/5bd27415-300d-4b8a-aa47-196eed041ed7){h=200px}
+
+> Deliver fresh, low-latency insights from real-time streams,
+> database CDC, and time-series data. Bring streaming and batch together,
+> let users join and analyze both live and historical data, and persist
+> results in managed Apache Iceberg™ tables.
+
+:::{dropdown} **Managed RisingWave**
+RisingWave Labs offers [managed products][RisingWave pricing]
+for building prototypes, production workloads, and enterprise-level, critical
+applications.
+:::
+
+```{div}
+:style: "clear: both"
+```
+
+## Synopsis
+
+:::{rubric} RisingWave
+:::
+Load an Apache Iceberg table, and serve it as materialized view.
+```sql
+CREATE SOURCE sensors_readings
+WITH (
+  connector = 'iceberg',
+  database.name='db.db',
+  warehouse.path='s3://warehouse/',
+  table.name='sensors_readings',
+  s3.endpoint = '<YOUR_S3_ENDPOINT>',
+  s3.access.key = '<YOUR_S3_ACCESS_KEY>',
+  s3.secret.key = '<YOUR_S3_SECRET_KEY>',
+  s3.region = '<YOUR_S3_REGION_NAME>'
+);
+```
+```sql
+CREATE MATERIALIZED VIEW average_sensor_readings AS
+SELECT
+  sensor_id,
+  AVG(reading) AS average_reading
+FROM sensors_readings
+GROUP BY sensor_id;
+```
+:::{rubric} CrateDB
+:::
+Converge into a CrateDB table for long-term persistence and efficient querying,
+even on large amounts of data.
+```sql
+CREATE TABLE public.average_sensor_readings (
+  sensor_id BIGINT PRIMARY KEY,
+  average_reading DOUBLE
+);
+```
+:::{note}
+The standard approach with RisingWave would be to use its [CREATE SINK] operation
+to connect to an external target.
+However, because this does not work with CrateDB, a little Python event processor
+is needed to relay the data. An example implementation can be found in the tutorial
+referenced below.
+:::
+
+## Learn
+
+:::{rubric} Tutorials
+:::
+- An example with data coming from an Apache Iceberg table and aggregations
+  materialized in real-time in CrateDB, using RisingWave.
+  See [Stream processing from Iceberg tables to CrateDB using RisingWave].
+
+:::{note}
+We are tracking interoperability issues per [Tool: RisingWave] and appreciate
+any contributions and reports.
+:::
+
+
+[CREATE SINK]: https://docs.risingwave.com/sql/commands/sql-create-sink
+[RisingWave]: https://github.com/risingwavelabs/risingwave
+[RisingWave pricing]: https://www.risingwave.com/pricing/
+[Tool: RisingWave]: https://github.com/crate/crate/labels/tool%3A%20RisingWave