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docs/integrate/etl/index.md

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- [Automating recurrent CrateDB queries using Node-RED]
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## RisingWave
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:::{toctree}
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:maxdepth: 1
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../risingwave/index
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:::
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## Singer / Meltano
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- [meltano-target-cratedb]

docs/integrate/risingwave/index.md

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(risingwave)=
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# RisingWave
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```{div}
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:style: "float: right; margin-left: 0.5em"
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[![](https://www.risingwave.com/_next/static/media/risingwave-logo-black-text.11ccd229.svg){w=180px}](https://www.risingwave.com/)
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```
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[RisingWave] is a stream processing and management platform that allows
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configuring data sources, views on that data, and destinations where
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results are materialized.
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It provides both a Postgres-compatible SQL interface, like CrateDB,
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and a DataFrame-style Python interface.
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RisingWave can ingest millions of events per second, continuously join
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and analyze live streams with historical data, serve ad-hoc queries at
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low latency, and persist fresh, consistent results to Apache Iceberg™
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or any other downstream system.
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![RisingWave overview](https://github.com/user-attachments/assets/5bd27415-300d-4b8a-aa47-196eed041ed7){h=200px}
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> Deliver fresh, low-latency insights from real-time streams,
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> database CDC, and time-series data. Bring streaming and batch together,
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> let users join and analyze both live and historical data, and persist
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> results in managed Apache Iceberg™ tables.
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:::{dropdown} **Managed RisingWave**
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RisingWave Labs offers [managed products][RisingWave pricing]
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for building prototypes, production workloads, and enterprise-level, critical
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applications.
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:::
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```{div}
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:style: "clear: both"
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```
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## Synopsis
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:::{rubric} RisingWave
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:::
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Load an Apache Iceberg table, and serve it as materialized view.
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```sql
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CREATE SOURCE sensors_readings
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WITH (
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connector = 'iceberg',
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database.name='db.db',
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warehouse.path='s3://warehouse/',
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table.name='sensors_readings',
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s3.endpoint = '<YOUR_S3_ENDPOINT>',
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s3.access.key = '<YOUR_S3_ACCESS_KEY>',
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s3.secret.key = '<YOUR_S3_SECRET_KEY>',
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s3.region = '<YOUR_S3_REGION_NAME>'
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);
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```
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```sql
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CREATE MATERIALIZED VIEW average_sensor_readings AS
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SELECT
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sensor_id,
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AVG(reading) AS average_reading
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FROM sensors_readings
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GROUP BY sensor_id;
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```
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:::{rubric} CrateDB
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:::
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Converge into a CrateDB table for long-term persistence and efficient querying,
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even on large amounts of data.
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```sql
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CREATE TABLE public.average_sensor_readings (
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sensor_id BIGINT PRIMARY KEY,
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average_reading DOUBLE
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);
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```
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## Learn
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:::{rubric} Tutorials
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:::
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- An example with data coming from an Apache Iceberg table and aggregations
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materialized in real-time in CrateDB, using RisingWave.
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See [Stream processing from Iceberg tables to CrateDB using RisingWave].
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[RisingWave]: https://github.com/risingwavelabs/risingwave
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[RisingWave pricing]: https://www.risingwave.com/pricing/

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