Streaming ETL

Streaming ETL — Real-Time Data Pipelines

Replace batch ETL with continuous, real-time data pipelines. RisingWave transforms streaming data using SQL — ingesting from Kafka, databases, and APIs, then delivering to data lakes, warehouses, and applications with exactly-once semantics.

Start Free Use Cases

SQL

No Code Pipelines

Define sources, transforms, and sinks entirely in SQL. No Python, no Spark, no Airflow DAGs.

Exactly-Once

End-to-End Delivery

Barrier-based checkpointing ensures no data loss or duplication across the entire pipeline.

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Source & Sink Connectors

Kafka, Pulsar, Kinesis, PostgreSQL CDC, MySQL CDC, S3, Iceberg, Delta Lake, and more.

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Data Freshness

Continuous processing replaces hourly batch loads with always-fresh data in downstream systems.

Why Streaming

Why is batch ETL no longer sufficient for modern data architectures?

Batch ETL introduces hours of latency between data creation and data availability. Modern applications demand real-time feature serving, instant fraud detection, and live operational dashboards. Batch pipelines also create brittle dependency chains that are expensive to monitor, debug, and maintain at scale.

Factor	Batch ETL	Streaming ETL (RisingWave)
Data Freshness	Hours	Sub-second
Pipeline Model	Scheduled DAGs	Continuous SQL queries
Failure Recovery	Restart from scratch	Exactly-once checkpoint resume
Language	Python / Spark	Standard SQL
Scaling	Job-level scaling	Automatic parallelism
Late Data Handling	Manual reprocessing	Watermark-based automation

How It Works

How does RisingWave simplify streaming ETL with SQL?

RisingWave lets you define entire ETL pipelines in SQL. Create sources to ingest from Kafka or CDC, write transformations as materialized views, and create sinks to deliver results to downstream systems. No DAG orchestration, no custom code, no infrastructure management.

SQL-Native Pipelines

Define sources, transforms, and sinks entirely in SQL. No Python, no Spark, no Airflow DAGs.

Built-in Connectors

Ingest from Kafka, Pulsar, Kinesis, PostgreSQL CDC, MySQL CDC, and S3 with CREATE SOURCE.

Exactly-Once Delivery

Barrier-based checkpointing ensures no data loss or duplication across the entire pipeline.

Sink to Anywhere

Deliver to Kafka, Iceberg, Delta Lake, PostgreSQL, ClickHouse, Elasticsearch, Redis, and more.

Patterns

What streaming ETL patterns does RisingWave support?

RisingWave supports the most common streaming ETL patterns out of the box, including CDC replication, stream enrichment with dimension joins, real-time aggregation pipelines, and multi-destination fan-out — all defined in standard SQL with automatic exactly-once guarantees.

→CDC replication: capture changes from PostgreSQL or MySQL and replicate to data lakes or warehouses in real time
→Stream enrichment: join live event streams with reference tables to add context before delivery
→Real-time aggregation: compute rolling metrics, windowed summaries, and running totals continuously
→Multi-destination fan-out: transform once, deliver to multiple sinks — Kafka, Iceberg, and PostgreSQL simultaneously
→Schema transformation: reshape, filter, and normalize streaming data with SQL SELECT and WHERE clauses

Frequently Asked Questions

Can RisingWave replace Apache Airflow for real-time pipelines?

Does RisingWave support exactly-once delivery to sinks?

What sources and destinations does RisingWave support?

How do I handle late-arriving data in streaming ETL?

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Replace batch DAGs with continuous SQL pipelines in minutes.

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Exactly-Once Streaming →Stream to Iceberg →