Streaming ETL vs Traditional ETL: The Complete Comparison (2026)

Traditional ETL (Extract, Transform, Load) runs on a schedule — hourly, daily, or weekly — processing data in bulk batches. Streaming ETL processes data continuously as it arrives, delivering results in seconds instead of hours. In 2026, streaming ETL with tools like RisingWave, Apache Flink, and Confluent is replacing batch ETL for workloads where data freshness drives business value.

How They Compare

Streaming ETL with SQL

In RisingWave, a streaming ETL pipeline is defined entirely in SQL:

-- Extract: Ingest from PostgreSQL CDC
CREATE SOURCE pg_orders WITH (connector = 'postgres-cdc', hostname = 'db-host', ...);
CREATE TABLE orders (...) FROM pg_orders TABLE 'public.orders';

-- Transform: Clean, enrich, aggregate
CREATE MATERIALIZED VIEW order_metrics AS
SELECT DATE(order_time) as order_date, region,
       COUNT(*) as orders, SUM(amount) as revenue,
       AVG(amount) as avg_order_value
FROM orders WHERE status != 'cancelled'
GROUP BY DATE(order_time), region;

-- Load: Sink to Iceberg lakehouse
CREATE SINK metrics_to_iceberg AS SELECT * FROM order_metrics
WITH (connector = 'iceberg', catalog.type = 'rest', ...);

This pipeline runs continuously — no Airflow DAGs, no cron jobs, no scheduling.

When to Switch to Streaming ETL

Switch when:

• Business needs data fresher than your batch schedule allows
• You're building real-time dashboards or alerting
• CDC-based replication is a core use case
• You want to eliminate orchestration complexity (Airflow DAGs)

Keep batch ETL when:

• Daily/weekly freshness is sufficient
• Your team is productive with dbt and Airflow
• Workloads are primarily historical analysis
• Cost minimization is the top priority

The Hybrid Approach

Many teams run both:

• Streaming ETL for operational analytics (real-time dashboards, alerting)
• Batch ETL (dbt) for historical analytics (monthly reports, ML features)
• Shared lakehouse (Iceberg) as the common destination

RisingWave sinks to Iceberg, where dbt models can run batch transformations on the same data.

Frequently Asked Questions

Is streaming ETL replacing traditional ETL?

Not entirely. Streaming ETL is replacing batch ETL for workloads requiring real-time data freshness. Traditional batch ETL with dbt and Airflow remains appropriate for historical analysis, complex transformations that don't need real-time results, and cost-sensitive workloads.

Can I use dbt with streaming ETL?

Not directly — dbt runs batch transformations on a schedule. However, you can use streaming ETL (RisingWave) to sink real-time data into Iceberg, then run dbt models on the Iceberg tables for batch-style analytics. This gives you both real-time and historical views of the same data.

What is the easiest streaming ETL tool?

RisingWave provides the simplest streaming ETL experience — define sources, transformations, and sinks entirely in PostgreSQL-compatible SQL. No Java, no cluster management, no orchestration. For teams familiar with SQL and dbt, RisingWave is the most natural transition.