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Databases · head to head

Apache Airflow vs RisingWave

Apache Airflow logo

Apache Airflow

Databases

Programmatically author, schedule and monitor data workflows in Python

From
Free
Rated
-
Full reviewWebsite
RisingWave logo

RisingWave

Databases

Streaming database that maintains incremental materialised views in SQL instead of Flink jobs

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Apache Airflow self-hosting is genuinely heavy: scheduler, metadata database, workers and executor choice are a standing operational job; RisingWave anything that does not fit SQL, such as custom windowing, complex event processing or heavy stateful logic, still needs Flink, so RisingWave often adds a system rather than removing one.
  • They diverge on capability: Apache Airflow covers Pipelines as Python, RisingWave covers SQL materialised views.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which Apache Airflow and RisingWave actually diverge.

Attributes where Apache Airflow and RisingWave differ
AttributeApache AirflowRisingWave
Pricing modelOpen source, no licence fee; managed services billed separatelyPer RisingWave Unit hour
PlatformsLinux, Docker, Kubernetes, Self-hostedLinux, Docker, Kubernetes, Cloud

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Databases).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in Apache Airflow

  • Pipelines as Python
  • Web UI
  • Cloud provider packages
  • Jinja templating
  • Retries and dependencies
  • Extensible operators

Only in RisingWave

  • SQL materialised views
  • Postgres wire compatibility
  • Object storage state
  • Source connectors
  • Sink connectors
  • Iceberg tables
  • Watermarks and windowing
  • User defined functions

What people use each for

The jobs each tool is most often brought in to do.

Apache Airflow

  • Scheduling nightly ETL where step order and retries matternot RisingWave
  • Coordinating machine learning training and evaluation runsnot RisingWave
  • Orchestrating dbt runs alongside extraction and loadingnot RisingWave
  • Replacing a sprawl of cron jobs with dependencies and visible run historynot RisingWave

RisingWave

  • A team with Kafka topics that needs continuously fresh aggregates for a dashboard without standing up a Flink clusternot Apache Airflow
  • A fraud or risk team maintaining rolling counters and joins across event streams expressed as SQL viewsnot Apache Airflow
  • A company doing Postgres CDC into a real-time denormalised view for search or servingnot Apache Airflow
  • An analytics group that wants streaming results landed directly into Apache Iceberg without a separate writer jobnot Apache Airflow

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Apache Airflow

  • Self-hosting is genuinely heavy: scheduler, metadata database, workers and executor choice are a standing operational job
  • Built for scheduled batch work, and a poor fit for event-driven or sub-minute latency pipelines
  • Because DAGs are Python that the scheduler parses continuously, expensive top-level code in a DAG file slows the whole scheduler
  • Local development and testing of DAGs is awkward compared with newer orchestrators designed with it in mind

RisingWave

  • Anything that does not fit SQL, such as custom windowing, complex event processing or heavy stateful logic, still needs Flink, so RisingWave often adds a system rather than removing one.
  • The Apache 2.0 community edition excludes premium features behind a licence key, and which capabilities sit on which side of that line moves between releases, so a self-hosted plan can be invalidated by an upgrade.
  • Long-running materialised views accumulate state in object storage, and cost and recovery time grow with retention in ways that are hard to forecast before you are in production.
  • The Postgres compatibility is protocol level; it is not a transactional Postgres and using it as a general purpose database, with point updates or high write concurrency, goes badly.
  • It is a comparatively young venture-funded project competing with Flink, Materialize and warehouse-native streaming, and the ecosystem of connectors, operators and third-party expertise is much thinner.

Pricing, plan by plan

Apache Airflow

Free
  • Apache AirflowFree
    • Full scheduler and web UI
    • All provider packages
    • No task or DAG limits

RisingWave

Free
  • Community EditionFree
    • Apache 2.0 licence, self-hosted
    • Core streaming engine and connectors
    • Premium features excluded and require a licence key
  • Cloud Basic$0.227/hour
    • Billed per RisingWave Unit hour
    • Hosted on AWS, GCP or Azure
    • Capped at 64 cores
  • Cloud Pro$undefined/year
    • No core limit
    • Bring your own cloud option
    • Premium features included
  • Self-managed Enterprise$undefined/year
    • On premises or Kubernetes
    • Premium features unlocked by licence
    • Annual contract with SLA

Which should you pick?

Choose Apache Airflow if

  • You need pipelines as python.
  • You want to start without paying.
  • You work on Linux, Docker, Kubernetes, Self-hosted.
  • You also want web ui.
Apache Airflow review

Choose RisingWave if

  • You need sql materialised views.
  • You want to start without paying.
  • You work on Linux, Docker, Kubernetes, Cloud.
  • You also want postgres wire compatibility.
RisingWave review

Questions people ask

Is Apache Airflow or RisingWave better?
Neither clearly leads. Apache Airflow starts at Free and RisingWave at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Airflow or RisingWave?
Apache Airflow starts at Free and RisingWave at Free.
Does Apache Airflow or RisingWave run on more platforms?
Apache Airflow runs on Linux, Docker, Kubernetes, Self-hosted. RisingWave runs on Linux, Docker, Kubernetes, Cloud.
Can I use Apache Airflow for free?
Both have a free tier, so you can try either at no cost before committing.
What is Apache Airflow best used for?
Apache Airflow is most often used for scheduling nightly etl where step order and retries matter, coordinating machine learning training and evaluation runs, orchestrating dbt runs alongside extraction and loading, replacing a sprawl of cron jobs with dependencies and visible run history. Of those, scheduling nightly etl where step order and retries matter and coordinating machine learning training and evaluation runs are not what RisingWave is typically brought in for.
What can Apache Airflow do that RisingWave cannot?
Apache Airflow covers Pipelines as Python, Web UI, Cloud provider packages, Jinja templating. RisingWave covers SQL materialised views, Postgres wire compatibility, Object storage state, Source connectors.

Answered from the vendors’ own pages

Apache Airflow: Is Apache Airflow free?

Yes. Airflow is open source under the Apache Software Foundation with no licence fee. Costs are the infrastructure to run it, or a managed service such as Google Cloud Composer or Amazon MWAA.

RisingWave: Is RisingWave open source?

The community edition is Apache 2.0 and self-hostable, but a set of premium features requires a paid licence key.

Apache Airflow: What language are Airflow workflows written in?

Python. A workflow is a Python file, so standard language features including loops and datetime handling can generate tasks dynamically, with no XML or command-line configuration.

RisingWave: What does the cloud cost?

It starts at 0.227 US dollars per RisingWave Unit hour on the Basic tier, which is capped at 64 cores.

Apache Airflow: Is Airflow suitable for real-time pipelines?

Not really. Airflow is designed for scheduled batch orchestration. Event-driven or sub-minute work is better served by a streaming platform such as Kafka or a purpose-built streaming engine.

RisingWave: Does it replace Flink?

For SQL-expressible transformations, often yes. For custom stateful processing and complex event handling, no.

Apache Airflow: What are the main alternatives to Airflow?

Dagster and Prefect are the two most commonly weighed against it, both newer and both designed around the local development and testing experience Airflow is criticised for.

RisingWave: Can I query it like Postgres?

Yes over the Postgres wire protocol, but it is an analytical streaming engine, not a transactional database.

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