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Databases · head to head

Apache Spark vs RisingWave

Apache Spark logo

Apache Spark

Databases

A distributed engine for batch, SQL, streaming and machine learning workloads over data that does not fit on one machine.

From
Free
Free plan
Yes
Visit Apache SparkFull review
RisingWave logo

RisingWave

Databases

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

From
Free
Free plan
Yes
Visit RisingWaveFull review

The short version

  • Apache Spark is free; RisingWave starts at $0.227/mo.
  • Only Apache Spark lists unified engine; only RisingWave lists SQL materialised views.
  • Prices and features on this page were last checked on 13 November 2026.

Where they differ

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

Attributes where Apache Spark and RisingWave differ
AttributeApache SparkRisingWave
Pricing modelOpen-sourcePer RisingWave Unit hour
PlatformsWebLinux, Self-hosted, Web

Identical on both: starting price (Free), free tier (Yes), 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 Spark

  • Unified engine
  • Catalyst optimiser
  • DataFrame and SQL APIs
  • Structured Streaming
  • Spark Connect
  • Kubernetes and YARN support
  • Table format integration
  • MLlib

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 Spark

  • Nightly ETL over terabytes in object storage, where a single machine would take longer than the batch window allowsnot RisingWave
  • Building and maintaining a lakehouse on Iceberg or Delta Lake, where Spark handles both the writes and the compactionnot RisingWave
  • Feature engineering and model training across datasets too large to fit in pandas on one nodenot RisingWave
  • Migrating legacy MapReduce or Hive workloads onto an engine that is still actively developed and widely supported by cloud vendorsnot RisingWave

RisingWave

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

Where each one falls short

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

Apache Spark

  • Running it well is JVM operations work: executor sizing, shuffle partition counts, off-heap memory and serialisation all have to be tuned, and the failures you actually get are out-of-memory errors and skewed shuffles rather than wrong answers, so you need somebody who can read the Spark UI or you will scale the cluster instead of fixing the query
  • The fastest Spark is not open source. Databricks' Photon engine and comparable vendor accelerations are proprietary, so benchmark numbers quoted for Spark frequently describe a fork you can only rent, and moving off that vendor loses the performance you sized your pipelines around
  • It is a distributed system with distributed overheads, and modern single-node tools such as DuckDB and Polars finish faster on datasets up to hundreds of gigabytes with no cluster to start, so a Spark job below that threshold is paying coordination cost for nothing
  • Structured Streaming is micro-batch, which puts an end-to-end latency floor in the range of hundreds of milliseconds to seconds; workloads that need genuine per-event latency go to Flink instead, and discovering this after building on Spark means a rewrite
  • Major upgrades deliberately break jobs: Spark 4.0 turns ANSI SQL mode on by default, so silent overflow and invalid casts that previously produced nulls now raise runtime errors, and a pipeline that worked for years can start failing purely on upgrade
  • PySpark hides a process boundary, and Python UDFs serialise every row between the JVM and a Python worker; a direct translation of pandas code into PySpark UDFs can run an order of magnitude slower than the equivalent built-in expressions

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 Spark

Free

No published plan breakdown. See the Apache Spark review.

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 ProOn request
    • No core limit
    • Bring your own cloud option
    • Premium features included
  • Self-managed EnterpriseOn request
    • On premises or Kubernetes
    • Premium features unlocked by licence
    • Annual contract with SLA

Which should you pick?

Choose Apache Spark if

  • You need unified engine.
  • You want to start without paying.
  • You also want catalyst optimiser.
Apache Spark review

Choose RisingWave if

  • You need sql materialised views.
  • You want to start without paying.
  • You work on Linux, Self-hosted, Web.
  • You also want postgres wire compatibility.
RisingWave review

Questions people ask

Is Apache Spark or RisingWave better?
Neither clearly leads. Apache Spark 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 Spark or RisingWave?
Apache Spark starts at Free and RisingWave at Free.
Does Apache Spark or RisingWave run on more platforms?
Apache Spark runs on Web. RisingWave runs on Linux, Self-hosted, Web.
Can I use Apache Spark for free?
Both have a free tier, so you can try either at no cost before committing.
What is Apache Spark best used for?
Apache Spark is most often used for nightly etl over terabytes in object storage, where a single machine would take longer than the batch window allows, building and maintaining a lakehouse on iceberg or delta lake, where spark handles both the writes and the compaction, feature engineering and model training across datasets too large to fit in pandas on one node, migrating legacy mapreduce or hive workloads onto an engine that is still actively developed and widely supported by cloud vendors. Of those, nightly etl over terabytes in object storage, where a single machine would take longer than the batch window allows and building and maintaining a lakehouse on iceberg or delta lake, where spark handles both the writes and the compaction are not what RisingWave is typically brought in for.
What can Apache Spark do that RisingWave cannot?
Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming. RisingWave covers SQL materialised views, Postgres wire compatibility, Object storage state, Source connectors.

Answered from the vendors’ own pages

Apache Spark: When is Spark the wrong choice?

When your data fits comfortably on one machine. DuckDB or Polars will process hundreds of gigabytes on a single large node faster than a Spark cluster, without a scheduler, a driver or a shuffle. Spark earns its overhead when the data genuinely does not fit.

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 Spark: Is Spark the same on Databricks as the open source version?

No. Databricks runs its own runtime including the proprietary Photon engine and its own optimisations, so performance figures and some behaviours do not carry over to open source Spark on EMR, Dataproc or your own Kubernetes cluster.

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 Spark: Can I use Spark for real-time processing?

For near-real-time, yes, with Structured Streaming's micro-batch model, which lands in the sub-second to seconds range. For true per-event latency in the low milliseconds, Flink is the usual choice.

RisingWave: Does it replace Flink?

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

Apache Spark: Does upgrading between major versions break things?

Yes, by design in some cases. Spark 4.0 makes ANSI SQL mode the default, which converts previously silent overflow and cast failures into runtime errors. Upgrades need a testing pass over production pipelines rather than a version bump.

RisingWave: Can I query it like Postgres?

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

Apache Spark: Do I need to know Scala?

No. Python covers the vast majority of work and PySpark is the most common interface. Scala still helps when reading the source, writing custom data sources or diagnosing errors that surface as JVM stack traces.

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