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Databases · head to head

Apache Spark vs EMQX

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
EMQX logo

EMQX

Databases

Erlang MQTT broker for large IoT fleets, relicensed to BSL with production free use limited to one node

From
Free
Free plan
Yes
Visit EMQXFull review

The short version

  • Apache Spark is free; EMQX starts at $234/mo.
  • Only Apache Spark lists catalyst optimiser; only EMQX lists erlang clustering.
  • Prices and features on this page were last checked on 13 November 2026.

Where they differ

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

Attributes where Apache Spark and EMQX differ
AttributeApache SparkEMQX
Pricing modelOpen-sourcePer month by connection and session volume
PlatformsWebLinux, Self-hosted, Web, macOS

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 EMQX

  • Erlang clustering
  • MQTT 5.0 and QUIC
  • Rule engine
  • Data integration sinks
  • Session persistence
  • Multi protocol gateways
  • Authentication and authorisation
  • Dashboard and REST API

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 EMQX
  • Building and maintaining a lakehouse on Iceberg or Delta Lake, where Spark handles both the writes and the compactionnot EMQX
  • Feature engineering and model training across datasets too large to fit in pandas on one nodenot EMQX
  • Migrating legacy MapReduce or Hive workloads onto an engine that is still actively developed and widely supported by cloud vendorsnot EMQX

EMQX

  • A connected vehicle platform needing millions of persistent MQTT sessions with QUIC for handover across mobile networksnot Apache Spark
  • An industrial operator routing sensor telemetry straight into TimescaleDB or Kafka without writing a consumer servicenot Apache Spark
  • An IoT product team that has outgrown a single Mosquitto instance and needs clustering and shared subscriptionsnot Apache Spark
  • A smart building deployment mixing MQTT, CoAP and LwM2M devices on one brokernot 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

EMQX

  • The move to Business Source Licence 1.1 at version 5.9 in May 2025 limits free production use to a single node, so the clustering that justifies choosing EMQX now requires a paid subscription from the second node onward
  • The relicensing arrived in a minor release, which caught users who had pinned to 5.x expecting Apache 2.0 terms and forced legal review of existing deployments
  • Running an Erlang/OTP cluster well requires operational knowledge most teams do not have, and diagnosing distribution, mnesia or memory issues means learning a runtime you use for nothing else
  • Enterprise pricing is quoted rather than published for the self-managed edition, so the cost of the clustered configuration you actually need is invisible until you talk to sales
  • The Serverless tier caps at around a thousand connections and shares infrastructure, which makes it a prototyping tier rather than a genuine small production option

Pricing, plan by plan

Apache Spark

Free

No published plan breakdown. See the Apache Spark review.

EMQX

Free
  • Open Source single nodeFree
    • BSL 1.1 licence since version 5.9
    • Free production use of one node only
    • All former enterprise features visible
  • ServerlessFree
    • Free monthly quota then pay as you go
    • Up to 1,000 connections
    • 8 by 5 support
  • Dedicated Flex$234/mo
    • Single tenant cluster
    • Traffic allowance included
    • 24/7 support
  • Enterprise self-managedOn request
    • Deploy on premises, at the edge or in any cloud
    • Clustering licensed
    • 256 MB maximum message size

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 EMQX if

  • You need erlang clustering.
  • You want to start without paying.
  • You work on Linux, Self-hosted, Web, macOS.
  • You also want mqtt 5.0 and quic.
EMQX review

Questions people ask

Is Apache Spark or EMQX better?
Neither clearly leads. Apache Spark starts at Free and EMQX at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark or EMQX?
Apache Spark starts at Free and EMQX at Free.
Does Apache Spark or EMQX run on more platforms?
Apache Spark runs on Web. EMQX runs on Linux, Self-hosted, Web, macOS.
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 EMQX is typically brought in for.
What can Apache Spark do that EMQX cannot?
Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming. EMQX covers Erlang clustering, MQTT 5.0 and QUIC, Rule engine, Data integration sinks.

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.

EMQX: Is EMQX still open source?

Not in the OSI sense since version 5.9. It is source-available under Business Source Licence 1.1, reverting to Apache 2.0 four years after each release.

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.

EMQX: Can I run a free cluster?

No. The additional use grant permits free production use of one node; clustering requires a commercial licence.

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.

EMQX: What changed for existing users?

Community and enterprise editions merged, so all features are now visible, but the licence terms tightened in the same minor release.

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.

EMQX: Which version is still Apache 2.0?

Releases before 5.9 remain under Apache 2.0, and each BSL release converts to Apache 2.0 four years after publication.

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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