Technology · head to head
Apache Spark vs Deno
Apache Spark
Technology
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
Deno
Developer Tools
A fast, open-source, fully Node-compatible JS runtime with TypeScript baked right in
- From
- Free
- Free plan
- Yes
The short version
- Each has a real cost: 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.; Deno egress bandwidth overage costs $0.20/GB which can become expensive for high-traffic applications
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which Apache Spark and Deno actually diverge.
| Attribute | Apache Spark | Deno |
|---|---|---|
| Pricing model | open-source | freemium |
| Category | Technology | Developer Tools |
Identical on both: starting price (Free), free tier (Yes), platforms (Web), user rating (Not yet rated).
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 Deno
Nothing recorded that Apache Spark does not also cover.
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 Deno
- Building and maintaining a lakehouse on Iceberg or Delta Lake, where Spark handles both the writes and the compactionnot Deno
- Feature engineering and model training across datasets too large to fit in pandas on one nodenot Deno
- Migrating legacy MapReduce or Hive workloads onto an engine that is still actively developed and widely supported by cloud vendorsnot Deno
Deno
- Running TypeScript natively without transpilation overheadnot Apache Spark
- Migrating Node.js applications to secure-by-default runtimenot Apache Spark
- Deploying edge functions and serverless containers seamlesslynot Apache Spark
- Consolidating developer tools into single binarynot 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.
Deno
- Egress bandwidth overage costs $0.20/GB which can become expensive for high-traffic applications
- CPU time overage is $0.10/hour on Pro and Builder tiers, adding to costs for compute-intensive workloads
- Enterprise pricing is not disclosed; requires sales contact
Pricing, plan by plan
Apache Spark
FreeNo published plan breakdown. See the Apache Spark review.
Deno
Free- FreeFree
- 1 million requests/month
- 20GB egress bandwidth
- 10 apps
- Pro$20/month
- 5 million requests/month
- 200GB egress
- 50 apps
- Builder$200/month
- 25 million requests/month
- 2,000GB egress
- 10,000 apps
- Enterprise$null/month
- Custom limits on all metrics
- 99.95% reliability SLA
- Email support 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.
Questions people ask
- Is Apache Spark or Deno better?
- Neither clearly leads. Apache Spark starts at Free and Deno at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Apache Spark or Deno?
- Apache Spark starts at Free and Deno at Free.
- Does Apache Spark or Deno run on more platforms?
- Both run on Web, so platform support will not decide this one for you.
- 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 Deno is typically brought in for.
- What can Apache Spark do that Deno cannot?
- Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming.
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.
Deno: How much does Deno Deploy cost?
Deno Deploy offers a free tier at $0/month, Pro at $20/month, and Builder at $200/month. Enterprise has custom pricing. All plans include base request and bandwidth limits, with overage charges for additional usage.
SourceApache 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.
Deno: What are Deno Deploy's overage charges?
Egress bandwidth overages cost $0.20 per GB. CPU time overages are $0.10 per hour on Pro and Builder tiers. Memory time and KV database operations also incur charges beyond included limits.
SourceApache 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.
Deno: How many apps can I deploy on Deno's free plan?
The free tier includes 10 apps with up to 1 concurrent build. Pro tier supports 50 apps with 3 concurrent builds, and Builder supports 10,000 apps with 20 concurrent builds.
SourceApache 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.
Deno: How many team members can access Deno Deploy?
The free tier supports up to 3 team members. Pro supports 10 team members. Builder supports 25 team members. Enterprise has custom seat limits.
SourceApache 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.
Related pages
More on Apache Spark
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- Deno vs pnpm
- Deno vs Visual Studio Code
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- Deno vs Argo CD
- Deno vs Git
- Deno vs Soketi
- Deno vs Bazel
- Deno vs Pants Build
- Deno vs Tilt
- Deno vs Dagger
- Deno vs RAD Studio
- Deno vs Refact
- Deno vs Restate
- Deno vs RubyMine
- Deno vs SonarQube