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Apache Spark vs Backstage

Apache Spark logo

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
Full reviewWebsite
Backstage logo

Backstage

Developer Tools

Open source internal developer portal framework created and open sourced by Spotify

From
Free
Free plan
Yes
Full reviewWebsite

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.; Backstage backstage is a framework rather than a product: there is no supported turnkey install, so getting to a usable portal means a TypeScript and React project your team owns, hosts and upgrades forever.
  • They diverge on capability: Apache Spark covers Unified engine, Backstage covers Software catalogue.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

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

Attributes where Apache Spark and Backstage differ
AttributeApache SparkBackstage
Pricing modelopen-sourceOpen source, no licence fee
PlatformsWebWeb, Self-hosted, Linux, Docker, Kubernetes
CategoryTechnologyDeveloper Tools

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

  • Software catalogue
  • Software templates
  • TechDocs
  • Plugin architecture
  • Kubernetes plugin
  • Search
  • Entity ownership model
  • Auth provider integrations

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

Backstage

  • An engineering organisation past roughly a hundred services where nobody can reliably answer who owns a given service at two in the morningnot Apache Spark
  • A platform team enforcing golden paths, so a new service is scaffolded with logging, CI and security defaults already wired innot Apache Spark
  • A company consolidating scattered READMEs and Confluence pages into docs that live beside the code and cannot silently rot unnoticednot Apache Spark
  • A regulated business that needs an auditable register of every running service, its owner and its data classificationnot 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.

Backstage

  • Backstage is a framework rather than a product: there is no supported turnkey install, so getting to a usable portal means a TypeScript and React project your team owns, hosts and upgrades forever.
  • Upstream releases move quickly and plugin APIs have broken across major versions, so a customised deployment accumulates upgrade debt and teams routinely fall months behind on versions they cannot cheaply catch up on.
  • The catalogue is only as good as the YAML descriptors engineers remember to write; without enforcement the register drifts out of date, and a developer portal nobody trusts is abandoned faster than one that never existed.
  • Community plugin quality varies sharply, with many unmaintained or pinned to old core versions, so the plugin that made the business case may be the one blocking your next upgrade.
  • Spotify Portal, the packaged commercial route, publishes no price at all and is quoted as a custom annual subscription, so the only cost you can actually forecast up front is the self-hosted one, which is the option with the largest hidden staffing bill.

Pricing, plan by plan

Apache Spark

Free

No published plan breakdown. See the Apache Spark review.

Backstage

Free
  • Backstage (open source)Free
    • Apache 2.0 licence
    • Software catalogue, templates, TechDocs and search
    • Full plugin ecosystem
  • Spotify Portal for Backstage$undefined/year
    • Packaged commercial distribution from Spotify
    • Spotify premium plugins including Soundcheck and Insights
    • Simplified setup and managed upgrade path

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 Backstage if

  • You need software catalogue.
  • You want to start without paying.
  • You work on Web, Self-hosted, Linux, Docker, Kubernetes.
  • You also want software templates.
Backstage review

Questions people ask

Is Apache Spark or Backstage better?
Neither clearly leads. Apache Spark starts at Free and Backstage at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark or Backstage?
Apache Spark starts at Free and Backstage at Free.
Does Apache Spark or Backstage run on more platforms?
Apache Spark runs on Web. Backstage runs on Web, Self-hosted, Linux, Docker, Kubernetes.
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 Backstage is typically brought in for.
What can Apache Spark do that Backstage cannot?
Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming. Backstage covers Software catalogue, Software templates, TechDocs, Plugin architecture.

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.

Backstage: Is Backstage free?

The project is Apache 2.0 with no licence fee. The real cost is engineering time; teams commonly dedicate one to two engineers permanently to running it.

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.

Backstage: Do I need to write code to use it?

Yes. Configuring and extending a Backstage application is a TypeScript and React project. This is the single most common misjudgement buyers make.

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.

Backstage: What is Spotify Portal for Backstage?

A commercial distribution from Spotify with premium plugins and support, generally available since October 2025 and sold as a quoted annual subscription with no published price.

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.

Backstage: Who owns the project?

Spotify created and open sourced it; it is now a CNCF project, so the upstream roadmap is not solely Spotify controlled.

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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