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Apache Spark vs Greenhouse

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
Rated
-
Full reviewWebsite
Greenhouse logo

Greenhouse

Technology

Hiring software for growing companies

From
On request
Rated
-
Full reviewWebsite

The short version

  • Only Apache Spark has a free tier, so it costs nothing to try first.
  • 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.; Greenhouse core plan lacks talent discovery and contact lookups
  • They diverge on capability: Apache Spark covers Unified engine, Greenhouse covers Applicant tracking.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Apache Spark and Greenhouse differ
AttributeApache SparkGreenhouse
Starting priceFreeOn request
Pricing modelopen-sourcequote
Free tierYesNo
PlatformsWebWeb, Ios, Android, Api
FoundedUnknown2012

Identical on both: user rating (Not yet rated), category (Technology).

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 Greenhouse

  • Applicant tracking
  • Interview scheduling
  • Scorecard system
  • Job board posting
  • Candidate CRM
  • Reporting & analytics
  • Offer management
  • EEO compliance

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

Greenhouse

  • Enterprise hiring and recruiting automationnot Apache Spark
  • Multi-location and multi-team talent acquisitionnot Apache Spark
  • Structured interview process managementnot 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.

Greenhouse

  • Core plan lacks talent discovery and contact lookups
  • Core plan lacks email automation and applicant texting
  • Plus plan lacks resume anonymisation and application limits
  • Plus plan lacks audit logging and developer tools
  • Pricing customised by hiring volume and company size, not published
  • Only Pro tier offers audit logs and developer sandbox

Pricing, plan by plan

Apache Spark

Free

No published plan breakdown. See the Apache Spark review.

Greenhouse

On request

No published plan breakdown. See the Greenhouse review.

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 Greenhouse if

  • You need applicant tracking.
  • You work on Web, Ios, Android, Api.
  • You also want interview scheduling.
Greenhouse review

Questions people ask

Is Apache Spark or Greenhouse better?
Neither clearly leads. Apache Spark starts at Free and Greenhouse at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark or Greenhouse?
Apache Spark has a free tier; the other does not. Paid plans start at Free for Apache Spark and On request for Greenhouse.
Does Apache Spark or Greenhouse run on more platforms?
Apache Spark runs on Web. Greenhouse runs on Web, Ios, Android, Api.
Can I use Apache Spark for free?
Yes. Apache Spark has a free tier, so you can try it without paying. Greenhouse starts at On request.
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 Greenhouse is typically brought in for.
What can Apache Spark do that Greenhouse cannot?
Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming. Greenhouse covers Applicant tracking, Interview scheduling, Scorecard system, Job board posting.

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.

Greenhouse: How much does Greenhouse cost?

Greenhouse does not publish specific pricing. The company states that pricing is customized based on your hiring needs, hiring volume, organizational complexity, and required features.

Source
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.

Greenhouse: What are the Greenhouse pricing tiers?

Greenhouse offers three plan levels: Core (basic hiring structure), Plus (multi-location optimization), and Pro (complex enterprise hiring). Exact pricing requires contacting Greenhouse for a custom quote.

Source
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.

Greenhouse: Do all Greenhouse tiers include the same features?

No. Core includes sourcing and scheduling, Plus adds automation and texting, and Pro adds enterprise data configuration and audit logs. All tiers can be customized.

Source
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.

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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