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Apache Spark vs Heap

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
Heap logo

Heap

Technology

Product analytics for the modern product team

From
Free
Rated
-
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.; Heap no built-in A/B testing or feature flags; requires integration with separate tools for experimentation
  • They diverge on capability: Apache Spark covers Unified engine, Heap covers Autocapture.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Apache Spark and Heap differ
AttributeApache SparkHeap
Pricing modelopen-sourceUnknown
PlatformsWebWeb, iOS, Android
FoundedUnknown2013

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

  • Autocapture
  • Retroactive analytics
  • Session replay
  • Funnel analysis
  • User segmentation
  • Path analysis
  • Data science
  • Virtual events

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

Heap

  • User behavior analysisnot Apache Spark
  • Conversion optimizationnot Apache Spark
  • Product adoptionnot Apache Spark
  • Customer journey mappingnot Apache Spark
  • A/B testing analysisnot 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.

Heap

  • No built-in A/B testing or feature flags; requires integration with separate tools for experimentation
  • Group analytics and advanced features require a sales conversation, not self-serve
  • Cloud-only deployment; no self-hosted option for data security or compliance requirements
  • Session replay lacks developer debugging tools compared to PostHog
  • Pricing for Growth and Pro plans requires direct sales contact; no transparency on how pricing scales

Pricing, plan by plan

Apache Spark

Free

No published plan breakdown. See the Apache Spark review.

Heap

Free
  • FreeFree
    • Up to 10,000 monthly sessions
    • Basic charts
    • 6 months data history
  • Growth$undefined/custom
    • Custom session pricing
    • Sense AI assistant
    • 12 months data history
  • Pro$undefined/custom
    • Custom session pricing
    • Account analytics
    • Engagement matrix
  • Premier$undefined/custom
    • Custom session pricing
    • Data warehouse integration
    • Unlimited projects

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 Heap if

  • You need autocapture.
  • You want to start without paying.
  • You work on Web, iOS, Android.
  • You also want retroactive analytics.
Heap review

Questions people ask

Is Apache Spark or Heap better?
Neither clearly leads. Apache Spark starts at Free and Heap at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark or Heap?
Apache Spark starts at Free and Heap at Free.
Does Apache Spark or Heap run on more platforms?
Apache Spark runs on Web. Heap runs on Web, iOS, Android.
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 Heap is typically brought in for.
What can Apache Spark do that Heap cannot?
Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming. Heap covers Autocapture, Retroactive analytics, Session replay, Funnel analysis.

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.

Heap: What does Heap's autocapture feature do?

Heap's autocapture is a single code snippet that automatically captures every click, swipe, tap, pageview, and form fill on your website and apps without requiring manual event setup. Once installed, Heap captures the entire digital experience of every user on every platform with no ongoing engineering maintenance needed.

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.

Heap: What are Heap's pricing plans and how much do they cost?

Heap offers a Free plan for up to 10,000 monthly sessions. Growth, Pro, and Premier plans use custom session-based pricing that requires contacting sales for a quote. Free includes basic charts and 6 months data history. Growth adds the Sense AI assistant. Pro adds account analytics. Premier adds data warehouse integration and dedicated customer success management.

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.

Heap: Does Heap include session replay and A/B testing?

Heap includes integrated session replay showing exactly what users did on your site. However, Heap does not include built-in A/B testing or feature flags. Teams requiring these capabilities must use separate tools or integrate with third-party platforms.

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.

Heap: What integrations does Heap support?

Heap supports over 100 integrations connecting to business tools including marketing platforms, CRMs, and data warehouses. This allows insights to reach relevant teams and ensures data flows to other business systems automatically.

Source
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.

Heap: Does Heap offer self-hosting or is it cloud-only?

Heap is cloud-only and does not offer self-hosted options. Organizations requiring on-premises deployment should consider alternatives like PostHog which supports self-hosting alongside its cloud product.

Source
Heap: What is Sense and how does it help with analytics?

Sense Chat is Heap's AI assistant that enables users to access analytics without extensive technical knowledge. It allows teams to ask questions about user behavior and get answers directly without lengthy onboarding or technical expertise, making insights more accessible to non-technical stakeholders.

Source
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