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Apache Spark vs Canny

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
Canny logo

Canny

Technology

Capture, organize, and analyze product feedback

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.; Canny tracked user pricing model causes costs to balloon as product grows and engagement increases, creating perverse incentive where more successful feedback gathering means higher costs
  • They diverge on capability: Apache Spark covers Unified engine, Canny covers Feedback boards.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Apache Spark and Canny differ
AttributeApache SparkCanny
Pricing modelopen-sourcesubscription
PlatformsWebWeb, Claude AI (MCP)
FoundedUnknown2015

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 Canny

  • Feedback boards
  • Voting system
  • Roadmap planning
  • Changelog
  • User segmentation
  • Status updates
  • Admin moderation
  • Analytics

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

Canny

  • Feature request managementnot Apache Spark
  • Product roadmappingnot Apache Spark
  • Customer feedback collectionnot Apache Spark
  • Changelog communicationnot Apache Spark
  • User engagementnot 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.

Canny

  • Tracked user pricing model causes costs to balloon as product grows and engagement increases, creating perverse incentive where more successful feedback gathering means higher costs
  • Limited integrations on Core plan; must upgrade to Pro to connect with Jira and Linear
  • Lacks built-in analytics for identifying themes and patterns across hundreds of feedback requests without manual tagging
  • Customizations limited for public-facing interfaces regarding branding and information presentation
  • Autopilot AI cannot be linked to product knowledge bases to better understand products and improve function
  • Slow response times reported by users, affecting feedback management efficiency
  • Limited custom user fields and manual data updates require API access

Pricing, plan by plan

Apache Spark

Free

No published plan breakdown. See the Apache Spark review.

Canny

Free
  • FreeFree
    • Up to 100 tracked users
    • Unlimited feedback
    • 1 board
  • Starter$400/month
    • 1,000 tracked users
    • Unlimited boards
    • Private boards
  • Growth$900/month
    • 5,000 tracked users
    • API access
    • SSO
  • Business$undefined/month
    • Unlimited tracked users
    • White label
    • SLA

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 Canny if

  • You need feedback boards.
  • You want to start without paying.
  • You work on Web, Claude AI (MCP).
  • You also want voting system.
Canny review

Questions people ask

Is Apache Spark or Canny better?
Neither clearly leads. Apache Spark starts at Free and Canny at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark or Canny?
Apache Spark starts at Free and Canny at Free.
Does Apache Spark or Canny run on more platforms?
Apache Spark runs on Web. Canny runs on Web, Claude AI (MCP).
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 Canny is typically brought in for.
What can Apache Spark do that Canny cannot?
Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming. Canny covers Feedback boards, Voting system, Roadmap planning, Changelog.

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.

Canny: Does Canny offer a free plan?

Yes. Canny offers a free plan that allows collecting up to 100 feedback ideas. Free users get basic features without roadmap, changelog, or integrations.

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.

Canny: How is Canny priced?

Canny pricing scales based on tracked users. Core starts at $19/month (100 users, annual), Pro starts at $79/month (100 users, annual). Prices increase as tracked users grow.

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.

Canny: What is a tracked user in Canny?

A tracked user is anyone who posts, votes, or comments on your Canny board or embedded widget. Each unique end user counts once and the count accumulates.

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.

Canny: What is Canny Autopilot?

Canny Autopilot is an AI feature that automatically captures feature requests from communication tools like Gong, Intercom, Slack, and Zendesk. It prioritizes requests by revenue impact.

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.

Canny: Does Canny have a public roadmap feature?

Yes. Canny provides a public roadmap where customers can see what is planned, building, and shipped. Customers can vote on features and watch their feedback progress.

Source
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