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Rowy vs Apache Spark MLlib

Rowy logo

Rowy

Spreadsheets

Spreadsheet interface for Google Cloud Firestore with cloud functions written in the browser

From
Free
Rated
-
Full reviewWebsite
Apache Spark MLlib logo

Apache Spark MLlib

Machine Learning

The machine learning library inside Apache Spark, for data that will not fit on one machine

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Rowy every Firestore limit is inherited, including the one megabyte per document ceiling and roughly one sustained write per second per document, which surprises users who expect spreadsheet behaviour.; Apache Spark MLlib the algorithm set has grown slowly and its gradient boosting does not match XGBoost or LightGBM in accuracy or speed, so teams routinely do feature engineering in Spark and then train elsewhere, which undoes the argument for using it at all.
  • They diverge on capability: Rowy covers Firestore backed grid, Apache Spark MLlib covers DataFrame-based pipelines.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which Rowy and Apache Spark MLlib actually diverge.

Attributes where Rowy and Apache Spark MLlib differ
AttributeRowyApache Spark MLlib
Pricing modelOpen source, no licence feeopen-source
PlatformsWebLinux, macOS, Windows
CategorySpreadsheetsMachine Learning
FoundedUnknown1999

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 Rowy

  • Firestore backed grid
  • Browser authored cloud functions
  • Typed columns
  • Runs in your Google Cloud project
  • Role based access
  • Form view
  • Open source

Only in Apache Spark MLlib

  • DataFrame-based pipelines
  • Distributed algorithms
  • Alternating least squares
  • Feature transformers
  • Model selection
  • Pipeline persistence
  • Language bindings
  • Runs in existing Spark deployments

What people use each for

The jobs each tool is most often brought in to do.

Rowy

  • A Firebase application that needs an internal admin interface without building onenot Apache Spark MLlib
  • Operations staff correcting production records in Firestore without developer involvementnot Apache Spark MLlib
  • Attaching a small transformation or notification function to a field change without a full deployment pipelinenot Apache Spark MLlib
  • A content team populating documents that a mobile application reads directly from Firestorenot Apache Spark MLlib

Apache Spark MLlib

  • Training on a data set too large to hold on one machine, where sampling down would lose the rare events you care aboutnot Rowy
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Rowy
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Rowy
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Rowy

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Rowy

  • Every Firestore limit is inherited, including the one megabyte per document ceiling and roughly one sustained write per second per document, which surprises users who expect spreadsheet behaviour.
  • Firestore has no joins, so linking tables in the grid is a convenience layer rather than a relational feature and reports across collections still need a separate query.
  • It is useful only to teams already on Firebase, so choosing it effectively locks the operational tooling to one cloud vendor.
  • Query costs are billed by document read, and an unfiltered grid on a large collection can generate a bill that a spreadsheet user has no intuition for.
  • Development activity has slowed relative to the broader category, so evaluate current maintenance before making it load bearing for internal operations.

Apache Spark MLlib

  • The algorithm set has grown slowly and its gradient boosting does not match XGBoost or LightGBM in accuracy or speed, so teams routinely do feature engineering in Spark and then train elsewhere, which undoes the argument for using it at all.
  • There is no deep learning in MLlib; neural network work on Spark requires a separate integration, and the DataFrame-centred interface is an awkward fit for it.
  • Fitted models serialise into Spark's own format, so low-latency serving needs either a Spark session in the request path, which is far too slow, or a conversion through ONNX or MLeap, and this is where most Spark ML projects stall.
  • Debugging is JVM cluster debugging: executor out-of-memory, shuffle spill, skewed partitions and serialisation failures, so an engineer without Spark operations experience spends more time tuning the cluster than improving the model.
  • The cluster is the real cost and Spark holds executors for the duration of a job, so a badly partitioned training run pays for idle cores across the whole fleet while one straggler task finishes.

Pricing, plan by plan

Rowy

Free
  • Open source self hostedFree
    • No licence fee
    • Deploys into your own Google Cloud project
    • You pay Google Cloud for Firestore reads, writes and storage

Apache Spark MLlib

Free

No published plan breakdown. See the Apache Spark MLlib review.

Which should you pick?

Choose Rowy if

  • You need firestore backed grid.
  • You want to start without paying.
  • You also want browser authored cloud functions.
Rowy review

Choose Apache Spark MLlib if

  • You need dataframe-based pipelines.
  • You want to start without paying.
  • You work on Linux, macOS, Windows.
  • You also want distributed algorithms.
Apache Spark MLlib review

Questions people ask

Is Rowy or Apache Spark MLlib better?
Neither clearly leads. Rowy starts at Free and Apache Spark MLlib at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Rowy or Apache Spark MLlib?
Rowy starts at Free and Apache Spark MLlib at Free.
Does Rowy or Apache Spark MLlib run on more platforms?
Rowy runs on Web. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use Rowy for free?
Both have a free tier, so you can try either at no cost before committing.
What is Rowy best used for?
Rowy is most often used for a firebase application that needs an internal admin interface without building one, operations staff correcting production records in firestore without developer involvement, attaching a small transformation or notification function to a field change without a full deployment pipeline, a content team populating documents that a mobile application reads directly from firestore. Of those, a firebase application that needs an internal admin interface without building one and operations staff correcting production records in firestore without developer involvement are not what Apache Spark MLlib is typically brought in for.
What can Rowy do that Apache Spark MLlib cannot?
Rowy covers Firestore backed grid, Browser authored cloud functions, Typed columns, Runs in your Google Cloud project. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

Rowy: Where is my data stored?

In your own Firestore instance inside your own Google Cloud project. Rowy does not hold a copy.

Apache Spark MLlib: What is the difference between spark.ml and spark.mllib?

spark.ml is the DataFrame-based interface and the one to use. spark.mllib is the older RDD-based package, kept for compatibility, in maintenance and receiving no new features.

Rowy: What does it cost to run?

No licence fee. You pay Google Cloud for Firestore reads, writes, storage and function invocations, and an open grid on a big collection reads a lot.

Apache Spark MLlib: Do I need a cluster?

Spark runs in local mode on one machine, which is useful for development, but if you are running on one machine you would generally be better served by scikit-learn or XGBoost, which are faster and more capable at that scale.

Rowy: Can it do joins across collections?

Not really. Firestore does not support joins, and no interface layer can add them.

Apache Spark MLlib: Can I use scikit-learn on Spark instead?

Yes, and it is often the better answer. You can distribute independent model fits across the cluster, or use pandas user-defined functions to run per-group models, keeping Spark for the data and a mature library for the modelling.

Rowy: Is there a row limit?

Not from Rowy. The limits that bite are Firestore document size and per document write throughput.

Apache Spark MLlib: How do I serve an MLlib model in real time?

Not directly. Either convert the pipeline to a portable format such as ONNX or MLeap, or reimplement the scoring path. Starting a Spark session per request adds seconds of overhead and is not a serving strategy.

Rowy: Is it suitable if I am not on Firebase?

No. It is specifically a Firestore interface.

Apache Spark MLlib: Is it free?

The library is Apache 2.0 and costs nothing. The cluster it runs on is billed by your cloud provider or by Databricks, and that is the actual expense.

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