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Cassandra vs Apache Spark MLlib

Cassandra logo

Cassandra

Databases

Manage massive amounts of data with linear scalability

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: Cassandra no support for joins across tables; 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: Cassandra covers Linear Scalability, Apache Spark MLlib covers DataFrame-based pipelines.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Cassandra and Apache Spark MLlib differ
AttributeCassandraApache Spark MLlib
Pricing modelUnknownopen-source
PlatformsLinux, macOS, Windows, Docker, KubernetesLinux, macOS, Windows
CategoryDatabasesMachine Learning
Founded20081999

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 Cassandra

  • Linear Scalability
  • Fault Tolerance
  • Multi-datacenter Replication
  • Tunable Consistency
  • CQL Query Language
  • Distributed Architecture
  • No Single Point of Failure
  • DataStax

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.

Cassandra

  • Real-time applicationsnot Apache Spark MLlib
  • Content managementnot Apache Spark MLlib
  • User profilesnot Apache Spark MLlib
  • Mobile backendsnot Apache Spark MLlib
  • Cachingnot 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 Cassandra
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Cassandra
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Cassandra
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Cassandra

Where each one falls short

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

Cassandra

  • No support for joins across tables
  • No ACID transactions across multiple rows
  • Data model must be designed around query patterns upfront, making schema evolution difficult
  • Partition key misconfigurations can cause uneven data distribution and hotspots that degrade performance

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

Cassandra

Free

No published plan breakdown. See the Cassandra review.

Apache Spark MLlib

Free

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

Which should you pick?

Choose Cassandra if

  • You need linear scalability.
  • You want to start without paying.
  • You work on Linux, macOS, Windows, Docker, Kubernetes.
  • You also want fault tolerance.
Cassandra 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 Cassandra or Apache Spark MLlib better?
Neither clearly leads. Cassandra 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, Cassandra or Apache Spark MLlib?
Cassandra starts at Free and Apache Spark MLlib at Free.
Does Cassandra or Apache Spark MLlib run on more platforms?
Cassandra runs on Linux, macOS, Windows, Docker, Kubernetes. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use Cassandra for free?
Both have a free tier, so you can try either at no cost before committing.
What is Cassandra best used for?
Cassandra is most often used for real-time applications, content management, user profiles, mobile backends. Of those, real-time applications and content management are not what Apache Spark MLlib is typically brought in for.
What can Cassandra do that Apache Spark MLlib cannot?
Cassandra covers Linear Scalability, Fault Tolerance, Multi-datacenter Replication, Tunable Consistency. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

Cassandra: Does Cassandra support joins between tables?

No. Cassandra does not support joins or foreign keys. The data model requires denormalization, meaning data must be duplicated across tables to support different query patterns.

Source
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.

Cassandra: Does Cassandra offer ACID transactions?

No. Cassandra provides only row-level atomicity and isolation, not full ACID transactions across multiple rows or tables. It uses lightweight transactions via Paxos for per-row compare-and-set operations.

Source
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.

Cassandra: What programming languages can connect to Cassandra?

Cassandra supports official drivers for multiple languages including Python, Java, Node.js, and Go, allowing applications to communicate via the native Cassandra protocol.

Source
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.

Cassandra: Can I deploy Cassandra in the cloud?

Yes. Cassandra can run on any cloud platform (AWS, Google Cloud, Azure) via Docker, virtual machines, or managed services like DataStax Astra DB, which provides a fully managed DBaaS option.

Source
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.

Cassandra: Does Cassandra have a free option?

The open source Apache Cassandra is free. DataStax also offers Astra DB with a free tier providing up to 25GB storage and 25 million read/write operations per month.

Source
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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