Software · head to head
Databricks vs Apache Spark MLlib
Databricks
Software
Unified analytics platform for data engineering and data science
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Databricks cloud compute is billed separately by the cloud provider on top of Databricks DBU charges; Apache Spark MLlib apache Spark MLlib is Apache 2.0 licensed and free with no paid tier from the Apache project itself; SLA-backed support has to be sourced from a third party such as a managed Spark vendor rather than from Apache.
- They diverge on capability: Databricks covers Delta Lake, Apache Spark MLlib covers Classification.
Where they differ
Only the attributes on which Databricks and Apache Spark MLlib actually diverge.
| Attribute | Databricks | Apache Spark MLlib |
|---|---|---|
| Pricing model | usage-based | open-source |
| Platforms | Web, Aws, Azure, Gcp | Linux, macOS, Windows |
| Founded | 2013 | 1999 |
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Unknown).
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 Databricks
- Delta Lake
- MLflow
- Unity Catalog
- Photon Engine
- Collaborative Notebooks
- Auto-scaling
- AWS
- Azure
Only in Apache Spark MLlib
- Classification
- Regression
- Clustering
- Collaborative filtering
- Feature engineering
- Hadoop
- Kafka
- Databricks
Both cover
- Apache Spark
What people use each for
The jobs each tool is most often brought in to do.
Databricks
- Running Spark data engineering pipelines on managed clustersnot Apache Spark MLlib
- Building a lakehouse over data in cloud object storagenot Apache Spark MLlib
- Training and serving machine learning models alongside the datanot Apache Spark MLlib
Apache Spark MLlib
- Large-scale distributed machine learning on Spark clustersnot Databricks
- Classification and regression with decision trees, random forests, gradient-boosted treesnot Databricks
- Clustering with K-means and Gaussian Mixture Modelsnot Databricks
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Databricks
- Cloud compute is billed separately by the cloud provider on top of Databricks DBU charges
- The free trial lasts 14 days
- Discounts require a Committed Use Contract, with larger commitments needed for larger discounts
- Azure Databricks pricing is set by Microsoft rather than by Databricks
- Security and compliance capabilities are sold as separate platform add ons rather than included in the base rate
Apache Spark MLlib
- Apache Spark MLlib is Apache 2.0 licensed and free with no paid tier from the Apache project itself; SLA-backed support has to be sourced from a third party such as a managed Spark vendor rather than from Apache.
Pricing, plan by plan
Databricks
Free- Community EditionFree
- Limited cluster
- Notebook environment
- Community support
- Standard$0.07/DBU
- Jobs compute
- SQL compute
- Standard support
Apache Spark MLlib
FreeNo published plan breakdown. See the Apache Spark MLlib review.
Which should you pick?
Choose Databricks if
- You need delta lake.
- You want to start without paying.
- You work on Web, Aws, Azure, Gcp.
- You also want mlflow.
Choose Apache Spark MLlib if
- You need classification.
- You want to start without paying.
- You work on Linux, macOS, Windows.
- You also want regression.
Questions people ask
- Is Databricks or Apache Spark MLlib better?
- Neither clearly leads. Databricks 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, Databricks or Apache Spark MLlib?
- Databricks starts at Free and Apache Spark MLlib at Free.
- Does Databricks or Apache Spark MLlib run on more platforms?
- Databricks runs on Web, Aws, Azure, Gcp. Apache Spark MLlib runs on Linux, macOS, Windows.
- Can I use Databricks for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Databricks best used for?
- Databricks is most often used for running spark data engineering pipelines on managed clusters, building a lakehouse over data in cloud object storage, training and serving machine learning models alongside the data. Of those, running spark data engineering pipelines on managed clusters and building a lakehouse over data in cloud object storage are not what Apache Spark MLlib is typically brought in for.
- What can Databricks do that Apache Spark MLlib cannot?
- Databricks covers Delta Lake, MLflow, Unity Catalog, Photon Engine. Apache Spark MLlib covers Classification, Regression, Clustering, Collaborative filtering. Both handle Apache Spark.