Software · head to head
BigQuery ML vs Apache Spark MLlib
The short version
- Each has a real cost: BigQuery ML not available in BigQuery's Standard edition, so the cheapest tier cannot use it; 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: BigQuery ML covers SQL-based ML, Apache Spark MLlib covers Classification.
Where they differ
Only the attributes on which BigQuery ML and Apache Spark MLlib actually diverge.
| Attribute | BigQuery ML | Apache Spark MLlib |
|---|---|---|
| Pricing model | usage-based | open-source |
| Platforms | Web | Linux, macOS, Windows |
| Founded | 2008 | 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 BigQuery ML
- SQL-based ML
- AutoML Tables
- Model export
- Prediction functions
- Feature preprocessing
- BigQuery
- Vertex AI
- TensorFlow
Only in Apache Spark MLlib
- Classification
- Regression
- Clustering
- Collaborative filtering
- Feature engineering
- Apache Spark
- Hadoop
- Kafka
What people use each for
The jobs each tool is most often brought in to do.
BigQuery ML
- Training models in SQL without exporting datanot Apache Spark MLlib
- Linear and logistic regression on warehouse datanot Apache Spark MLlib
- K-means clustering and matrix factorisation for recommendationsnot Apache Spark MLlib
- Time series forecasting with ARIMA_PLUSnot Apache Spark MLlib
- Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot Apache Spark MLlib
Apache Spark MLlib
- Large-scale distributed machine learning on Spark clustersnot BigQuery ML
- Classification and regression with decision trees, random forests, gradient-boosted treesnot BigQuery ML
- Clustering with K-means and Gaussian Mixture Modelsnot BigQuery ML
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
BigQuery ML
- Not available in BigQuery's Standard edition, so the cheapest tier cannot use it
- Billed through BigQuery compute and storage rather than as its own product, so training cost tracks data scanned
- Remote models incur extra Agent Platform charges on top
- Externally trained model types such as boosted trees and AutoML run through Agent Platform rather than inside BigQuery
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
BigQuery ML
Free- Free TierFree
- 10GB storage
- 1TB queries
- On-Demand$5/TB
- Pay per TB scanned
- ML training costs
Apache Spark MLlib
FreeNo published plan breakdown. See the Apache Spark MLlib review.
Which should you pick?
Choose BigQuery ML if
- You need sql-based ml.
- You want to start without paying.
- You also want automl tables.
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 BigQuery ML or Apache Spark MLlib better?
- Neither clearly leads. BigQuery ML 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, BigQuery ML or Apache Spark MLlib?
- BigQuery ML starts at Free and Apache Spark MLlib at Free.
- Does BigQuery ML or Apache Spark MLlib run on more platforms?
- BigQuery ML runs on Web. Apache Spark MLlib runs on Linux, macOS, Windows.
- Can I use BigQuery ML for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is BigQuery ML best used for?
- BigQuery ML is most often used for training models in sql without exporting data, linear and logistic regression on warehouse data, k-means clustering and matrix factorisation for recommendations, time series forecasting with arima_plus. Of those, training models in sql without exporting data and linear and logistic regression on warehouse data are not what Apache Spark MLlib is typically brought in for.
- What can BigQuery ML do that Apache Spark MLlib cannot?
- BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. Apache Spark MLlib covers Classification, Regression, Clustering, Collaborative filtering.
Related pages
More on BigQuery ML
More on Apache Spark MLlib
Keep looking
Other head to heads
- BigQuery ML vs AWS SageMaker
- BigQuery ML vs Google Vertex AI
- BigQuery ML vs Azure Machine Learning
- BigQuery ML vs DataRobot
- BigQuery ML vs Snowflake
- BigQuery ML vs TensorFlow
- BigQuery ML vs Comet ML
- BigQuery ML vs Keras
- BigQuery ML vs MLflow
- BigQuery ML vs Jupyter
- BigQuery ML vs PyTorch
- BigQuery ML vs scikit-learn
- BigQuery ML vs Weights & Biases
- BigQuery ML vs Alteryx
- BigQuery ML vs Anaconda
- BigQuery ML vs Databricks
- BigQuery ML vs Dataiku
- BigQuery ML vs DVC
- Apache Spark MLlib vs AWS SageMaker
- Apache Spark MLlib vs Google Vertex AI
- Apache Spark MLlib vs Azure Machine Learning
- Apache Spark MLlib vs DataRobot
- Apache Spark MLlib vs Snowflake
- Apache Spark MLlib vs TensorFlow
- Apache Spark MLlib vs Comet ML
- Apache Spark MLlib vs Keras
- Apache Spark MLlib vs MLflow
- Apache Spark MLlib vs Jupyter
- Apache Spark MLlib vs PyTorch
- Apache Spark MLlib vs scikit-learn
- Apache Spark MLlib vs Weights & Biases
- Apache Spark MLlib vs Alteryx
- Apache Spark MLlib vs Anaconda
- Apache Spark MLlib vs Databricks
- Apache Spark MLlib vs Dataiku
- Apache Spark MLlib vs DVC