Machine Learning & Data Science · head to head
BigQuery ML vs Dataiku
BigQuery ML
Machine Learning & Data Science
Machine learning in BigQuery using SQL
- From
- Free
- Rated
- -
Dataiku
Machine Learning & Data Science
Everyday AI, Extraordinary People
- From
- Free
- Rated
- -
The short version
- Each has a real cost: BigQuery ML not available in BigQuery's Standard edition, so the cheapest tier cannot use it; Dataiku no pricing is published at any tier, and the plans page carries no figures at all
- They diverge on capability: BigQuery ML covers SQL-based ML, Dataiku covers Visual data prep.
Where they differ
Only the attributes on which BigQuery ML and Dataiku actually diverge.
| Attribute | BigQuery ML | Dataiku |
|---|---|---|
| Pricing model | usage-based | freemium |
| Platforms | Web | Linux, Mac, Windows, Web |
| Founded | 2008 | 2013 |
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Machine Learning & Data Science).
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 Dataiku
- Visual data prep
- AutoML
- MLOps
- Collaboration
- Governence
- Python
- R
- Spark
Both cover
- Web support
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 Dataiku
- Linear and logistic regression on warehouse datanot Dataiku
- K-means clustering and matrix factorisation for recommendationsnot Dataiku
- Time series forecasting with ARIMA_PLUSnot Dataiku
- Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot Dataiku
Dataiku
- Building and deploying data science and machine learning pipelinesnot BigQuery ML
- Giving analysts and data scientists a shared visual and code environmentnot 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
Dataiku
- No pricing is published at any tier, and the plans page carries no figures at all
- User, row and compute limits are not stated, so nothing about scale can be assessed before contacting sales
- Access begins with a demo request or a trial rather than a self serve signup
Pricing, plan by plan
BigQuery ML
Free- Free TierFree
- 10GB storage
- 1TB queries
- On-Demand$5/TB
- Pay per TB scanned
- ML training costs
Dataiku
Free- Free EditionFree
- Single user
- Core features
- EnterpriseFree
- Full platform
- Collaboration
- MLOps
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 Dataiku if
- You need visual data prep.
- You want to start without paying.
- You work on Linux, Mac, Windows, Web.
- You also want automl.
Questions people ask
- Is BigQuery ML or Dataiku better?
- Neither clearly leads. BigQuery ML starts at Free and Dataiku at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, BigQuery ML or Dataiku?
- BigQuery ML starts at Free and Dataiku at Free.
- Does BigQuery ML or Dataiku run on more platforms?
- BigQuery ML runs on Web. Dataiku runs on Linux, Mac, Windows, Web.
- 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 Dataiku is typically brought in for.
- What can BigQuery ML do that Dataiku cannot?
- BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. Dataiku covers Visual data prep, AutoML, MLOps, Collaboration. Both handle Web support.
Related pages
More on BigQuery ML
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 Apache Spark MLlib
- BigQuery ML vs Weights & Biases
- BigQuery ML vs Alteryx
- BigQuery ML vs Anaconda
- BigQuery ML vs Databricks
- BigQuery ML vs DVC
- Dataiku vs AWS SageMaker
- Dataiku vs Google Vertex AI
- Dataiku vs Azure Machine Learning
- Dataiku vs DataRobot
- Dataiku vs Snowflake
- Dataiku vs TensorFlow
- Dataiku vs Comet ML
- Dataiku vs Keras
- Dataiku vs MLflow
- Dataiku vs Jupyter
- Dataiku vs PyTorch
- Dataiku vs scikit-learn
- Dataiku vs Apache Spark MLlib
- Dataiku vs Weights & Biases
- Dataiku vs Alteryx
- Dataiku vs Anaconda
- Dataiku vs Databricks
- Dataiku vs DVC