Databricks vs BigML
Compare data AI Tools
0% Similar based on 0 shared tags
Databricks
Unified data and AI platform with lakehouse architecture collaborative notebooks SQL warehouse ML runtime and governance built for scalable analytics and production AI.
Pricing Usage based, contact sales
Category data
Difficulty Beginner
Type Web App
Status Active
BigML
End to end machine learning platform with GUI and REST API that covers data prep modeling evaluation deployment and governance for cloud or on premises use.
Pricing Free trial, contact sales
Category data
Difficulty Beginner
Type Web App
Status Active
Feature Tags Comparison
Only in Databricks
lakehouseetlsqlmlmlflowvector-search
Shared
None
Only in BigML
machine-learningautomlapideploymentsgovernancecloud
Key Features
Databricks
- • Lakehouse storage and compute that unifies batch streaming BI and ML on open formats for cost and portability across clouds
- • Collaborative notebooks and repos that let data and ML teams build together with version control alerts and CI friendly patterns
- • SQL Warehouses that power dashboards and ad hoc analysis with elastic clusters and fine grained governance via catalogs
- • MLflow native integration for experiment tracking packaging registry and deployment that works across jobs and services
- • Vector search and RAG building blocks that bring enterprise content into assistants under governance and observability
- • Jobs and workflows that schedule pipelines with retries alerts and asset lineage visible in Unity Catalog for audits
BigML
- • GUI and REST API for the full ML lifecycle with reproducible resources
- • AutoML and ensembles
- • Time series anomaly detection clustering and topic modeling
- • WhizzML to script and share pipelines
- • Versioned immutable resources
- • Organizations with roles projects and dashboards
Use Cases
Databricks
- → Build governed data products that serve BI dashboards and ML models without copying data across silos
- → Modernize ETL by shifting to Delta pipelines that handle streaming and batch with fewer moving parts and clearer lineage
- → Deploy RAG assistants that search governed documents with vector indexes and access controls for safe retrieval
- → Scale experimentation with MLflow so teams compare runs promote models and enable reproducible releases
- → Consolidate legacy warehouses and data science clusters to reduce cost and drift while improving security posture
- → Serve predictive features to apps using online stores that sync from batch and streaming pipelines under catalog control
BigML
- → Stand up a governed ML workflow
- → Automate repeatable training and evaluation with WhizzML
- → Detect anomalies for risk monitoring
- → Forecast demand with time series
- → Cluster customers and products
- → Embed predictions through the REST API
Perfect For
Databricks
data engineers analytics leaders ML engineers platform teams and architects at companies that want a governed lakehouse for ETL BI and production AI with usage based pricing
BigML
Data scientists, analytics engineers, and ML platform teams who want a standardized GUI plus API approach to build govern and deploy models
Capabilities
Databricks
Delta Pipelines Professional
SQL Warehouses Professional
MLflow and Features Professional
Vector and RAG Intermediate
BigML
AutoML and Models Professional
Pipelines with WhizzML Professional
Cloud or Private Enterprise
Versioning and Roles Professional
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