Data Science Platforms (2026): Dataiku, Databricks, KNIME

Enterprise Data Science & AI Platforms

7 entries

Platforms for collaborative model building and deployment.

01DataikuEnd-to-end platform for AI and machine learning. Amplify the impact and productivity of data science teams, bringing robust tooling for model design, development, and delivery. 02Domino AI PlatformBuild, deploy, and manage AI. Access data, tools, compute, models, and projects across any environment — built, scaled, and governed as one. 03Cloudera AISupports traditional ML, GenAI, and agentic AI; offers secure, scalable, and governed AI development; and ensures data and model privacy from idea to deployment. 04Iguazio AI PlatformOperationalizes and de-risks ML and gen AI applications at scale so organizations can implement AI effectively and responsibly in live business environments. 05DatabricksLakehouse-based platform for data engineering, analytics and machine learning. 06DataRobotEnterprise AI platform for building, governing and operating models. 07H2O.aiOpen-source and enterprise machine learning and generative AI platform.

Visual & Low-code Analytics

5 entries

Workflow-driven analytics for analysts and data scientists.

08Alteryx OneConnects data, business context, and AI for insights that lead to confident decisions — automating workflows across prep, analysis, and action. 09KNIME Analytics PlatformEnsures users remain on the bleeding edge of data science, with 300+ connectors to data sources and integrations to all popular machine learning libraries. 10SpotfireA visual data science platform that makes smart people smarter — combining market-leading visual analytics, data science, and data wrangling to analyze data at-rest, in-motion, and at-scale. 11TelliusThe AI platform for your company's data and knowledge — empower anyone to uncover insights, automate workflows, and drive actions with AI. 12dotData Feature FactoryEmpowers data scientists with automated feature discovery, making it easier to use all your data in building better, more accurate ML models.

Cloud ML Services

2 entries

Managed machine learning from the major clouds.

13Amazon SageMakerAWS's managed service for building, training and deploying ML models. 14Azure Machine LearningMicrosoft's cloud platform for the machine learning lifecycle.

Statistical Software

1 entries

Classical statistics and analysis.

15IBM SPSS StatisticsA comprehensive statistical analysis platform designed to help organizations and individuals extract reliable insights from data, with AI-assisted output explanations.

Notebook Environments

3 entries

Open environments.

16JupyterLabNotebook IDE. 17marimoReactive Python notebooks. 18MLflowOpen-source MLOps and LLMOps platform.

8 entries

Related directories on this site.

19Data ScienceData science hub. 20Data Science IDEsNotebook and IDE tools. 21Data Wrangling ToolsData preparation tools. 22Data Sets for ML/DLDatasets for training and benchmarking. 23Websites to Learn Data ScienceLearning websites. 24Machine LearningMachine learning hub. 25Artificial IntelligenceHub of AI platforms, models, frameworks, governance and learning resources. 26Big Data Analytics ToolsSpark, Hadoop, Trino, Flink, Iceberg, real-time OLAP and cloud big data platforms.

Frequently Asked Questions

What is a data science platform?

It is software that supports the work from data access and preparation through model training, deployment and monitoring, usually with collaboration and governance features.

Platform or notebook?

Notebooks are lightweight individual workspaces; platforms add shared projects, scheduling, deployment and governance for teams.

Low-code or code-first?

Visual tools such as KNIME and Alteryx suit analysts; code-first platforms such as Databricks and Domino suit data scientists and engineers.

How do these relate to MLOps?

Many platforms include MLOps features; see the MLOps Platforms page for tools focused on deployment and operations.

Which additional references does this page add?

The section 'Notebook Environments' lists JupyterLab, marimo, MLflow.

How should I choose?

Consider your data stack, team skills, deployment targets, governance needs and total cost.

Integrated Platforms for Data Science and Machine Learning