Dataiku

Overview

Dataiku is an enterprise AI and machine learning platform that unifies data engineers, data scientists, and business analysts into a single collaborative environment. It streamlines end-to-end data pipelines, from raw data preparation and visual analytics to advanced machine learning model deployment and governance. By bridging low-code/no-code capabilities with full-code environments, Dataiku democratizes AI development across complex organizational structures.

What it does

• Provides low-code and full-code environments for data preparation, machine learning, and visual analytics
• Automates model training, feature engineering, and MLOps deployment across multi-cloud infrastructure
• Offers centralized data governance, lineage tracking, and security monitoring for enterprise AI projects
• Enables collaborative real-time workflows between data science teams and business domain experts.

Who it's for

• Enterprise Data Scientists
• Data Engineers & ML Engineers
• Business Analytics Leaders
• Chief Data Officers (CDOs)

Why it works

• Hybrid approach combines visual drag-and-drop tools with Python/R code notebooks for all technical skill levels
• Cloud-agnostic deployment connects directly with Snowflake, AWS, Azure, and Google Cloud infrastructure
• Centralized governance frameworks ensure regulatory compliance, auditability, and responsible AI practices
• Scalable MLOps features reduce time-to-production for predictive and generative AI models.

Founder story

Dataiku was founded in 2013 in Paris, France, by Florian Douetteau, Clément Stenac, Thomas Cabrol, and Marc Batty. The founders recognized that enterprise data projects failed due to severe friction between technical data teams and business stakeholders. They created Dataiku to bridge this gap, scaling the business into a global enterprise AI leader now headquartered in New York City.

Alternatives

• Databricks
• DataRobot
• C3 AI
• Alteryx

Comparison overview

• Dataiku emphasizes a unified collaborative space for both non-technical analysts and code-first data scientists, whereas Databricks focuses predominantly on developer-centric data engineering and code-first ML workflows.
• Dataiku operates cloud-agnostically across all major cloud data warehouses, unlike hyperscaler native tools like AWS SageMaker or Azure ML.