Executive SummaryUpdated on Jul 27, 2025
Databricks and Dataiku compete in the field of large-scale analytics and data science. Databricks seems to have the upper hand in performance and speed, while Dataiku appeals to users looking for ease of use and visual interface.
Features: Databricks offers a unified platform with scalable Spark clusters, a robust machine learning library, and seamless cloud integration. Dataiku provides a drag-and-drop interface, visual workflows, and promotes effective collaboration without needing extensive coding.
Room for Improvement: Databricks users seek enhanced visualization capabilities, more libraries for predictive analytics, and better BI integration. Pricing, interface simplicity, and documentation depth are also concerns. Dataiku users want improvements in handling complex data types, enhanced GitHub integration, and broader machine learning training capabilities. The pricing model, especially for large deployments, is noted as expensive.
Ease of Deployment and Customer Service: Databricks supports cloud-based deployment, allowing quick scaling and deployment, with generally favorable reviews for technical support. Dataiku offers both on-premises and cloud deployment options, praised for comprehensive documentation and intuitive platform nature, although more detailed support and integration assistance is needed.
Pricing and ROI: Databricks utilizes a pay-per-use model, found flexible but sometimes expensive, especially for large-scale deployments. Integration costs can add to the expense, yet performance justifies the cost for many users. Dataiku’s non-consumption-based model is perceived as higher and less cost-effective for smaller cases, with the high price point being a significant drawback despite appreciation for affordability compared to some competitors.
To learn more, read our detailed
Databricks vs. Dataiku Report (Updated: September 2025).
