# Dataiku Reviews **Vendor:** Dataiku **Category:** [Generative AI Infrastructure Software](https://www.g2.com/categories/generative-ai-infrastructure) **Average Rating:** 4.4/5.0 **Total Reviews:** 196 ## About Dataiku Dataiku is the Platform for AI Success that unites people, orchestration, and governance to turn AI investments into measurable business outcomes. It helps organizations move from fragmented experimentation to coordinated, trusted execution at scale. Built for AI success: Dataiku brings business experts and AI specialists into the same environment, embedding business context into analytics, models, and AI agents. Business teams can self-serve and innovate, while AI experts build, deploy, and optimize quickly, closing the gap between pilots and production. Orchestration that scales: Dataiku connects data, AI services, and enterprise apps across analytics, machine learning, and AI agents. Integrated workflows deliver value across any cloud or infrastructure without vendor lock-in or fragmentation. Governance you can trust: Dataiku embeds governance across the AI lifecycle, enabling teams to track performance, cost, and risk to keep systems explainable, compliant, and auditable. ## Dataiku Pros & Cons **What users like:** - Users love the **ease of use** of Dataiku, simplifying complex tasks and enhancing productivity in ML development. (82 reviews) - Users appreciate the **user-friendly learning resources** of Dataiku, which simplify the ML development process significantly. (82 reviews) - Users appreciate the **user-friendly interface** of Dataiku, allowing seamless collaboration and ease of use for all skill levels. (46 reviews) - Users love the **easy integrations** in Dataiku, enabling seamless connections to various data sources and platforms. (43 reviews) - Users value the **productivity improvement** from Dataiku's unified platform, enabling efficient data management and analysis for everyone. (42 reviews) - Collaboration (41 reviews) - Users value the **wide range of integrations** in Dataiku, facilitating collaboration and diverse analytics capabilities. (40 reviews) - Performance (40 reviews) - Data Visualization (38 reviews) - Machine Learning (38 reviews) **What users dislike:** - Users find the **steep learning curve** challenging, especially when trying to utilize advanced features effectively. (45 reviews) - Users find the **steep learning curve** challenging, particularly for beginners wanting to use advanced features. (26 reviews) - Users experience **slow performance** in Dataiku, particularly with large datasets and complex scenario executions. (24 reviews) - Users find the **difficult learning curve** of Dataiku challenging, especially for those new to advanced features and integration. (23 reviews) - Users highlight the **expensive pricing structure** as a major drawback, particularly for smaller organizations or teams. (22 reviews) - Complexity (20 reviews) - Users find the **complexity issues** in Dataiku's tools and documentation can hinder their overall experience. (20 reviews) - Users face **performance issues** that disrupt workflow, complicate data preparation, and limit access to plugins for testing. (19 reviews) - Missing Features (16 reviews) - Data Management Issues (14 reviews) ## Dataiku Reviews ### 1. Dataiku:A plug in tool for Data Science **Rating:** 4.5/5.0 stars **Reviewed by:** Rakshith N. | Analyst , Retail, Enterprise (> 1000 emp.) **Reviewed Date:** March 12, 2026 **What do you like best about Dataiku?** What I like most about Dataiku is how it brings the entire data workflow into one place. It allows teams to easily prepare data, build machine learning models, and deploy them without switching between multiple tools. The visual interface makes it easy to understand data pipelines, while still allowing advanced users to write code when needed. This balance between visual tools and coding flexibility makes collaboration between data scientists, analysts, and engineers much smoother. It helps teams move faster from raw data to real insights and production-ready models. **What do you dislike about Dataiku?** One thing I dislike about Dataiku is that it can feel a bit heavy and complex, especially when working with very large datasets or many workflows. Sometimes the interface becomes slower, and managing multiple projects can get confusing. Also, while the visual tools are helpful, certain advanced customizations still require coding, which might be challenging for non-technical users. Overall, it’s a powerful platform, but there is a bit of a learning curve when you first start using it. **What problems is Dataiku solving and how is that benefiting you?** Dataiku helps solve the problem of managing the entire data and machine learning workflow in one platform. Instead of using separate tools for data preparation, analysis, model building, and deployment, Dataiku brings everything together. This makes it easier to organize projects, track data pipelines, and collaborate with other team members. For me, it has been helpful because it simplifies the process of turning raw data into useful insights and models. It also improves collaboration between technical and non-technical teams, since analysts can use the visual interface while data scientists can still write code when needed. Overall, it helps speed up the development process and makes data projects more structured and easier to manage. ### 2. Dataiku: User-Friendly Collaboration Across the Full Data Lifecycle **Rating:** 4.5/5.0 stars **Reviewed by:** Mahmoud H. | DevOps Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** January 25, 2026 **What do you like best about Dataiku?** What I like most about Dataiku is its user-friendly interface and strong collaboration features. It makes it easy for data scientists, analysts, and engineers to work together on the same projects. I also appreciate that it supports the full data lifecycle, from data preparation to machine learning and deployment. **What do you dislike about Dataiku?** One thing I dislike about Dataiku is that it can be quite demanding on system resources, especially when I’m working with large datasets. In addition, some of the more advanced features come with a learning curve, so it can take time to fully understand how to use them effectively. **What problems is Dataiku solving and how is that benefiting you?** Dataiku addresses the challenge of fragmented data workflows by bringing data preparation, analysis, machine learning, and deployment together in a single platform. It also makes it easier for teams to collaborate and automate key processes. For me, this translates into time savings, better productivity, and data projects that are simpler to manage end to end. ### 3. A Tool That Brings Everything Together **Rating:** 4.5/5.0 stars **Reviewed by:** Rodrigo M. | IT Consultant, Mid-Market (51-1000 emp.) **Reviewed Date:** April 07, 2026 **What do you like best about Dataiku?** I really like how Dataiku brings everything together in one place. It makes my workflow feel more organized and less scattered, which helps me stay on track. That said, there are times when it can feel a bit overwhelming, especially with so much in one interface, but overall it still makes my work easier. **What do you dislike about Dataiku?** For me, the biggest downside is that it doesn’t always feel as intuitive as I’d like, especially once I get into the more advanced parts. At times, I end up spending more time trying to figure out how to do something than actually doing it, and that can be pretty frustrating. **What problems is Dataiku solving and how is that benefiting you?** Dataiku helps me bring everything into one place. Before, I had to jump between different tools for data prep, analysis, and modeling, which made the whole process feel scattered and inefficient. Now my workflow feels much more organized and streamlined, and I can spend more time focusing on the actual problem I’m trying to solve instead of constantly managing and switching between tools. ### 4. Flexible AI Platform with Stellar UI, Needs Better Visualization and Deployment Support **Rating:** 4.0/5.0 stars **Reviewed by:** Xiaoguang D. | Principal Adviser Data Science | Senior RioExpert, Small-Business (50 or fewer emp.) **Reviewed Date:** April 13, 2026 **What do you like best about Dataiku?** I think the user interface of Dataiku is very user-friendly. Even if you don't have a strong data science or data engineering background, you can still use it by drawing boxes, which makes it accessible for many people. I also like that you can customize your solutions by writing your own code to cater to specific business needs. Additionally, with its fast-paced development, Dataiku regularly updates and upgrades the system to include the latest AI features, which I find awesome. The graphical, no-code environment significantly reduces my development life cycle, saving at least 50% of my time. It also makes interaction with end users easy because they can access our development environment to see progress and give quick feedback. **What do you dislike about Dataiku?** So first of all, I think I got some limitation that you to be honest with you, because let's say, if you want to display and visualize a large dataset, it always has some limitation. And, also, I find out the dashboard in built by the API is not super fancy and super user friendly. Comparing to Power BI or the other visualization tools like Tableau, I think that's something that you can improve as well. Other main pinpoint for us is about the deployment. Because, you need to link to the different development, the requirements, how to deploy our AI solution, particularly to another cloud form. For example, AWS Azure, I think that we need a little bit more support on this. **What problems is Dataiku solving and how is that benefiting you?** Dataiku helps me build AI solutions like multi-agent systems, handling both test images and numerical data. It significantly reduces my development life cycle by 50% and enhances collaboration by allowing quick user feedback, leading to faster project iterations. ### 5. A Unified Platform That Bridges Data Experts and Business Teams Seamlessly **Rating:** 5.0/5.0 stars **Reviewed by:** Seerapu N. | Backend Developer, Mid-Market (51-1000 emp.) **Reviewed Date:** January 15, 2026 **What do you like best about Dataiku?** Its greatest strength is enabling true collaboration between data experts and business teams on a single platform. It seamlessly bridges technical work like coding and ML engineering with visual and no-code interfaces. This breaks down silos, accelerates project delivery and ensures AI solutions are built with crucial business context, making them more impactful and sustainable. **What do you dislike about Dataiku?** For smaller teams or simpler projects, Dataiku will be premium. The platform's extensive features come with inherent complexity, which can lead to a steeper learning curve. Its pricing model is often seen as enterprise-focused, potentially making it less accessible for startups or individual users who don't need its full collaborative scale. **What problems is Dataiku solving and how is that benefiting you?** Dataiku solves the critical challenges of fragmented data science workflows. It provides a unified, collaborative platform that connects data preparation, experimentation and deployment into one governed environment. This directly benefits us by drastically reducing project lead times, improving model governance and reproducibility and enabling both technical and business users to contribute effectively to data-driven outcomes. ### 6. Simple Data Analysis That Saves Time **Rating:** 5.0/5.0 stars **Reviewed by:** Henri S. | Junior Data scientist, Enterprise (> 1000 emp.) **Reviewed Date:** April 13, 2026 **What do you like best about Dataiku?** The simplicity to analyse data, results, and the gain of time compared to doing all that in python in a classic IDE **What do you dislike about Dataiku?** Recently I did not like how difficult and long it was to add input files in managed folders. I could not upload subfolemders for exemple. Also multiple times when I uploaded large number of files, some were not uploaded. **What problems is Dataiku solving and how is that benefiting you?** I work in data science and we do everything in dataiku ### 7. Effortless Data Collaboration with Robust Features **Rating:** 4.5/5.0 stars **Reviewed by:** Christopher M. **Reviewed Date:** January 14, 2026 **What do you like best about Dataiku?** I like that Dataiku lets me handle data projects and build machine learning models by pulling in data from different sources, cleaning and organizing it, and experimenting with models all in one place. The combination of a visual interface with coding options makes it accessible for both technical and non-technical team members, smoothing out data project management. I love how it reduces repetitive tasks, decreases mistakes, and keeps complex projects organized and running smoothly. It's great that everyone on the team can contribute, no matter their technical skills, making data work easier and less stressful. **What do you dislike about Dataiku?** One thing I’ve noticed about Dataiku is that it can feel a bit overwhelming at first because there are so many features and options. Working with really large datasets or complex workflows can sometimes be a little slow. I also think it could be a bit easier for new users to get started. Overall, it’s a great tool, but a little more guidance and smoother performance would make it even better. **What problems is Dataiku solving and how is that benefiting you?** I use Dataiku to streamline data projects by integrating data sources, cleaning data, and building models in one platform. It allows team collaboration regardless of technical skills, saves time on repetitive tasks, reduces mistakes, and keeps complex projects organized. ### 8. Intuitive Visual Interface, Powerful pipelines, but Needs Better History Management **Rating:** 4.0/5.0 stars **Reviewed by:** Akshay B. | Data Scientist, Small-Business (50 or fewer emp.) **Reviewed Date:** October 14, 2025 **What do you like best about Dataiku?** I really appreciate how the graphical user interface handles paths and threads. It allows you to manage all your code and datasets visually, and everything is automatically aligned, which makes the experience very soothing to use. **What do you dislike about Dataiku?** There isn't anything in particular that I dislike about Dataiku. However, one area for improvement would be better management of the history and recent code I've worked on. It would be helpful if this information were more easily accessible and visually highlighted. **What problems is Dataiku solving and how is that benefiting you?** Managing large datasets was my primary challenge. Having access to a unified portal for both geospatial and other data, along with the required processing power, has been crucial for achieving my objectives as a data scientist. The ability to test various machine learning models in one shot is simply revolutionary. I cannot imagine ever going back to working on my PC for these tasks. ### 9. Flexible and Visual, But Could Improve Code Management **Rating:** 3.0/5.0 stars **Reviewed by:** Aurelien B. | Small-Business (50 or fewer emp.) **Reviewed Date:** April 13, 2026 **What do you like best about Dataiku?** I like that Dataiku makes data analysis more visual and less painful. I appreciate the flexibility of the solutions available, such as the ability to host custom Python webapps, use Python filters, build custom pipelines, and create custom scenarios. The initial setup was super easy after doing the trainings. **What do you dislike about Dataiku?** Webapp code management is challenging because it involves working with one big file, and the limited Python API calls are restrictive. **What problems is Dataiku solving and how is that benefiting you?** I find Dataiku makes data analysis more visual and less painful. ### 10. Dataiku is a great ML Flow and ML OPs tool all the way from a beginner to an expert **Rating:** 4.5/5.0 stars **Reviewed by:** Teeka Raman K. | Analytics Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** March 14, 2025 **What do you like best about Dataiku?** I started using Dataiku as a junior data analyst. The visual recipes have turned around how you built an analytics project from end to end. As I started tackling complex projects and started expanding my knowledge of data science and the domain I am working on, I started to discover the latitudes of capabilities that I can adopt from dataiku tools and api. It has immensely helped me to expedite my career goals. Another fantastic aspect would be the consistent upgradation of the features and tools like Data quality management, LLM mesh and Agentic AI in the studio which becomes an inspiration for me to tryout and implement additional steps (in the ML flow) that helps me increase business value in the projects I am working on. I enrolled in the dataiku academy too. **What do you dislike about Dataiku?** As I described dataiku is fantastic to start with as a beginner but as the project gets more complex, as I started using dataiku apis in python I started feel a lack of detail in the documentation availability. For example, I wish that the dataiku apis for python to have a clearer documentation as we can observe in some libraries like pytorch, Scikit learn, Scipy or plotly. Details like all the parameters available for a specific function and additional parameters which can be used with an example or two explaining what each parameter mean for implementing of the function. The documentation currently available is highly limited in helping me understand the complete capabilities of a specific function or api. So, my best resource for referral often is the blog post answers that the dataiker provides. With gratitude I would request the team to improve the documentation to such an extent it would add value to an experienced ML Ops developer. **What problems is Dataiku solving and how is that benefiting you?** Dataiku simplifies ML Flow and ML Ops process which enables me to focus on data preparation, building models, validating them and implementation. I would like to appreciate the availability of dataiku functionality through dataiku apis which makes it easier of me to create and deploy projects just with python. ### 11. Easy-to-Use Recipes Make Scenario Setup Simple **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Airlines/Aviation | Enterprise (> 1000 emp.) **Reviewed Date:** April 13, 2026 **What do you like best about Dataiku?** Having easy to use recipes with an easy and simple way to setup scenarios **What do you dislike about Dataiku?** It’s not that I dislike this, but I want to have easier tool to use AI with step by step tutorials **What problems is Dataiku solving and how is that benefiting you?** Everything is consolidated into one environment. I have the ability to do so much things in Dataiku ### 12. A Powerful Platform for End-to-End Data Science & Collaboration **Rating:** 4.5/5.0 stars **Reviewed by:** Aniket D. | Graphic Designer, Design, Mid-Market (51-1000 emp.) **Reviewed Date:** August 23, 2025 **What do you like best about Dataiku?** Dataiku is excellent for managing the entire data pipeline from data preparation to machine learning and deployment. The best part is it easy to implement. The best part is how it allows both technical and non-technical users to collaborate on the same platform. Visual workflows make it easy to build projects without heavy coding, while advanced users can still dive deep with Python, R, or SQL. The integration with cloud platforms and version control is also very smooth. **What do you dislike about Dataiku?** The platform can feel heavy for smaller projects, and the initial learning curve is a bit steep for beginners. Also, the licensing costs can be high for small companies or startups. **What problems is Dataiku solving and how is that benefiting you?** For me, Dataiku mainly solves the problem of collaboration between technical and non-technical teams. Earlier, a lot of time used to get wasted when data scientists, analysts, and business teams worked separately and had to constantly exchange files and reports. With Dataiku, we can all work on the same platform data cleaning, model building, and visualization happen in one place. It also saves me from doing repetitive manual tasks since a lot of workflows can be automated. Overall, it has made our data projects faster, more transparent, and easier to manage. ### 13. Template Solutions That Speed Up Implementations **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Pharmaceuticals | Enterprise (> 1000 emp.) **Reviewed Date:** April 17, 2026 **What do you like best about Dataiku?** Template solutions to speed up implementations **What do you dislike about Dataiku?** The template solutions don't expose the code behind so it's sometimes hard to understand how the features work (documentation is not sufficient) **What problems is Dataiku solving and how is that benefiting you?** Quick prototyping of solutions ### 14. Great product with many flexibility and plug-ins **Rating:** 4.5/5.0 stars **Reviewed by:** Donglin J. | Lead Analytic Data Analyst, Enterprise (> 1000 emp.) **Reviewed Date:** April 25, 2025 **What do you like best about Dataiku?** End-to-end platform: From data ingestion and preparation to model deployment and monitoring, Dataiku covers the entire lifecycle of a data project. This eliminates the need for disparate tools and streamlines the entire workflow. Collaborative environment: The platform fosters seamless collaboration through shared projects, commenting, and version control. This ensures everyone is on the same page and contributes their unique expertise. My coworkers share their projects with each other and work together on some projects. Extensive integrations: Dataiku integrates seamlessly with a wide range of databases, cloud platforms, and machine learning libraries. This flexibility allows us to leverage our existing infrastructure and resources. I just learned that a new R library was added recently which could make our life easier on data manipulation. Robust model management: The platform provides comprehensive tools for tracking model performance, managing versions, and ensuring compliance. This is critical for maintaining the accuracy and reliability of our data-driven decisions. **What do you dislike about Dataiku?** The running engine could be tricky, there is no one engine can run it all. Sometimes I have to try different engines to make it work. **What problems is Dataiku solving and how is that benefiting you?** We have data lives in different platforms, such as Google Cloud BigQuery, Helix, Microsoft Excel etc. Our team need to get data from those different data sources and run the ETL process, manipulate the data and generate a single analytical data file for different uses. Dataiku can help use to achieve that goal by the visualized receipts. Another problems is our team has different Data Science skillsets, such as Python and R. Each member created their piece of logic using their preferred coding language. We need to put all their pieces of logic together and generated a unified logic to generate an analytical dataset. We can use Dataiku as a single platform to incorporate Python and R codes together. ### 15. A Complete and Intuitive Platform for Data Science Projects **Rating:** 5.0/5.0 stars **Reviewed by:** Márcio B. | Engenheiro de dados sênior, Small-Business (50 or fewer emp.) **Reviewed Date:** April 25, 2025 **What do you like best about Dataiku?** Dataiku stands out for its ease of use — the drag-and-drop interface combined with the option to code when necessary makes it accessible to a wide range of users. Implementation was smooth and well-supported by the customer success team. I use Dataiku frequently because it offers a vast number of features, from data preparation to model deployment. Integration with other tools like databases, cloud services, and APIs is straightforward, making the platform even more powerful for end-to-end projects. **What do you dislike about Dataiku?** While overall the platform is excellent, some advanced settings and options could be a bit overwhelming for new users. Additionally, the pricing structure can be a limiting factor for smaller companies or teams. **What problems is Dataiku solving and how is that benefiting you?** Dataiku is helping to solve the complexity of managing the full data science workflow, from data ingestion and preparation to model development and deployment. It centralizes collaboration between data scientists, analysts, and business users, ensuring that projects move faster and with better governance. The platform also reduces dependency on purely manual coding, making advanced analytics more accessible. This is benefiting me by significantly increasing productivity, improving project visibility, and enabling faster decision-making based on reliable, well-structured data pipelines. ### 16. Great one-stop shop for all Data Science needs **Rating:** 5.0/5.0 stars **Reviewed by:** Katyayani P. | Data Scientist - AI & Advanced Analytics, Enterprise (> 1000 emp.) **Reviewed Date:** April 25, 2025 **What do you like best about Dataiku?** The plethora of data plug-in sources, tons of options for ready-made recipes (for EDAs, data engineering, & quick-and-dirty analyses). Along with that, we have code-based recipes too to write our own codes. And cherry on top is the collaborative environment without us having to explicitly handle any of that! **What do you dislike about Dataiku?** Wish there was more customization available to some of the visual recipes. Another thing is version control - although Dataiku does handle version control, it is very non-intuitive and difficult to go back to a previous version, or even understand the changes made between different versions. We need to have commit comments and other git-like features for that to work better. **What problems is Dataiku solving and how is that benefiting you?** Dataiku is solving the biggest problem of providing us with computing bandwidth to develop, test, and deploy complex ML-models in a collaborative environment. It is providing us with an end-to-end solution for all our project related needs - right from bringing in data, cleaning and processing it, developing a model, deploying it, and even visualizing results. On the GenAI front, my team and I have been able to spin out multiple pilots and MVPs because of Dataiku's Streamlit + VS Code integration - saving us critical time in deployment and integration ourselves. ### 17. Flexible, Usable but not perfect. **Rating:** 3.0/5.0 stars **Reviewed by:** Jason F. | Manager, Enterprise (> 1000 emp.) **Reviewed Date:** April 24, 2025 **What do you like best about Dataiku?** Dataiku has been a game-changer in democratizing data workflows. I love how intuitive it is for cross-functional teams to build pipelines, transform datasets, and collaborate all within a visual flow and massage the data. It’s the kind of tool that makes manipulation effortless, experimentation easy, and sharing work frictionless. **What do you dislike about Dataiku?** As much as I appreciate how approachable and feature-rich Dataiku is, there are moments where its flexibility feels like a double-edged sword. Some use cases become surprisingly complex due to very recipe logic, and handling parameterized or reusable workflows can feel clunky. It can also be slow as a cloud-based platform, with multiple users editing single workflows. It gets the job done—but sometimes you just wish it handled faster, cleaner, more tactile. **What problems is Dataiku solving and how is that benefiting you?** Dataiku is the backbone for new data warehouse. It's been essential in uncovering decentralized logic and hidden SQL ( tribal knowledge . the visual flows makes transformations traceable and reproducible. The tool succeeds in getting the many different teams working with a single source of truth and eliminating the several different versions that previously floated around the org and lastly, allows for faster delivery capabilities. **Official Response from Taylor MECHAM:** > Thank you for sharing your detailed feedback on Dataiku. We're thrilled to hear that you find our platform intuitive and beneficial for cross-functional collaboration, and appreciate your insights on areas where you feel there is room for improvement. Regarding the challenges you mentioned, we understand the importance of efficiency and will continue to explore ways to optimize speed and streamline workflows for better usability. If you have specific suggestions or encounter any issues in the future, please don't hesitate to reach out to our support team. We value your input and are committed to providing a seamless experience for all our users. Thank you for choosing Dataiku! ### 18. Dataiku review **Rating:** 3.0/5.0 stars **Reviewed by:** Juliette M. | Consultant, Healthcare Data Science and Analytics, Mid-Market (51-1000 emp.) **Reviewed Date:** April 24, 2025 **What do you like best about Dataiku?** I love the platform, it's intuitive and very useful. The llm recipes are especially useful. Overall I think its a great platform, it looks great, it makes sense, and it definetely allows me to do my work quicker. **What do you dislike about Dataiku?** The actual support hasn't always been the best. I've often reached out for support and wasted a lot of time going back and forth without resolving a problem, only to be told that the person trying to help me doesn't know as much on the cloud version of dataiku. The documentation is never cloud-specific too so it's a little confusing. The process through which dataiku have been working out a use case for us has also had some difficulties, **What problems is Dataiku solving and how is that benefiting you?** We are still testing out dataiku, seeing what it can do for us, but so far it's made simple data transformations a lot easier. We are also using some of the traditional data modelling and some traditional ML features. It's been most useful for using llms, allowing us to summarize and extract data from free text, giving us data that we've not been able to access until now ### 19. The one tool to rule theM alL! **Rating:** 4.5/5.0 stars **Reviewed by:** Christian T. | Senior Dataiku Platform Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** April 24, 2025 **What do you like best about Dataiku?** It's hard to highlight a single feature so I will have to mention several: - The ability to serve different personas, such as "coders" and "clickers" means that Dataiku is well received by non Data Scientists. Yet for those that prefer to code they can code as well. - The ability to integrate with so many technologies and compute and storage engines both for ingestion and parallel compute means there is no job too big to be done in Dataiku when you use the right technology. - The Flow makes complex data pipelines simple to understand and design. It also makes it very esy to use. - The integration of Jupyter Notebooks, built-in Git versioning and Python code environment management makes the creation of new projects and project management very easily. - And finally I would like to specifically mention their incredible Support team. In my IT career I have dealt with a myriad of enterprise software vendors including all the large ones and I can honestly say that Dataiku Support is the best one I have dealt with by miles. Response speed is amazing even at weekends or out of hours. It's clear they run a 24x7 operation across the globe. The quality and quantity of the responses from Support es exceptional. Even when asking for code snippets to use Dataiku API, which most vendors will normally charge for under professional services, we have been surprised by their willingness to help and always achieved a outcome. **What do you dislike about Dataiku?** The GUI is inconsistent at times on how certain actions need to be done. While we found Dataiku Support to be exceptional we had less luck in getting new features implemented. Bug fixing has also been slow in our view even though Dataiku has a good release schedule (they usually release a patch release every 2 weeks!). In our view core features and bug fixing should take more priority than LLM features and other new features. It needs more work to improve ML Ops. For instance model drift is also available via an additional plugin and only on certain algorithms. This should be a core capability. Collaboration could also be improved as there are some concurrency issues that need to be fixed. **What problems is Dataiku solving and how is that benefiting you?** Dataiku is our main ML learning platform for all our advanced analytics work loads. ### 20. Dataiku Makes Data-Driven Solutions Simple and Fast **Rating:** 4.5/5.0 stars **Reviewed by:** Sri P. | Data Science Manager, Small-Business (50 or fewer emp.) **Reviewed Date:** April 24, 2025 **What do you like best about Dataiku?** What I like most about Dataiku is how easy it is to use for creating and managing data-driven solutions. The platform has a very friendly interface, so even if you are not expert, you can start to work on your data projects without much trouble. It is simple to make data pipelines, do analytics, and even create machine learning models, all in one place. Also, I really like that Dataiku can connect very easily with different cloud services and data sources. This makes my work much faster and more efficient because I do not need to spend much time on integration. Overall, Dataiku helps a lot to move quickly from raw data to useful results. **What do you dislike about Dataiku?** There are not many things I dislike about Dataiku, but sometimes, if you want to use more advanced features, you need to have deeper technical knowledge. For someone who is just starting, this can be a bit difficult. Also, because Dataiku always adds new features, sometimes it is not easy to keep up and find the best way to use everything. Sometimes, when integrating with some cloud systems, there can be small technical problems, but usually there is good support and documentation to help. **What problems is Dataiku solving and how is that benefiting you?** I use it for OCR automations to extract data from documents, which saves me a lot of manual work. I use Dataiku to create RAG chatbots and connect with large language models, which makes it easy to answer questions and help users quickly. I use Dataiku to make and automate operational reports. Another use case is predictive maintenance for my equipment. ### 21. Dataiku is Awesome **Rating:** 3.0/5.0 stars **Reviewed by:** Satish K. | Senior Data Architect, Enterprise (> 1000 emp.) **Reviewed Date:** April 24, 2025 **What do you like best about Dataiku?** 🔄 Smart Data Preparation Transform raw data into structured, ready-to-use assets using intuitive tools enhanced by AI-driven suggestions, auto-schema detection, and intelligent type recognition. 🧪 Continuous Development Support agile analytics with a CI/CD-style environment where data flows, scripts, and models evolve continuously, promoting rapid iteration and improvement. ⚙️ Ease of Implementation Minimize setup complexity with modular components, drag-and-drop interfaces, and seamless integration with existing data ecosystems (cloud, on-prem, hybrid). ✅ Robust Data Validation Ensure data quality through built-in validation checks, profiling dashboards, and the flexibility to implement custom Python logic for complex or domain-specific rules. 🧠 Scenario Building Model and simulate different business or analytical scenarios using parameterized workflows, branching logic, and reusable components to support what-if analyses. 🌀 Flow Zones Organize and manage data processes in "Flow Zones" — clearly defined stages (e.g., Ingest → Transform → Validate → Output) that make pipeline orchestration transparent and scalable. 📚 Integrated WIKI Page Empower collaboration and knowledge sharing with an embedded WIKI page. Document logic, share best practices, track changes, and onboard new users effortlessly. **What do you dislike about Dataiku?** While DSS offers a powerful visual interface and flexibility, working with large datasets often introduces significant friction, particularly during scenario execution and debugging. 🚧 Key Pain Points: Performance Bottlenecks: Executing complex scenarios on large datasets directly in the DSS engine is slow and resource-intensive, often making it impractical for time-sensitive analytics. Dependence on External Engines: To achieve acceptable performance, teams must offload processing to SQL or Spark engines, requiring: Additional infrastructure setup (clusters, permissions, connections) Advanced SQL or PySpark expertise, which can be a barrier for data analysts or citizen data scientists. Debugging Overhead: Troubleshooting large workflows is cumbersome due to: Limited transparency into underlying code execution Multi-layered architecture (visual flow → Spark/SQL translation → execution engine) Slower iteration cycles, especially with Spark **What problems is Dataiku solving and how is that benefiting you?** ✅ Automated Data Validation Prebuilt validation rules with customizable logic (Python/SQL) Auto-profiling and anomaly detection at ingest Validation integrated directly into data pipelines and alerts 🧠 Smart Data Ingestion & Reading Intelligent schema detection, auto-type inference, and data previews Efficient sampling of large datasets without full-load requirements Flexible connectors for cloud, on-prem, and APIs with minimal setup 📊 Quick Insights Through Data Visualization One-click data summaries with charts, distributions, and KPIs Drill-down capabilities for root-cause analysis Seamless embedding of visuals into flows, dashboards, and WIKI pages 🔐 Built-in Data Governance Centralized metadata catalog and lineage tracking Role-based access controls and audit trails Versioning, change tracking, and approval workflows Integration with data privacy and compliance frameworks (GDPR, HIPAA, etc.) ### 22. Powerful Dataiku Integrations, Though I Haven’t Used It Yet **Rating:** 3.5/5.0 stars **Reviewed by:** Verified User in Banking | Small-Business (50 or fewer emp.) **Reviewed Date:** April 14, 2026 **What do you like best about Dataiku?** All the integrations you can add in Dataiku are really powerful. **What do you dislike about Dataiku?** Im not a user but i have an interest in dataiku **What problems is Dataiku solving and how is that benefiting you?** As a data scientist, I feel the next update in June 2026 would transform my job and make it much easier and more efficient, especially for doing PoCs. ### 23. Great product for pipeline development **Rating:** 4.5/5.0 stars **Reviewed by:** Ashley H. | Business Operations Coordinator, Enterprise (> 1000 emp.) **Reviewed Date:** April 14, 2025 **What do you like best about Dataiku?** Dataiku makes it really easy to organize and develop a data pipeline. Especially if your team works on the same pipelines together, it's really easy to co-work. I love how modular I'm able to make my flow and that I can alternate between SQL/R/Python seamlessly. **What do you dislike about Dataiku?** Sometimes the error messages are really confusing and not helpful, especially if you're running a query downstream. **What problems is Dataiku solving and how is that benefiting you?** Before using Dataiku we struggled to manage large data pipelines in snowflake. Having to save a series of 20+ queries without any kind of visual representation of how they flow into each other was difficult. It was also hard to co-work on the same pipelines, especially since my team works remote. Having dataiku allows us to visually see our entire data flow, which is often over 100 queries. It's also easy to co-work. We can also apply machine learning to a dataset without having to write out a code for it since Dataiku has it built in. ### 24. Why I love Dataiku as a Data Analyst **Rating:** 5.0/5.0 stars **Reviewed by:** AJ C. | Assistant Vice President, Enterprise (> 1000 emp.) **Reviewed Date:** April 24, 2025 **What do you like best about Dataiku?** My first real analytics role began with me using Dataiku. I knew how to write SQL, but the platform gave me multiple options in terms of how to execute queries. Visual recipes were easy, and I of course had thr option to write the actual code too. Being able to track my transformations through the data flow made it easy for me to understand what was going on! **What do you dislike about Dataiku?** I wish there were capabilities to do further excel like formatting within Dataiku. I typically have to export it and then work on the file to make it look pretty. **What problems is Dataiku solving and how is that benefiting you?** Dataiku helps me obtain insights into our NBA tool used by Financial Advisors. NBA Measurement is a huge initiative for us, and as the lead analyst I'm responsible for doing many queries and understanding what are our trends. ### 25. A game changing data science platform **Rating:** 5.0/5.0 stars **Reviewed by:** Lucas M. | Pricing Data Scientist, Mid-Market (51-1000 emp.) **Reviewed Date:** March 24, 2025 **What do you like best about Dataiku?** It's flexibility. I can code and I like that I can create recipes that uses code to process my data. However, I enjoy having the ability to just select a visual recipe and quickly apply transformations without writing lines of coding. This keeps my skills fresh and gives me a productivity boost when I need to deliver quickly. I use the platform on a daily basis and it forms part of my core tools to develop my projects. As a big organisation, we have our own internal support to deal with issues. However, I've attended a meet up and a conference in London and met the UK team. It was an amazing experience and they offered me a lot of support showcasing new features and facilitating the contact with the Dataiku user community. Another aspect that I enjoy is the seamlessly integration with our current data systems. Using Dataiku, I can connect with all of our data sources and develop projects that weren't even possible before. **What do you dislike about Dataiku?** My only dislike about it is the cost. Although I think it delivers what it promises, the cost is a huge barrier within my organisation. I would like to have more of our analysts with access to a designer licence. That would empower them by developing new skills. Today, only a few data scientist (including myself) and a couple of analysts have full access to the tool. **What problems is Dataiku solving and how is that benefiting you?** When I joined the company, we didn't have a ETL manager. I used to write my SQL queries and build the connections using Power Query. The process was cumbersome and used to take a lot of hours to make small progress. With Dataiku, I now can create my models, schedule the refreshes, save the data in a centralised repository and just expose it to my data visualisation tools (Power BI). Another issue was having the compute power to process our data. I work in the energy industry and our data is all half-hourly. With Dataiku, I can use spark on EKS and process huge amount of data in just a fraction of the time I used to. ### 26. Visual Recipes and Ease of Use Make This a Joy to Work With **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Manufacturing | Enterprise (> 1000 emp.) **Reviewed Date:** April 13, 2026 **What do you like best about Dataiku?** I do enjoy greatly the visual recipes and ease of use **What do you dislike about Dataiku?** I dislike the fact that insights sometimes are just a snapshot in time, not re-usable **What problems is Dataiku solving and how is that benefiting you?** It is solving data and analytics problems ### 27. Dataiku : Making your Data Science work easy **Rating:** 4.0/5.0 stars **Reviewed by:** palbha n. | Data Science Specialist, Enterprise (> 1000 emp.) **Reviewed Date:** October 03, 2025 **What do you like best about Dataiku?** I find the platform very easy to use, which makes it great for quickly prototyping and getting your MVP out as soon as possible. It's also simple to plug and play, which really speeds up the process. **What do you dislike about Dataiku?** I find the documentation somewhat incomplete, with few tutorials available. It can be a struggle to find solutions when I need help. **What problems is Dataiku solving and how is that benefiting you?** Both MVP and end-to-end approaches allow for rapid use case development, but when it comes to building large-scale, scalable solutions with real impact, the process can be more challenging. ### 28. A robust, complete, and highly customizable platform! **Rating:** 5.0/5.0 stars **Reviewed by:** Ana Paula R. | Data Scientist, Mid-Market (51-1000 emp.) **Reviewed Date:** March 14, 2025 **What do you like best about Dataiku?** Dataiku has several great features. For me, the most important ones are the model version control, which allows you to track and compare different implementations, making it much easier to retrain and deploy models. Another key feature is the customizable recipes, especially in Python, a widely used language in data science. This brings great flexibility, along with numerous visually intuitive tools within the platform, enabling you to implement your code seamlessly within a data pipeline. **What do you dislike about Dataiku?** I’m not sure if I would point out something I don’t like about Dataiku, but areas for improvement would be the statistical analysis of data within the platform. Sometimes, you might want to perform a test on a column, but the process for graphical visualization either includes only a subset of the data or requires a long path to get there. **What problems is Dataiku solving and how is that benefiting you?** Dataiku is a comprehensive end-to-end platform, which makes it easy to ingest data and manage the entire pipeline until it is consumed by machine learning models. This is especially true for real-time models, where data can arrive through an endpoint, be processed, and then inserted into the model for inference. ### 29. Grow applications and ROI helping business units **Rating:** 5.0/5.0 stars **Reviewed by:** Iván P. | Vice President, Small-Business (50 or fewer emp.) **Reviewed Date:** April 01, 2025 **What do you like best about Dataiku?** Very simple to learn and use and integrate to your environment no matter which cloud provider you use. Streamlined interface. With Dataiku you can easily serve a lot of clients in the company: IT and business units. Helps to democratize access to information and creation of applications. Reuse code ... you have in Python ... use it. Implementing Dataiku is straightforward. Customer support really works **What do you dislike about Dataiku?** There is no simple and scalable price model for Gen AI applications. Dataiku Answers can be much more powerful ,,, it should exploit a data model more easily and give you graphs and not only text as answers. **What problems is Dataiku solving and how is that benefiting you?** - Creating Gen AI Agents (Bots) - Solving Churn (early detecting leaving customers) - Replacing SAS or some processes - Creating demand forecasting models - Replacing hand labour (Excel, CSV, text) processing. ### 30. Very Easy to Use with Numerous Use Cases **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Financial Services | Enterprise (> 1000 emp.) **Reviewed Date:** April 13, 2026 **What do you like best about Dataiku?** Very easy to use and numerous use cases. **What do you dislike about Dataiku?** I don’t dislike that much - nothing to declare here **What problems is Dataiku solving and how is that benefiting you?** Data transformation, reconciliation, machine learning ### 31. A better way to journey through the path of AI **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Oil & Energy | Enterprise (> 1000 emp.) **Reviewed Date:** April 24, 2025 **What do you like best about Dataiku?** Its sheer capabilities of providing almost every aspect of AI project in low code no code way, its been long I did a genuine coding in a project thanks to the dataiku features that makes my job smooth and easy. Customer support is very very prompt and responsive, never I need to wait for more than 4 hours for a response to any query I raised. **What do you dislike about Dataiku?** I understand aggresive version release to keep up with the progress in GenAI field. but in any actual organization its not so easy to keep upgrading version every alternate month. I would love to see some major feature coming out as version release also provide as patch or plugin for previous version to avoid forcing to upgrade everytime. **What problems is Dataiku solving and how is that benefiting you?** We are able to provide our non technical employees a place to develop their idea. So we manage a dataiku platform with centralize architecture that let me as administrator manage the platform and keep my user free of worry and let them focus on use case. ### 32. Great Informaion. Gave me Perspectives on IA and how use by real companies **Rating:** 4.0/5.0 stars **Reviewed by:** Kevin F. | Website Admin and AI Expert, Small-Business (50 or fewer emp.) **Reviewed Date:** April 24, 2025 **What do you like best about Dataiku?** What I liked most about the Dataiku AI Conference was how down-to-earth it all felt, even with all the high-tech talk. They made big ideas like Generative AI and machine learning easy to understand and actually useful for real businesses. The hands-on demos were solid, and you could tell they put thought into making sure folks from all walks of life felt welcome. It wasn’t just smart—it was practical, and that’s what really stuck with me. **What do you dislike about Dataiku?** One thing they could do better is cut down a bit on the buzzwords and tech lingo—sometimes it felt like they were talking to a room full of data scientists only. It’d be great if they offered more sessions geared toward small business folks or hands-on pros who want to use AI without needing a PhD. Also, a few more real-world case studies from regular companies—not just the big guys—would help show how this stuff works in the everyday world. Keep it smart, but make it more relatable. **What problems is Dataiku solving and how is that benefiting you?** The real estate model was very impressive. makes me think of what could be possible for me. ### 33. Dataiku: A High‑Octane Launchpad with Smart Checks and Balances **Rating:** 5.0/5.0 stars **Reviewed by:** Vikas B. | Director, Analytics Consulting, Enterprise (> 1000 emp.) **Reviewed Date:** April 24, 2025 **What do you like best about Dataiku?** Rapid proof‑of‑value: Visual flows & pre‑built connectors allowing to demo a live model on client data within days. Lower change‑management load: Because both power users and casual business folks stay in one UI, training overhead and resistance drops sharply. Smooth exit strategy: Strong vendor support and vibrant community let you hand over the keys without “consultant lock‑in” fears. Future‑proofing: Frequent releases add integrations (e.g., Snowflake Cortex, Vertex AI) are fast enough that the architecture diagrams don’t age out during mid project. **What do you dislike about Dataiku?** Steep license jumps once viewer counts or premium add‑ons grow - budget surprises at renewal. High compute appetite inflates cloud/on‑prem costs. Plugin/Python drift community plugins and shared environments break on upgrades without strict version pinning. Advanced features’ learning curve stalls adoption if you skip structured enablement. UI lag on projects with thousands of datasets pushes you to shared workspaces. **What problems is Dataiku solving and how is that benefiting you?** low code no code platform for enabling end business users ### 34. Informational and Exciting Experience **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Manufacturing | Enterprise (> 1000 emp.) **Reviewed Date:** April 24, 2025 **What do you like best about Dataiku?** Dataiku offers a multitude of benefits that make it an invaluable tool for organizations looking to leverage their data effectively. My favorite things is that its collaborative environment, which fosters teamwork among data scientists, analysts, and business users, enabling them to share insights and work together seamlessly. **What do you dislike about Dataiku?** A couple of things that I dislike about Dataiku, specifically within my organization is that we have had many unannounced errors arise. We could have a flow working for a lengthy period of time then one day it no longer works due to an error. These random errors can stop normal daily tasks to have to investigate errors that were not originally an issue. **What problems is Dataiku solving and how is that benefiting you?** Dataiku has allowed for me to take large forms of data and conduct calculations as well as summarize to use in business each day. Dataiku also allows for me to take this data and add even more information to be able to calculate forecasts and future possible outcomes. ### 35. Revolutionizing the way we interact with data **Rating:** 5.0/5.0 stars **Reviewed by:** Powers P. | Lead, Data Modernization, Mid-Market (51-1000 emp.) **Reviewed Date:** April 24, 2025 **What do you like best about Dataiku?** Dataiku is unbelievably easy to use and implement. It is almost unreal how powerful it is but even with all of the power behind it the set up and mapping is so intuitive you feel like you must be missing steps. It is slowly becoming a daily used system for our company and a cornerstone as we start to modernize our data. The support we have received from the internal team has been nothing short of fantastic. They are there to answer questions, walk you through implementations, and make the already easy integration process, even easier. **What do you dislike about Dataiku?** I haven't found anything I have disliked up until this point. **What problems is Dataiku solving and how is that benefiting you?** We wanted a solution that would allow non technical people interact with our data and gain insights. We wanted them to be able to do this independently and without having a team of report order takers to fulfill requests or random questions. That team still exists but for larger and more strategic requests. ### 36. Fast and Furious **Rating:** 4.5/5.0 stars **Reviewed by:** Abhishek S. | Senior Director - Head of AI, Small-Business (50 or fewer emp.) **Reviewed Date:** April 22, 2025 **What do you like best about Dataiku?** Easy drag and drop allows less SQL skilled people to analyze data. Reduces time to during EDA. Allows for a small team to rapidly build a capability for a new use case - other platforms take days to configure to onboard all users and data sources. Best AI platform to explore, test and create your proof of concepts that require frequent changes. **What do you dislike about Dataiku?** A MLOps module with UI to monitor the health of models that can be used by DS including communication module will be make this platform wholesome for an enterprise offering. **What problems is Dataiku solving and how is that benefiting you?** Scaling to non-DS user base for data analysis Reduced time to onboard a new AI model ### 37. Great orchestration tool for AI/ML/GenAI use cases **Rating:** 4.5/5.0 stars **Reviewed by:** Sumit M. | Consultant, Enterprise (> 1000 emp.) **Reviewed Date:** April 24, 2025 **What do you like best about Dataiku?** I love the fact that Dataiku makes the orchestration of AI/ML/GenAI models so easy and everything is in a single place. **What do you dislike about Dataiku?** There is nothing specific that I dislike but there were certain features that we discovered as part of exploring Dataiku but were later fixed by the Dataiku team. Given it is an evolving product and the AI landscape is changing so fast, they need to catch up faster than their competitors. **What problems is Dataiku solving and how is that benefiting you?** Making the orchestration easier not having to worry about building my own connectors with different sources, code environments, and having to write long lines of code. I can simply use the recipes that are inbuilt. ### 38. Great tool for performing Everyday Data Science & AI **Rating:** 4.0/5.0 stars **Reviewed by:** Tirth S. | Data Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** April 24, 2025 **What do you like best about Dataiku?** The features and capabilities of the Dataiku. I find Dataiku as a one stop shop for doing everyday analytics, data science and AI. **What do you dislike about Dataiku?** The infrastructure setup to host Dataiku stack on-premise was very painful. It took over 2 months just to setup the infrastructure to get Dataiku running on our cloud. **What problems is Dataiku solving and how is that benefiting you?** 1) It provides access to some of the most challenging to implement ML algorithms with just a click. 2) Helps fast track our journey through Data Science and ML use cases. 3) The low code and no code capabilities helps non programmers to get started with ML development and deployment quickly and efficiently. ### 39. My daily use Data Platform **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Research | Mid-Market (51-1000 emp.) **Reviewed Date:** March 25, 2025 **What do you like best about Dataiku?** Like their company motto, Dataiku is our "Everyday AI" platform. From data preparation and Exploratory Data Analysis (EDA), to MLOps and AI models, Dataiku has everything we need to streamline our analytic and AI needs. Integrating our heterogeneous data sources is simple, and allows our staff to find the data they need and enrich it in just a few steps. The team administrating the platform at our organization can focus all their efforts in providing support and onboarding to our new users, and be sure to have the expert and excellent Dataiku support when needed. Today, around 20% of our organization's staff uses Dataiku at least once a week, to consume reports and dashboards, or to do their own analysis, thus making the decision making process a more efficient and robust one. **What do you dislike about Dataiku?** For some users, specially those used to doing all their tasks with code, the onboarding can be challenging. They might struggle to find the added value if they don't have a specific use case to start from. **What problems is Dataiku solving and how is that benefiting you?** Dataiku is solving our problem of producing consistent reports and improving our operations with readily available reports. ### 40. Its one of the smoothest transition for Data files **Rating:** 4.5/5.0 stars **Reviewed by:** shreyang p. | Quality Data Analyst, Enterprise (> 1000 emp.) **Reviewed Date:** April 24, 2025 **What do you like best about Dataiku?** Its really easy to join different servers and get the extract into one other server which makes a hard part easy for me by writing different SQL queries **What do you dislike about Dataiku?** I am still working on the application, but I see that there are not more plug ins for Power BI. If it was possible to send the extract file directly to Power BI that would help a lot for BI's **What problems is Dataiku solving and how is that benefiting you?** I wont need to write different SQL queries to get data out of different servers and also helps me to build a predictive analysis for maintenance shop ### 41. Connected Data & Data Cleansing **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Chemicals | Enterprise (> 1000 emp.) **Reviewed Date:** April 24, 2025 **What do you like best about Dataiku?** The visual flow developer - I think it really helps users understand how their data is processed. The job log - allows user to follow each scenario run and troubleshoot any issues. Ability to copy projects - Often times I am looking to replicate a specific project - DataIku makes that very easy to do - making new project implementation very quick. **What do you dislike about Dataiku?** I can't think of any dislikes. The tool itself is intuitive to use and compartmentalizes information well. It's one tool that I can say I'm truly satisfied with. **What problems is Dataiku solving and how is that benefiting you?** DataIku solves my data connectivity issue - allowing me to interrogate several SQL databases and then clean, filter and sort the data. This data is then pushed to a user friendly output for many industrial employees to see. ### 42. Easy to use Data Analytics Platform **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Airlines/Aviation | Enterprise (> 1000 emp.) **Reviewed Date:** May 06, 2025 **What do you like best about Dataiku?** The UI is easy to use, it just take me small amount of time to learn and understand the concept related to Dataiku and can create my own flow. The CS is very responsive, the reply to my question very fast. **What do you dislike about Dataiku?** I think Dataiku is already working with the latest trend of Ai, but I think it would be better if It include feature like the integrate between copilot & VS code, which allow seamless generation of code by AI **What problems is Dataiku solving and how is that benefiting you?** Dataiku solves problems like complex data pipeline management, collaboration within teams, and automating repetitive AI/ML tasks. It benefits me by simplifying workflows with a visual interface, also me and my teammate could collaborate more easier in the platform. ### 43. Simplifying the Machine Learning Workflow **Rating:** 4.5/5.0 stars **Reviewed by:** Marzieh k. | Data Scientist, Enterprise (> 1000 emp.) **Reviewed Date:** April 24, 2025 **What do you like best about Dataiku?** Love how this app makes ML development so easy! It takes care of the complicated stuff and lets you focus on building cool models **What do you dislike about Dataiku?** after some recent updates, we've experienced a few issues that disrupted our workflow. **What problems is Dataiku solving and how is that benefiting you?** Dataiku simplifies the machine learning workflow by providing built-in recipes that eliminate the need to rewrite repetitive code. This allows me to focus more on the overall pipeline and strategy, rather than getting bogged down in routine coding tasks. It saves time and helps maintain consistency across projects ### 44. Dataiku review **Rating:** 2.5/5.0 stars **Reviewed by:** Katrina B. | Overnight stock associate , Small-Business (50 or fewer emp.) **Reviewed Date:** September 16, 2025 **What do you like best about Dataiku?** I like that its basically ran by Ai and you don't have to do a whole lot **What do you dislike about Dataiku?** Nothing its a great app maybe a little costly but worth it **What problems is Dataiku solving and how is that benefiting you?** It solved my issue with keeping track of all my paperwork it does it all for me ### 45. The closest thing to end-to-end data analytics available. **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Manufacturing | Enterprise (> 1000 emp.) **Reviewed Date:** April 24, 2025 **What do you like best about Dataiku?** Dataiku combines the best features on data exploration, data pipelines, Dev Ops, data visualization, web apps development, machine learning, and generative AI. They are always adding new features, and they are constantly taking feedback and requests from the development community for new features. **What do you dislike about Dataiku?** There can be some translation issues with the terminology used in the product. I would also like to see a semantic layer feature instead of every transformation step writing a new table **What problems is Dataiku solving and how is that benefiting you?** Dataiku allows me to move from prototype to production quickly. During problem solving sessions, I can pull in data from many different sources to analyze and create a report or dashboard to monitor the situation without leaving the tool. ### 46. A partner worth its weight in gold **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Financial Services | Enterprise (> 1000 emp.) **Reviewed Date:** April 24, 2025 **What do you like best about Dataiku?** As someone steering data strategy in wealth management, where client trust and regulatory rigor are non-negotiable, finding a platform that balances innovation with governance is paramount. After 18 months of using Dataiku across our global teams, here’s my candid take. Likes 1. Collaboration That Bridges Silos Dataiku’s unified environment has been transformative for breaking down walls between our quants, business analysts, and risk teams. For instance, building client segmentation models used to take weeks of back-and-forth. Now, data scientists prototype in Python while business analysts tweak logic visually, accelerating time-to-insight. One standout moment: A high-net-worth portfolio risk tool was co-developed by our quant team and advisors in half the usual time, thanks to shared workflows. 2. End-to-End Governance In wealth management, audit trails are lifeblood. Dataiku’s granular permissions and data lineage tracking (who did what, when) have made SOX and GDPR audits less painful. We recently traced a model’s decision logic back through six months of iterations during a regulatory review—without breaking a sweat. 3. Flexibility for Hybrid Use Cases Whether it’s batch-processing historical portfolio performance or real-time dashboards for advisors, Dataiku handles both gracefully. The integration with Snowflake and Tableau streamlined our migration to cloud-native analytics, while plugins for Bloomberg APIs let us pull market data without custom coding. **What do you dislike about Dataiku?** Dislikes** 1. Learning Curve for Non-Tech Stakeholders While analysts love the visual interface, our senior advisors initially struggled to embrace self-service dashboards. We’ve sunk hours into training, and even now, some revert to “just email me the PDF.” Dataiku’s business user onboarding feels half-baked compared to Power BI. 2. Real-Time Analytics Gaps For high-frequency trading scenario, Dataiku’s real-time capabilities lag. We had to bolt on Apache Kafka for live bond pricing alerts—a costly workaround. 3. Performance Hiccups at Scale A European client’s portfolio—10+ years of hourly trades across 20k assets—brought Dataiku to its knees. We ended up pre-aggregating data in Snowflake, which defeated the purpose of “in-platform” big data tools. **What problems is Dataiku solving and how is that benefiting you?** Dataiku isn’t perfect, but it’s the closest we’ve found to a Swiss Army knife for wealth management’s unique demands. The collaboration and governance features alone justify the investment, though I wish the pricing and real-time gaps were addressed. For firms ready to invest in training and hybrid architectures, it’s a powerhouse. Just don’t expect it to replace your entire stack overnight. Would I recommend it? Absolutely—but with a caveat: Treat it as a marathon partner, not a sprinting miracle worker. ### 47. Streamlined Low-code/No-code ETL and ML Platform **Rating:** 4.5/5.0 stars **Reviewed by:** Austin L. | Business Intelligence Manager, Enterprise (> 1000 emp.) **Reviewed Date:** April 09, 2025 **What do you like best about Dataiku?** I have been using Dataiku for over 3 years and it has made my day-to-day work more efficient. It is easy for me to set up ML models and build data pipelines in a short amount of time. **What do you dislike about Dataiku?** I think that Dataiku could develop a stronger online forum for users to come and share about their experiences using the platform. **What problems is Dataiku solving and how is that benefiting you?** Dataiku accelerates ML and data processing work. This helps me directly by speeding up the time-to-delivery of the models I build. ### 48. Great website and great platform!! **Rating:** 4.5/5.0 stars **Reviewed by:** Federico B. | IT manager, Packaging and Containers, Mid-Market (51-1000 emp.) **Reviewed Date:** August 22, 2025 **What do you like best about Dataiku?** It brings together data people analysts, engineers, scientists on one platform **What do you dislike about Dataiku?** honestly, is that it can feel a bit heavy and slow, especially on large projects with a lot of visual recipes or datasets. **What problems is Dataiku solving and how is that benefiting you?** Collaboration gaps, i think it brings data scientists, analysts, engineers, and business users into one shared workspace ### 49. Reduction of time to value **Rating:** 3.5/5.0 stars **Reviewed by:** Logan S. | Data Scientist, Enterprise (> 1000 emp.) **Reviewed Date:** April 24, 2025 **What do you like best about Dataiku?** Visual layout makes data transformation more clear. Built in tools speed up development time in particular with tools like the LLM recipes and ML model "battle" **What do you dislike about Dataiku?** I have noticed inconsistencies with how code is executed in a python notebook vs a python recipe. Sometimes that works in a recipe doesn't work in a notebook. Refusal to add global dark mode. **What problems is Dataiku solving and how is that benefiting you?** Simplifying the use and deployment of LLM projects in friendly interface. **Official Response from Taylor MECHAM:** > We're glad to hear that you find the visual layout and built-in tools helpful for data transformation and improved development time. We appreciate your feedback about the inconsistencies with code execution between Python notebooks and recipes—we'll share this with our team for further review and improvement. ### 50. Transformative Experience for a new omer **Rating:** 5.0/5.0 stars **Reviewed by:** Christopher B. | Data Analyst, Enterprise (> 1000 emp.) **Reviewed Date:** April 24, 2025 **What do you like best about Dataiku?** As a newcomer to the field of Data Analytics, Dataiku’s no code approach allowed me to hit the ground running in a my new role and then as I got more comfortable with code, I’ve been able to really harness the power of Dataiku. **What do you dislike about Dataiku?** There can be a larger learning curve for those unfamiliar with data structures. **What problems is Dataiku solving and how is that benefiting you?** Automating large and small data processes in a fast and efficient manner . ## Dataiku Discussions - [Can I securely work on my sensitive data?](https://www.g2.com/discussions/can-i-securely-work-on-my-sensitive-data) - 2 comments, 2 upvotes - [What data visualization and reporting methods do you support?](https://www.g2.com/discussions/data-visualization) - 2 comments, 1 upvote - [What machine learning algorithms do you support?](https://www.g2.com/discussions/techical-specifications-5dafbb22-fb53-40e6-99ca-920a3000c257) - 1 comment, 1 upvote - [What programming languages do you support?](https://www.g2.com/discussions/techical-specifications-168d61c9-5165-4b3f-bded-0167b92ff8ed) - 1 comment, 1 upvote - [What data wrangling techniques do you support?](https://www.g2.com/discussions/techical-specifications-ab5002f3-fd5f-4acb-8fbd-280592800a16) - 1 comment, 1 upvote - [View Dataiku pricing details and edition comparison](https://www.g2.com/products/dataiku/reviews?section=pricing&secure%5Bexpires_at%5D=2026-05-14+20%3A20%3A58+-0500&secure%5Bsession_id%5D=3d205a04-101d-4725-94fb-9ba730cf45a1&secure%5Btoken%5D=998d4a336efc193a666d43463c3adef734bda33aa1930cf80660055680d08597&format=llm_user) ## Dataiku Integrations - [AWS Cloud Development Kit (AWS CDK)](https://www.g2.com/products/aws-cloud-development-kit-aws-cdk/reviews) - [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews) - [KaTe GCP Adapter for SAP PO](https://www.g2.com/products/kate-gcp-adapter-for-sap-po/reviews) - [Microsoft SharePoint](https://www.g2.com/products/microsoft-sharepoint/reviews) - [Snowflake](https://www.g2.com/products/snowflake/reviews) ## Dataiku Features **Reports** - Reports Interface - Steps to Answer - Graphs and Charts - Score Cards - Dashboards **Deployment** - Language Flexibility - Framework Flexibility - Versioning - Ease of Deployment - Scalability **System** - Data Ingestion & Wrangling **Data Preparation** - Connectors - Data Governance **Responses** - Personalization - Route To Human - Natural Language Understanding (NLU) **Deployment** - Language Flexibility - Framework Flexibility - Versioning - Ease of Deployment - Scalability **Scalability and Performance - Generative AI Infrastructure** - AI High Availability - AI Model Training Scalability - AI Inference Speed **Automation - AI Agents** - Sales Follow-Up - Customer Interaction Automation - Lead Generation - Document Processing - Feedback Collection **Integration - Machine Learning** - Integration **Prompt Engineering - Large Language Model Operationalization (LLMOps) ** - Prompt Optimization Tools - Template Library **Inference Optimization - Large Language Model Operationalization (LLMOps)** - Batch Processing Support **Agentic AI - AWS Marketplace** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration **Workflow Design & Integration - AI Orchestration** - Dependency Management - Workflow Coordination - Multi-Provider API Connectivity - Multi-Step Workflow Creation - Enterprise System Integration - Real-Time Data Pipelines **Data Ingestion & Preparation - Low-Code Machine Learning Platforms** - Automatic Data Profiling & Quality Assessment - Multi‑Source Connector Support - Schema Drift / Change Detection **Statistical Tool** - Scripting - Data Mining - Algorithms **Model Development** - Language Support - Drag and Drop - Pre-Built Algorithms - Model Training **Data Transformation** - Real-Time Analytics - Data Querying **Management** - Cataloging - Monitoring - Governing - Model Registry **Model Development** - Feature Engineering **Data Modeling and Blending** - Data Querying - Data Filtering - Data Blending **Platform** - Conversation Editor - Integration - Human-In-The-Loop **Operations** - Metrics - Infrastructure management - Collaboration **Cost and Efficiency - Generative AI Infrastructure** - AI Cost per API Call - AI Resource Allocation Flexibility - AI Energy Efficiency **Autonomy - AI Agents** - Independent Decision Making - Adaptive Responses - Task Execution - Problem Solving **Learning - Machine Learning** - Training Data - Actionable Insights - Algorithm **Model Garden - Large Language Model Operationalization (LLMOps)** - Model Comparison Dashboard **Performance Optimization & Analytics - AI Orchestration** - Workflow Performance Dashboards - Workflow Reporting - Resource Utilization Monitoring - Computational Resource Management - Dynamic Scaling - Component Monitoring **Model Construction & Automation - Low-Code Machine Learning Platforms** - Guided Algorithm & Hyperparameter Recommendation - Code Extensibility - Automated Feature Engineering **Data Analysis** - Analysis - Data Interaction **Machine/Deep Learning Services** - Computer Vision - Natural Language Processing - Natural Language Generation - Artificial Neural Networks **Machine/Deep Learning Services** - Natural Language Understanding - Deep Learning **Management** - Cataloging - Monitoring - Governing **Generative AI** - AI Text Generation - AI Text Summarization **Generative AI** - AI Text Generation - AI Text Summarization **Integration and Extensibility - Generative AI Infrastructure** - AI Multi-cloud Support - AI Data Pipeline Integration - AI API Support and Flexibility **Custom Training - Large Language Model Operationalization (LLMOps)** - Fine-Tuning Interface **Governance & Compliance Controls - AI Orchestration** - Regulatory Compliance - Governance Policy Enforcement - Role-Based Access Control - Audit Trail Management - Security Protocols **Agentic AI - AI Agents** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Natural Language Interaction - Proactive Assistance - Decision Making **Decision Making** - Modeling - Data Visualizations - Report Generation - Data Unification **Deployment** - Managed Service - Application - Scalability **Connectivity** - Hadoop Integration - Spark Integration - Multi-Source Analysis - Data Lake **Generative AI** - AI Text Generation - AI Text Summarization **Security and Compliance - Generative AI Infrastructure** - AI GDPR and Regulatory Compliance - AI Role-based Access Control - AI Data Encryption **Application Development - Large Language Model Operationalization (LLMOps) ** - SDK & API Integrations **Agentic AI - Analytics Platforms** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Natural Language Interaction - Proactive Assistance - Decision Making **Self Service ** - Calculated Fields - Data Column Filtering - Data Discovery - Search - Collaboration / Workflow - Automodeling **Operations** - Data Visualization - Data Workflow - Governed Discovery - Embedded Analytics - Notebooks **Generative AI** - AI Text Generation - AI Text Summarization **Generative AI** - AI Text Generation - AI Text Summarization - AI Text-to-Image **Usability and Support - Generative AI Infrastructure** - AI Documentation Quality - AI Community Activity **Model Deployment - Large Language Model Operationalization (LLMOps) ** - One-Click Deployment - Scalability Management **Deployment & Integration - Analytics Platforms** - No-code Dashboard Builder - Report Scheduling and Automation - Embedded Analytics and White-labeling - Data Source Connectivity **Advanced Analytics** - Predictive Analytics - Data Visualization - Big Data Services **Guardrails - Large Language Model Operationalization (LLMOps)** - Content Moderation Rules - Policy Compliance Checker **Agentic AI - Data Science and Machine Learning Platforms** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Natural Language Interaction - Proactive Assistance - Decision Making **Performance & Scalability - Analytics Platforms** - Large data handling and Query Speed - Concurrent User Support **Model Monitoring - Large Language Model Operationalization (LLMOps)** - Drift Detection Alerts - Real-Time Performance Metrics **Advanced Analytics & Modeling - Analytics Platforms** - Data Modeling and Governance - Notebook and Script Integration - Built-in Predictive and Statistical Models **Security - Large Language Model Operationalization (LLMOps)** - Data Encryption Tools - Access Control Management **Agentic AI Capabilities - Analytics Platforms** - Auto-generated Insights and Narratives - Natural Language Queries - Proactive KPI Monitoring and Alerts - AI Agents for Analytical Follow-ups **Gateways & Routers - Large Language Model Operationalization (LLMOps)** - Request Routing Optimization **Personalized Intelligence - Analytics Platforms** - Behavioral Learning for Contextual Query Refinement - Role-based Insight Personalization - Conversational and Prompt-based Analytics **Building Reports** - Data Transformation - Data Modeling - WYSIWYG Report Design - Integration APIs **Platform** - Customization - User, Role, and Access Management - Internationalization - Sandbox / Test Environments - Performance and Reliability - Breadth of Partner Applications ## Top Dataiku Alternatives - [Alteryx](https://www.g2.com/products/alteryx/reviews) - 4.6/5.0 (651 reviews) - [Gemini Enterprise Agent Platform](https://www.g2.com/products/gemini-enterprise-agent-platform/reviews) - 4.3/5.0 (647 reviews) - [Altair AI Studio](https://www.g2.com/products/rapidminer-studio/reviews) - 4.6/5.0 (490 reviews)