---
title: Dataiku Reviews
meta_title: 'Dataiku Reviews 2026: Details, Pricing, & Features | G2'
meta_description: Filter 215 reviews by the users' company size, role or industry
  to find out how Dataiku works for a business like yours.
aggregate_rating:
  rating_value: 4.4
  review_count: 215
  scale: '5'
date_modified: '2026-06-22'
parent_category:
  name: Generative AI
  url: https://www.g2.com/categories/generative-ai
---

# 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:** 215
## About Dataiku
Dataiku is the Platform for AI Success: the AI orchestration layer where enterprises build, deploy, and govern analytics, models, and agents at scale. It sits on top of the data platforms, clouds, and AI services you already use, working across all of them without locking you into any one. Dataiku expands who can build production AI, putting the right tools in the hands of data scientists and domain experts alike, from fraud analysts to demand planners. It orchestrates machine learning, rules, LLMs, and agents as one governed system, built on more than a decade of running production AI. Governance is part of the build rather than something bolted on afterward, so teams ship faster while keeping performance, cost, and risk under control. The result: AI that moves from experimentation to trusted, measurable execution now, not in 18 months.



## Dataiku Pros & Cons
**What users like:**

- Users love the **ease of use** in Dataiku, simplifying ML development and streamlining server data integration. (82 reviews)
- Users appreciate the **ease of learning and diverse training resources** offered by Dataiku, enhancing their ML development experience. (82 reviews)
- Users appreciate the **user-friendly interface** of Dataiku, enhancing collaboration and simplifying data projects for all team members. (46 reviews)
- Users appreciate the **easy integrations** with various data sources, enhancing flexibility and workflow simplicity in Dataiku. (43 reviews)
- Users value the **productivity improvement** from Dataiku's intuitive platform, streamlining the entire data science lifecycle efficiently. (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 a **steep learning curve** in Dataiku, as advanced features require deeper technical knowledge and troubleshooting skills. (45 reviews)
- Users find the **steep learning curve** challenging for beginners, making initial adoption of Dataiku difficult. (26 reviews)
- Users experience **slow performance** when executing large datasets, complicating analysis and increasing resource demands on Dataiku. (24 reviews)
- Users find **difficult learning** curves with Dataiku, especially when trying to master advanced features and cloud integration. (23 reviews)
- Users highlight the **expensive pricing structure** as a barrier for smaller organizations and teams seeking accessibility. (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. From idea to model in minutes: Dataiku accelerates the team's work

**Rating:** 4.5/5.0 stars

**Reviewed by:** Bill C. | Group Operations Manager, Enterprise (> 1000 emp.)

**Reviewed Date:** June 16, 2026

**What do you like best about Dataiku?**

The best thing about Dataiku is how easy it is to go from an idea to a working model. I can clean the data, create variables, test models, and deploy them without having to switch tools. Additionally, it keeps everything well organized and helps the team move faster.

**What do you dislike about Dataiku?**

What I like least about Dataiku is that some heavier workflows can feel slow, especially when several users are working simultaneously with large datasets. Additionally, some functions require more clicks than I expected to get to what I need. It's nothing critical, but there is room to simplify and streamline the experience.

**What problems is Dataiku solving and how is that benefiting you?**

Dataiku helps us solve the problem of having data scattered across different tools and teams. It centralizes our workflows, reduces manual work, and greatly facilitates collaboration. By automating repetitive tasks and providing us with a consistent way to prepare data, analyze it, and deploy models, it accelerates our projects and improves the quality of our decisions.

  ### 2. Streamlined Data Management with Stellar Support

**Rating:** 5.0/5.0 stars

**Reviewed by:** Alec P. | Data Science Capstone, Enterprise (> 1000 emp.)

**Reviewed Date:** June 19, 2026

**What do you like best about Dataiku?**

I really like Dataiku's graphical interface. I'm a huge fan of that visual flow showing how joins happen and where data is moving. As a visual person, it helps me get a better map of our complex projects, making it easy to understand what I'm doing and where I'm going. I think the ease of setup was impressive too. I don't recall the implementation being a headache at all; it was pretty straightforward connecting to our Databricks. We love how easy it is to work with different data types in Dataiku. I also enjoy cleaning data there when I get the chance, even though I'm often in Databricks. We love how much our team enjoys using Dataiku, and we're really happy customers. The tag-ups and consulting services have always been really on point and helpful for us. Due to the enthusiasm at Santee Cooper, we've expanded our Dataiku licenses significantly.

**What do you dislike about Dataiku?**

Sometimes working with your own custom code can be challenging. I will hit weird runtime errors when trying to run scripts I wrote.

**What problems is Dataiku solving and how is that benefiting you?**

Dataiku solves our shadow IT problem by allowing us to control and curate data access with role-based permissions. It uses our centralized hub (Databricks) and allows us to track data lineage and output validation.

  ### 3. Versatile Tool with Exceptional VisualML Capabilities

**Rating:** 5.0/5.0 stars

**Reviewed by:** Diego G.

**Reviewed Date:** June 19, 2026

**What do you like best about Dataiku?**

I really appreciate Dataiku for its VisualML feature, which lets me compare and evaluate multiple ML models simultaneously without having to set everything up from scratch in code. I also like that it doesn't restrict me to only low-code tools; when I need more control, I can use Python recipes to customize the logic, features, and apply specific modeling approaches. This flexibility was particularly useful in a recent project where I needed a custom-based model for a GLM. Dataiku allowed me to explore and compare using the visual components while giving me the freedom to incorporate Python recipes. The ability to experiment with different approaches, models, features, and metrics saves me a lot of time.

**What do you dislike about Dataiku?**

I would say it could improve in advanced model customization, debugging, and maybe making complex mixed visual/code workflows easier to manage. When I mix visual workflows with Python recipes, plus MLFlow, it's a bit hard to debug or keep track on everything. Maybe a better integration between VisualML and custom code, easier custom metrics, more advanced parameter control, or stronger logging for Python recipes. Better tools to compare inputs and outputs across workflows.

**What problems is Dataiku solving and how is that benefiting you?**

Dataiku helps me organize raw data into ML workflows visually, speeding up feature preparation and model building. VisualML lets me compare multiple models without coding, saving time. It combines low-code tools with Python for flexibility, supporting custom approaches when needed.

  ### 4. Centralized Data Prep and Collaboration in One Powerful Platform

**Rating:** 4.5/5.0 stars

**Reviewed by:** Luciana S. | IT Manager , Health, Wellness and Fitness, Mid-Market (51-1000 emp.)

**Reviewed Date:** June 12, 2026

**What do you like best about Dataiku?**

Dataiku makes us appreciate the centralized data preparation capabilities, governance, deployment, machine learning and analytics , all in a single platform 
The program allows all stakeholders, from analysts, data scientists, engineers and information users to collaborate  and work together 
Dataiku is non technical, and supports both coders and non technical teams, making the business workflow efficient 
The program has a fast development cycle, and it allows faster data exploration for efficient operations 
The interface for Dataiku is straightforward and it makes complex data projects to be easily executed
We experience seamless data visualization, something that explains in details users about data structures and analytics

**What do you dislike about Dataiku?**

Dataiku gets complicated, more so when users explore advanced features and this overwhelms them 
There is slow performance from Dataiku, more so when handling larger projects 
Dataiku is expensive and enterprise focused, something that affects small organizations

**What problems is Dataiku solving and how is that benefiting you?**

Dataiku ensures the data fragmentation issues have been resolved, centralizing data preparation, monitoring and analytics for accurate reporting 
The software supports users to transform, clean, and prepare database for improved performance 
There is brilliant collaboration with all company stakeholders, from analysts, data scientists, business users, among others
The program foster the concept of machine learning, which gives remarkable visual tools and deployment features 
There is efficient model governance from the software, compliance management and this makes all business processes more effective 
Dataiku helps our company in analytics initiatives and Scale AL and no additional technical burdens 
The software streamlines advanced data analytics, where even low code users faces no operational challenges

  ### 5. 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.

  ### 6. A powerful central hub for our data science and analytics teams

**Rating:** 5.0/5.0 stars

**Reviewed by:** Lokesh S. | Senior Data Scientist, Mid-Market (51-1000 emp.)

**Reviewed Date:** June 02, 2026

**What do you like best about Dataiku?**

In our mid-sized company, we use Dataiku as the central hub for all our machine learning and advanced analytics projects. Before this, our data workflows were a mess of isolated Python scripts and disconnected spreadsheets. Now, we use it to build end-to-end pipelines, specifically focusing on customer churn prediction models and automated inventory forecasting. It acts as the bridge where our data engineering, data science, and business analytics teams actually collaborate on the same exact projects without stepping on each other's toes.As a senior data scientist, the biggest win for me is the flexibility between visual tools and actual coding. I do not want to be boxed into a pure drag-and-drop interface, and Dataiku gets that. I can dive into a code recipe and write custom Python or SQL when I need to do something highly complex, while our business analysts can use the visual recipes to handle basic data joining and cleaning. This hybrid approach saves me countless hours of doing repetitive data prep. I also really appreciate how straightforward it makes model deployment. Pushing a model into production used to require a massive handoff meeting with IT, but now we can package and deploy our models with just a few clicks, making our iteration cycles much faster.

**What do you dislike about Dataiku?**

Despite being marketed heavily as a tool for everyone, the learning curve for completely non-technical users is still pretty steep. When I onboard new business analysts, it takes them a solid few weeks to truly get comfortable navigating the flow and understanding the logic of the visual recipes. Another frustration is that the version control, while functional, feels a bit clunky and restrictive if you are used to a traditional Git workflow. Lastly, when you are dealing with very heavy, complex flows with dozens of datasets, the visual interface can sometimes lag or feel a bit cluttered to navigate.

**What problems is Dataiku solving and how is that benefiting you?**

The main problem Dataiku solved for us was the massive bottleneck between the data science team and the business stakeholders. We used to build predictive models, like our churn risk scorer, and then struggle to make those insights easily accessible to the sales team on a daily basis. By keeping everything in Dataiku, we automated the entire pipeline. Now, the model scores new data overnight, and the sales team can just check a built-in dashboard every morning to see which accounts need immediate attention. It took us out of the business of manually running scripts and sending CSVs, freeing up my team to actually focus on researching and building better models.

  ### 7. Dataiku Speeds Up Repeatable Marketing Data Workflows

**Rating:** 4.5/5.0 stars

**Reviewed by:** Michele C. | Marketing Consultant, Small-Business (50 or fewer emp.)

**Reviewed Date:** May 22, 2026

**What do you like best about Dataiku?**

What I like most about Dataiku is how much faster it lets me move on marketing data projects for clients. In my day-to-day work as a digital marketing consultant, I often have to bring together data from multiple sources—CRM exports, campaign performance reports, website analytics, sales data, and sometimes offline datasets. Dataiku gives me a structured environment to clean, connect, and analyze everything in one place, without having to rebuild the entire process from scratch each time.

I use the visual workflows regularly because they make the end-to-end process far more transparent. Rather than working only in spreadsheets or in isolated scripts, I can see every step of the data preparation flow and explain it clearly to clients or internal teams. This is especially helpful when I need to show exactly how a lead scoring model, a customer segmentation analysis, or a campaign performance dataset was created.

Another thing I really value is the balance between no-code and code options. For everyday consulting work, it’s practical: I can move quickly with visual recipes for common tasks, and then go deeper with SQL or Python when the analysis needs more flexibility. That saves time and makes it easier to adapt the workflow to the complexity of each project.

Dataiku also improves collaboration with non-technical stakeholders. When I’m working with marketing managers or sales teams, they don’t always need the technical details, but they do need to trust the output. Having a clear, documented workflow makes conversations smoother and helps translate analysis into concrete marketing decisions.

Overall, the biggest benefit for me is that Dataiku turns complex data preparation and analysis into a repeatable consulting workflow. It helps me spend less time on manual data cleaning and more time interpreting results, spotting opportunities, and recommending actions to improve campaign performance, customer targeting, and ROI.

**What do you dislike about Dataiku?**

What I dislike about Dataiku is that it can feel a bit heavy at the beginning, especially if the team is not already familiar with data workflows, data preparation logic or machine learning concepts. As a consultant, I can usually navigate the platform quite well, but when I involve clients or marketing teams who are less technical, there is sometimes a learning curve before they feel comfortable using it independently.

The pricing can also be a limitation, especially for smaller clients or companies that are still at an early stage in their data maturity. Dataiku can deliver strong value when it is used regularly across multiple projects, teams and data sources, but for a smaller marketing team that only needs occasional analysis, it may feel like a significant investment. The ROI is much clearer when the company is ready to operationalize data workflows, not just run one-off reports.

In terms of onboarding, I think the platform requires a structured introduction to get the most out of it. There are many features, which is a strength, but it can also be overwhelming at first. For some clients, I need to spend extra time explaining not only how the tool works, but also how to think in terms of reusable data pipelines instead of simple spreadsheet-based analysis.

Regarding AI and machine learning, the capabilities are powerful, but they still require good data quality and a clear business objective. Dataiku can help a lot with automation and predictive models, but it does not replace the strategic work of defining the right question, selecting the right variables and interpreting the results correctly. In my daily work, I still need to guide clients carefully so they do not treat AI outputs as automatic answers without proper validation.

So overall, my main dislike is not about a single missing feature, but about the complexity that comes with such a complete platform. It is very useful, but it needs the right level of adoption, training and business commitment to fully justify the investment.

**What problems is Dataiku solving and how is that benefiting you?**

Dataiku helps me solve one of the main problems I face as a digital marketing consultant: fragmented data. In many client projects, marketing data is spread across different platforms, spreadsheets, CRM systems, advertising accounts and analytics tools. Before using a structured platform like Dataiku, a lot of time could be lost just cleaning files, matching columns, checking inconsistencies and preparing the data before any real analysis could begin.

With Dataiku, I can create more repeatable workflows for tasks like campaign performance analysis, customer segmentation, lead scoring, churn analysis and ROI reporting. This benefits me because I do not have to start from zero every time a client sends updated data. Once the workflow is built, I can refresh the inputs, review the outputs and focus more on insights and recommendations.

It also helps me reduce manual errors. When working only with spreadsheets, it is easy to lose track of formulas, versions or manual changes. Dataiku makes the process more structured and transparent, so I can better control how the data is transformed and explain the logic behind the final results to clients.

Another important benefit is that it helps me move from simple reporting to more advanced decision support. Instead of only showing what happened in a campaign, I can help clients understand patterns, identify high-value segments, predict possible outcomes and prioritize marketing actions more effectively.

Overall, Dataiku saves time, improves the reliability of my analysis and helps me deliver more strategic value. It allows me to spend less energy on repetitive data preparation and more time advising clients on what to do next.

  ### 8. 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.

  ### 9. Parameter Analyzer Tool Makes Root Cause Analysis Faster

**Rating:** 5.0/5.0 stars

**Reviewed by:** Wesley H. | Site Data Analyst, Mid-Market (51-1000 emp.)

**Reviewed Date:** June 18, 2026

**What do you like best about Dataiku?**

I love the parameter analyzer tool, we use it as a first step in problem solving to help narrow down root cause analysis

**What do you dislike about Dataiku?**

I would like there to be a global env for secrets, and more inter connectivity between separate instances. If you needed to use something on one instance on another instance it’s a pain to use the api to attempt to retrieve secrets

**What problems is Dataiku solving and how is that benefiting you?**

It streamlines pipeline development. As an analyst, I can build my own pipelines and accelerate the delivery of reports through dashboards or web apps.

  ### 10. 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.

  ### 11. Superb Tool for Data Governance and AI Success

**Rating:** 5.0/5.0 stars

**Reviewed by:** Aparna S. | Mid-Market (51-1000 emp.)

**Reviewed Date:** June 17, 2026

**What do you like best about Dataiku?**

I really appreciate the AI success with Dataiku. It's something I'm genuinely interested in, and it has been a great experience. The tool resolved our data governance issues, made our AI projects successful, and its orchestration along with people integration into our data systems has been great. The initial setup was pretty easy, thanks to a support team that helped us. Overall, my experience with Dataiku has been very positive, and I would definitely recommend it.

**What do you dislike about Dataiku?**

I think we can add agent tech and expand it to white coding. Also, adding some data intelligence BI would be beneficial.

**What problems is Dataiku solving and how is that benefiting you?**

Dataiku resolved our data governance issues and facilitated successful AI integration. It improved orchestration and integration across all our data systems.

  ### 12. 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.

  ### 13. User-Friendly Dataiku with Outstanding Learning Resources

**Rating:** 5.0/5.0 stars

**Reviewed by:** Beau E. | Enterprise (> 1000 emp.)

**Reviewed Date:** June 17, 2026

**What do you like best about Dataiku?**

I find Dataiku to be incredibly user-friendly, which is essential for my work in building apps and cleaning data. I really enjoy the academy because the lessons are so easy to follow, making learning a breeze. The web apps are a fantastic feature for me too; I love that I can create apps without needing to purchase third-party software. The initial setup for our team was also very easy and only took a few months.

**What do you dislike about Dataiku?**

Needs a dark mode

**What problems is Dataiku solving and how is that benefiting you?**

Dataiku brings many inputs together from many places, making data cleaning and app building easier. It's user-friendly, and the academy lessons are easy to follow. I love creating apps without needing 3rd party software.

  ### 14. 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.

  ### 15. Streamlined Data Transformation with Ease

**Rating:** 4.0/5.0 stars

**Reviewed by:** Khalid S. | Enterprise Data Architect, Enterprise (> 1000 emp.)

**Reviewed Date:** June 01, 2026

**What do you like best about Dataiku?**

I like how Dataiku helps us identify some data quality issues and correct them before presentation. It's really important because, without data quality, the data is almost worthless, and you can't really make any insight out of it if it's not good quality. I also found the setup process to be very easy. We integrated with our intra authentication system, set up connections to Snowflake, and then started working on it.

**What do you dislike about Dataiku?**

It's the ability to actually be able to extract data, look at it, evaluate, transform it, and present it.

**What problems is Dataiku solving and how is that benefiting you?**

I use Dataiku for data transformation, making it easy to manipulate and present data. It helps us identify and correct data quality issues before presentation.

  ### 16. Powerful Collaboration, but Can Feel Bulky and Resource-Heavy

**Rating:** 3.0/5.0 stars

**Reviewed by:** Verified User in Information Technology and Services | Mid-Market (51-1000 emp.)

**Reviewed Date:** May 19, 2026

**What do you like best about Dataiku?**

What I like most about Dataiku is the way it balances ease of use with strong analytics and AI capabilities. It allows business users, analysts, and data scientists to collaborate smoothly within the same platform. The combination of visual workflows and the flexibility to code when needed makes it straightforward to go from experimentation to production more quickly.

**What do you dislike about Dataiku?**

One thing I dislike about Dataiku is that it can feel bulky and resource-heavy at times.
It tends to save/cache data in many places, which can make workflows harder to manage and slower to navigate.
For hardcore technical teams that prefer lightweight, code-first tooling, it may feel slower and more restrictive than working directly with native engineering stacks.

**What problems is Dataiku solving and how is that benefiting you?**

Dataiku solves the problem of fragmented data, disconnected teams, and slow AI deployment by bringing data prep, analytics, and machine learning into one platform.
It benefits me by making collaboration between business and technical teams much smoother and reducing the time needed to move projects from experimentation to production.

  ### 17. Powerful Governance, Needs Better UI Integration

**Rating:** 4.0/5.0 stars

**Reviewed by:** Samridhi G. | Cybersecurity Specialist, Enterprise (> 1000 emp.)

**Reviewed Date:** June 16, 2026

**What do you like best about Dataiku?**

I really like the Orchestrator level, as it helps with my tasks. The UI is another aspect that I find valuable, especially for various use cases. Dataiku is also helpful in AI governance, agent traces, and token aspects. Additionally, I appreciate the model evaluation and prompt monitoring features, which make my workflow more efficient.

**What do you dislike about Dataiku?**

The UI can be improved with more integration.

**What problems is Dataiku solving and how is that benefiting you?**

I use Dataiku for AI governance, agent traces, model evaluation, and prompt monitoring. It's great for orchestrator-level tasks, but the UI could be improved with more integration.

  ### 18. 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

  ### 19. 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.

  ### 20. Easy Low-Code Workflow Creation That Just Works

**Rating:** 4.0/5.0 stars

**Reviewed by:** Brad S. | Business Intelligence Analyst, Enterprise (> 1000 emp.)

**Reviewed Date:** June 18, 2026

**What do you like best about Dataiku?**

The low code environment and workflow creation plus ease of use

**What do you dislike about Dataiku?**

There nothing o can think of that I dislike

**What problems is Dataiku solving and how is that benefiting you?**

Dataiku has helped us reduce the dependency on technical teams for analytics and data preparation. Business users can access, prepare, and analyze data more efficiently, which has accelerated decision-making and improved collaboration across departments. The platform’s governance, automation, and AI capabilities have also helped us scale data initiatives while maintaining consistency and control.

  ### 21. 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.

  ### 22. Visual Flow Ease, Coding Pilot Needs Work

**Rating:** 3.5/5.0 stars

**Reviewed by:** Verified User

**Reviewed Date:** June 17, 2026

**What do you like best about Dataiku?**

I really like probably seeing the flow visually with Dataiku. It's helpful having the visual recipes too if I don't wanna write code.

**What do you dislike about Dataiku?**

The coding pilot doesn't work for me. I don't know if that's just a Boeing restriction that we don't have it activated. So I have to go to a third party AI tool if I wanna use the coding agent.

**What problems is Dataiku solving and how is that benefiting you?**

I use Dataiku to build a part recommender pipeline, and it solves the problem of explaining the code easily to customers because of its visual flow.

  ### 23. Single Pane of Glass for Data & AI Agents with Built-In Observability

**Rating:** 4.0/5.0 stars

**Reviewed by:** Chandrakanth K. | Lead Advanced Data Analytics, Enterprise (> 1000 emp.)

**Reviewed Date:** June 18, 2026

**What do you like best about Dataiku?**

Having a single pane of glass for data and AI agents, with built-in observability, makes it much easier to monitor everything in one place.

**What do you dislike about Dataiku?**

It could be an overhead to maintain, especially if the customer is already migrating to Databricks.

**What problems is Dataiku solving and how is that benefiting you?**

We haven’t had the chance to explore the use cases yet.

  ### 24. 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.

  ### 25. User-Friendly Interface, Needs Better Handling of Unstructured Data

**Rating:** 3.5/5.0 stars

**Reviewed by:** Verified User | Small-Business (50 or fewer emp.)

**Reviewed Date:** June 16, 2026

**What do you like best about Dataiku?**

I appreciate Dataiku for its interface and ease of use, making the initial setup very easy. I also found the low code data pipeline setup beneficial when I used it in my past job.

**What do you dislike about Dataiku?**

Customized pipelines or use cases, like dealing with semi-structured or unstructured data, are hard to tackle.

**What problems is Dataiku solving and how is that benefiting you?**

I appreciated Dataiku for its low code data pipeline setup and ease of use, though dealing with semi-structured or unstructured data could be improved.

  ### 26. Easy for New Users, but Integrating with Existing Orchestration Tools Was Challenging

**Rating:** 3.0/5.0 stars

**Reviewed by:** Verified User in Airlines/Aviation | Small-Business (50 or fewer emp.)

**Reviewed Date:** June 16, 2026

**What do you like best about Dataiku?**

The interface allows people with little experience to get up to speed and to start to deliver quickly. Complex solutions and capabilities are there, but are also insulated from over whelmed new users.

**What do you dislike about Dataiku?**

The data iku environment is very self contained and works seamlessly with its own components, however we struggled when we wanted to integrate with some of our other existing orchestration tools.

**What problems is Dataiku solving and how is that benefiting you?**

Integration of our loyalty and booking patterns

  ### 27. 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.

  ### 28. 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

  ### 29. Powerful Automation, AI, and Governance for Smarter Data Cataloging

**Rating:** 4.5/5.0 stars

**Reviewed by:** Verified User in Pharmaceuticals | Mid-Market (51-1000 emp.)

**Reviewed Date:** June 01, 2026

**What do you like best about Dataiku?**

Automation, data, AI, cataloging, governance

**What do you dislike about Dataiku?**

Takes time for non tech user to learn and adapt. Help is not great. Documentation is basic.

**What problems is Dataiku solving and how is that benefiting you?**

Automation

  ### 30. Intuitive UI and Easy-to-Follow Recipes

**Rating:** 4.5/5.0 stars

**Reviewed by:** Verified User in Retail | Enterprise (> 1000 emp.)

**Reviewed Date:** June 19, 2026

**What do you like best about Dataiku?**

It’s very intuitive UI and recipes are easy to understand

**What do you dislike about Dataiku?**

Should be integrated with DG tools well so they can be easily visualized within

**What problems is Dataiku solving and how is that benefiting you?**

It’s creating a space for business users to play with the data before deploying for business use

  ### 31. 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.

  ### 32. 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

  ### 33. 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.

  ### 34. 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.

  ### 35. 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.

  ### 36. 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!

  ### 37. 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

  ### 38. 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.

  ### 39. 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.

  ### 40. 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.)

  ### 41. 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.

  ### 42. 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.

  ### 43. 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.

  ### 44. 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.

  ### 45. 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

  ### 46. 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.

  ### 47. 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.

  ### 48. 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.

  ### 49. 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

  ### 50. 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.


## 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=2026-06-22+13%3A57%3A56+-0500&secure%5Bsession_id%5D=e68b1142-b2ee-4b7e-bdf5-406de328267a&secure%5Btoken%5D=655770aef4ba521dc5a75e7765f31e357c9f13d855d3730ce80c84bd4fec76a6&format=llm_user)
## Dataiku Integrations
  - [Alation](https://www.g2.com/products/alation/reviews)
  - [Alteryx Designer Cloud](https://www.g2.com/products/alteryx-alteryx-designer-cloud/reviews)
  - [Amazon S3 Glacier](https://www.g2.com/products/amazon-s3-glacier/reviews)
  - [Anthropic SDK](https://www.g2.com/products/anthropic-sdk/reviews)
  - [AWS Bedrock](https://www.g2.com/products/aws-bedrock/reviews)
  - [AWS Cloud Development Kit (AWS CDK)](https://www.g2.com/products/aws-cloud-development-kit-aws-cdk/reviews)
  - [Azure](https://www.g2.com/products/hopem-azure/reviews)
  - [Azure Blob Storage](https://www.g2.com/products/azure-blob-storage/reviews)
  - [Databricks](https://www.g2.com/products/databricks/reviews)
  - [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews)
  - [Google Vertex AI SDK](https://www.g2.com/products/google-vertex-ai-sdk/reviews)
  - [KaTe GCP Adapter for SAP PO](https://www.g2.com/products/kate-gcp-adapter-for-sap-po/reviews)
  - [MySQL](https://www.g2.com/products/mysql/reviews)
  - [OpenAI SDK](https://www.g2.com/products/openai-sdk/reviews)
  - [PostgreSQL](https://www.g2.com/products/postgresql/reviews)
  - [RedShift](https://www.g2.com/products/redshift-redshift/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

**Customization - AI Agent Builders**
- Natural Language Configuration
- Tone Customization
- Security Guardrails

**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

**Functionality - AI Agent Builders**
- Omni-channel Support
- Agent Branding
- Proactive Response Capabilities
- Seamless Human Escalation

**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

**Data and Analytics - AI Agent Builders**
- Analytics & Reporting
- Contextual Awareness
- Data Privacy Compliance

**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

**Integration - AI Agent Builders**
- Workflow Automation
- API Usage
- Platform Interoperability
- CRM Data Integration

**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
  - [Gemini Enterprise Agent Platform](https://www.g2.com/products/gemini-enterprise-agent-platform/reviews) - 4.3/5.0 (652 reviews)
  - [Alteryx](https://www.g2.com/products/alteryx/reviews) - 4.6/5.0 (805 reviews)
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