--- title: Dataiku Reviews meta_title: 'Dataiku Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 224 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: 224 scale: '5' date_modified: '2026-08-10' 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:** 224 ## 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 appreciate how Dataiku facilitates **easy ML development** , allowing focus on building models without the complexity. (80 reviews) - Users love the **ease of use** in Dataiku, simplifying complex tasks and enhancing their data analysis experience. (79 reviews) - Users appreciate the **ease of usability** in Dataiku, enabling collaboration for both technical and non-technical users. (45 reviews) - Users appreciate the **easy integrations** of Dataiku, facilitating smooth collaboration and deployment across various analytics tools. (43 reviews) - Users benefit from the **productivity improvement** of Dataiku, enabling faster project development and enhanced career growth. (41 reviews) - Collaboration (40 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) - Time-Saving (38 reviews) **What users dislike:** - Users find the **steep learning curve** of Dataiku challenging, making it tough for beginners to master the platform. (43 reviews) - Users find the **steep learning curve** challenging for beginners, impacting their ability to effectively use Dataiku. (25 reviews) - Users find the **difficult learning** curve challenging, particularly for beginners navigating advanced features. (23 reviews) - Users experience **slow performance** with Dataiku when handling large datasets, affecting efficiency and productivity. (23 reviews) - Users find Dataiku **expensive** , especially for smaller organizations and projects, impacting accessibility and affordability. (22 reviews) - Complexity (20 reviews) - Users find the **complexity issues** in Dataiku's tools and documentation can hinder their overall experience. (19 reviews) - Users face **performance issues** that disrupt workflow, complicate data preparation, and limit access to plugins for testing. (18 reviews) - Data Management Issues (14 reviews) - Missing Features (14 reviews) ## Dataiku Reviews ### 1. 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. ### 2. 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. ### 3. 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. ### 4. 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. ### 5. 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. ### 6. 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. ### 7. 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.) ### 8. 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. ### 9. 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. ### 10. 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. ### 11. 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. ### 12. 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. ### 13. 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 ### 14. 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. ### 15. 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. ### 16. 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. ### 17. 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 ### 18. 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. ### 19. Emerging player in Data Science **Rating:** 4.5/5.0 stars **Reviewed by:** Sanket B. | Technical Lead, Mid-Market (51-1000 emp.) **Reviewed Date:** August 20, 2025 **What do you like best about Dataiku?** One of the best feature in Dataiku is No code feature which can help resources who are not comfortable in coding. It supports Python/R libraries and workflow playbook. **What do you dislike about Dataiku?** Still expensive solution for implementation. **What problems is Dataiku solving and how is that benefiting you?** Generally Dataiku helped us to build Data Science projects and predictive analysis to build some KPI's. ### 20. Accelerating Data Workflows with Dataiku **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Commercial Real Estate | Mid-Market (51-1000 emp.) **Reviewed Date:** April 24, 2025 **What do you like best about Dataiku?** It strikes a good balance between no-code workflows and also helps integrate code when required. It's a visual tool that handles the whole lifecycle of the data, it has the visual workflow style of KNIME and FME with the depth and flexibility of custom coding and cloud-based architecture. Great customer support by the way! It's also an intuitive platform to have new users onboarded on. **What do you dislike about Dataiku?** Performance can lag with large datasets, and partitioning isn't intuitive. Version controlling is great, but the roll back does not always work as intended especially within the coding environments. **What problems is Dataiku solving and how is that benefiting you?** Collaboration, versioning and non-local infrastructure. Those were the major pain-points that Dataiku alleviated. ### 21. Dataiku is a great low-code solution **Rating:** 4.0/5.0 stars **Reviewed by:** Josh B. | Sr Principal Analyst, People Analytics and Employee Experience, Enterprise (> 1000 emp.) **Reviewed Date:** April 17, 2025 **What do you like best about Dataiku?** I like how Dataiku supports low-code operations, but we can also create custom code in the environment and share it easily within our group. **What do you dislike about Dataiku?** Knowledge transfer for use of PromptStudio was a bit of a lift. The UI is a little less intuitive than other parts of Datiku **What problems is Dataiku solving and how is that benefiting you?** We have been using PromptStudio to test prompt ins multiple LLMs. Dataiku streamlines and simplifies this process once it is setup ### 22. Citizen data analyst **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Food Production | Small-Business (50 or fewer emp.) **Reviewed Date:** April 24, 2025 **What do you like best about Dataiku?** The learning curve was easy. The trainings are readily available to go back to frequently. The videos are short yet adequate for learning purposes. The sample projects being sectioned into different learning sections made it easy to isolate and learn one functionality at a time. The capability to use python recipes without prior knowledge of python. **What do you dislike about Dataiku?** Running into error message that sometimes are hard to figure out with limited debugging options that are free. **What problems is Dataiku solving and how is that benefiting you?** Automating repetitive tasks that are otherwise time consuming, labor intensive and prone to errors. Forecasting and predictive analysis. providing business teams with reliable forecasts to use for business planning. ### 23. Dataiku has changed my job for the better! **Rating:** 5.0/5.0 stars **Reviewed by:** Fernando d. | Senior Data Analyst, Enterprise (> 1000 emp.) **Reviewed Date:** April 24, 2025 **What do you like best about Dataiku?** Dataiku excels at providing a collaborative, end-to-end platform that bridges the gap between visual data preparation and advanced coding for diverse teams. **What do you dislike about Dataiku?** Dataiku Application could be better. The concept of instances and concurrent users sometimes can conflict. **What problems is Dataiku solving and how is that benefiting you?** My job is to translate manual data related processes into dataiku flows and integrations. ### 24. AI conference **Rating:** 5.0/5.0 stars **Reviewed by:** Pranav A. | Senior Data Scientist, Enterprise (> 1000 emp.) **Reviewed Date:** April 24, 2025 **What do you like best about Dataiku?** Dataiku provides a unified platform that covers a wide range of the data science lifecycle. Users appreciate being able to connect to data, prepare it, build and evaluate machine learning models (using both AutoML and code), deploy models, visualize results, and manage governance all within one environment. **What do you dislike about Dataiku?** The licensing cost is frequently mentioned as a significant drawback, potentially putting it out of reach for smaller organizations or teams. Some users feel there's a lack of suitable pricing tiers below the full enterprise offering. **What problems is Dataiku solving and how is that benefiting you?** Low code is the best ### 25. Dataiku for Advanced Analytics **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Financial Services | Enterprise (> 1000 emp.) **Reviewed Date:** March 24, 2025 **What do you like best about Dataiku?** Dataiku excels at creating a centralized platform for democratizing data for every user to collaborate regardless of their skill level. The platform is suitable for coders and non-coders, developers and business users, etc. Each release contains new Gen AI, machine learning or analytics capabilities and allows you to connect to source systems, build data workflows, interactive visualizations and automate the entire end-to-end life cycle. **What do you dislike about Dataiku?** I think the user documentation can be challenging to sift through, I wish information was collated within one repository instead of many different links. **What problems is Dataiku solving and how is that benefiting you?** Dataiku allows me to build end-to-end solutions leveraging big data at scale. ### 26. Great platform for practical EDA and ML purposes **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Consulting | Small-Business (50 or fewer emp.) **Reviewed Date:** March 24, 2025 **What do you like best about Dataiku?** You'd be hard pressed to create a platform with greater ease of use than Dataiku. Moving around the UI while performing data analytics or when showcasing the work performed by the team is seamless / effortless. New features also come out regularly and are constantly improving the platform. Ease of implementing the platform with other tools, such as Snowflake and Power BI, is also much appreciated. **What do you dislike about Dataiku?** Projects can get unruly once they become large enough. This can be alleviated by creating many flow zones. **What problems is Dataiku solving and how is that benefiting you?** Forecasting customer demand using data ingesting, prep, and time series analysis has been super valuable. ### 27. Review of Dataiku DSS **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Oil & Energy | Mid-Market (51-1000 emp.) **Reviewed Date:** April 24, 2025 **What do you like best about Dataiku?** Dataiku stands out for its user-friendly interface, collaboration features, seamless integration with various data sources, automation of repetitive tasks, and scalability for handling large datasets and complex workflows. It's a versatile tool suitable for both beginners and experienced data scientists, I primarily like it for citizen data scientists. **What do you dislike about Dataiku?** It would be more helpful if Dataiku had auto-scaling virtual machines as part of their infrastructure. This will minimize overhead as an administrator for the application. **What problems is Dataiku solving and how is that benefiting you?** Data preparation, data science, and limited ML ops cases ### 28. Easy and flexible tool for any type of user **Rating:** 4.5/5.0 stars **Reviewed by:** Krishna Prasad B. | Vice President, Enterprise (> 1000 emp.) **Reviewed Date:** April 24, 2025 **What do you like best about Dataiku?** Dataiku is a user-friendly and powerful data science platform that enables both technical and non-technical users to build, deploy, and manage AI projects collaboratively. With visual workflows, AutoML, and support for Python, R, and SQL, it balances simplicity with flexibility. Its strong integration, governance, and MLOps features make it ideal for scaling data initiatives across teams and industries. Great for accelerating data-driven decision-making. **What do you dislike about Dataiku?** None. Sometimes tool gets stuck in running complex jobs **What problems is Dataiku solving and how is that benefiting you?** Quick analytical insights to daily business decisions ### 29. Alteryx to Dataiku Convert **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Manufacturing | Enterprise (> 1000 emp.) **Reviewed Date:** April 24, 2025 **What do you like best about Dataiku?** Dataiku is very flexible and allows me to utilize fewer tools compared to Alteryx. This has allowed me to create smaller flows making them easier to follow. **What do you dislike about Dataiku?** It can be hard to find certain steps since there are many tools inside of one tool. **What problems is Dataiku solving and how is that benefiting you?** I work with a lot of data, especially dirty data and Dataiku allows me to manipulate the data I receive and turn it into something I can interpret. ### 30. Very user friendly **Rating:** 4.0/5.0 stars **Reviewed by:** Pratik M. | Data Strategy, Enterprise (> 1000 emp.) **Reviewed Date:** April 24, 2025 **What do you like best about Dataiku?** I think super friendly. And best part about it it’s all in one. You do not have to navigate to multiple applications to from building the model or creating a flow. **What do you dislike about Dataiku?** So far I don’t have any complaints about Dataiku it’s working fine for me. **What problems is Dataiku solving and how is that benefiting you?** It’s providing me a platform where I perform all the data curation as well as analysis of data in single platform. And provide insights to stakeholders. ### 31. Dataiku Makes Data Analytics Accessible to End Users **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Pharmaceuticals | Enterprise (> 1000 emp.) **Reviewed Date:** April 24, 2025 **What do you like best about Dataiku?** Dataiku’s vast connections to different data structures allows backend users to connect multiple end users’ data together to make meaningful connections for the end users through user friendly and customizable web apps. **What do you dislike about Dataiku?** Even though Dataiku is great in many aspects, understanding all that is has to offer takes some time and I am always finding new things I didn’t know it could do. Understanding how it can be combined with what we have already is challenging. **What problems is Dataiku solving and how is that benefiting you?** Being able to have technical and non technical users in the same space allowing for technical users to build in the same platform the end users can view. ### 32. Great tool for anyone **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Consulting | Enterprise (> 1000 emp.) **Reviewed Date:** April 24, 2025 **What do you like best about Dataiku?** You can do a lot with just drag-and-drop components, easy to use for people from non technical background. You can bring in your own models, connect to huge data sources, and even integrate with most of the tools. **What do you dislike about Dataiku?** For large datasets, processing might slow down if it's not well-optimized or errors in joins can lead to recipes running forever. Could prompt some warning to handle such cases **What problems is Dataiku solving and how is that benefiting you?** Dataiku solves the problem of siloed workflows by uniting data prep, modeling, and deployment in one platform. For me, that means I can prototype a model, share it with stakeholders, and automate retraining ### 33. Company with good use cases to support for AI **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Banking | Mid-Market (51-1000 emp.) **Reviewed Date:** April 24, 2025 **What do you like best about Dataiku?** Great AI shop for getting to learn about AI and get up to speed on integrating AI in your tech space. **What do you dislike about Dataiku?** Currently could not come across anything which could be disliked. **What problems is Dataiku solving and how is that benefiting you?** We are still in the early stages of integrating AI in the workplace. Dataiku is helping in understanding the different use cases in the IT space ### 34. A comprehensive/complete tool for data science **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Chemicals | Enterprise (> 1000 emp.) **Reviewed Date:** April 03, 2025 **What do you like best about Dataiku?** versatility: visual, low code, API integration (wiki, dashboards, flow, models) connectivity (Databricks) ease of use for basic applications immediate support **What do you dislike about Dataiku?** quite expensive subscription limitations in flow output (images) steep learning curve for specific projects **What problems is Dataiku solving and how is that benefiting you?** Creation Machine learning models for feature analysis and predictions Time series analysis and modeling easy implementation (hyperparameter, comparison models, visualizations) dokumentation (wiki) visibility/visualization via flow ### 35. Dataiku does it all - support for the full data pipeline. **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Consulting | Mid-Market (51-1000 emp.) **Reviewed Date:** April 02, 2025 **What do you like best about Dataiku?** Easy to switch between clicking and coding, plus support for SQL, Python, and R. The customer support is outstanding - they really know the product and are very responsive. **What do you dislike about Dataiku?** Can't move icons around on the screen - they are placed randomly and haphazardly. **What problems is Dataiku solving and how is that benefiting you?** Data orchestration, data analysis and ETL, collaboration with other teams. ### 36. User friendly interface for business and non-technical oriented people. **Rating:** 4.5/5.0 stars **Reviewed by:** Diane M. | data science, Mid-Market (51-1000 emp.) **Reviewed Date:** April 24, 2025 **What do you like best about Dataiku?** Has a friendly interface and SAAS which is easier to upgrade between version updates **What do you dislike about Dataiku?** It rund on top of database so it will always be a bit slower than running queries directly in database. **What problems is Dataiku solving and how is that benefiting you?** Enabaling data manipulation, transformation, modeling, predictive, prescriptive, etc. ### 37. Excellent tool **Rating:** 5.0/5.0 stars **Reviewed by:** Nicholas H. | Senior Analytics Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** April 24, 2025 **What do you like best about Dataiku?** Dataiku is a great way to integrate multiple analytics tools and skill sets, quickly ideas, and deploy solutions rapidly **What do you dislike about Dataiku?** Dashboaring capabilities could be improved; cost **What problems is Dataiku solving and how is that benefiting you?** Dataiku makes it easy to complete etl tasks and automate complex audit work ### 38. One-stop shop for all analytics and ML needs **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Financial Services | Enterprise (> 1000 emp.) **Reviewed Date:** April 24, 2025 **What do you like best about Dataiku?** I love how Dataiku allows for such seamless creation and implementation of ML models. It offers the ability to seamlessly collaborate and integrate with workflow with colleagues. **What do you dislike about Dataiku?** Dataiku has a decent learning curve if you are coming from a non-technical background. **What problems is Dataiku solving and how is that benefiting you?** Dataiku is allowing me to solve data analytics problems that maybe take a few minutes in Python in mere seconds. It saves me a considerable amount of time in the long run. ### 39. Great and business user friendly tool. **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Financial Services | Enterprise (> 1000 emp.) **Reviewed Date:** April 24, 2025 **What do you like best about Dataiku?** I love dataiku cause it is a business user friendly tool that people who don’t know about sql can easily build their flows and get output for further analysis **What do you dislike about Dataiku?** Wish dataiku can have a better integration with cloud, so it can have more computing power **What problems is Dataiku solving and how is that benefiting you?** We use dataiku for reporting. It is a great tool to use for joining data. ### 40. Great platform for AI projects **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Government Relations | Enterprise (> 1000 emp.) **Reviewed Date:** April 24, 2025 **What do you like best about Dataiku?** Very easy to use and great layout. You can connect to various data sources. Good for integration with other platforms. **What do you dislike about Dataiku?** There is nothing that I dislike about it. **What problems is Dataiku solving and how is that benefiting you?** Great for going through document and summarize the content for the user to interrogate the documents and produce reports and make decisions. ### 41. Robust tool for data science engineer **Rating:** 5.0/5.0 stars **Reviewed by:** Kuldeep S. | App/Cloud Specialist, Enterprise (> 1000 emp.) **Reviewed Date:** September 28, 2023 **What do you like best about Dataiku?** Frequently Use: this tool we use very frequently for data analysis and optimise an d filtering. Easy to use: this tool is easy to use. It's very user friendly Easy to integration: this tool is easy to integration with other platforms. So that we could collaborate with other team. No of features: there are no of features .like 1.robust data integration 2. scalability 3. Visual data preparation 4.modelmonitering Etc Easy to implementation: dataiku dss is easy to implementation Customer support: this is very good customer support. **What do you dislike about Dataiku?** Although this tool is very easy to use nd user friendly but some people who are new in data science get some challenging to use. Cost: cost is also high .this is not suitable for small organization . Limited free version:there are limitations of free version. **What problems is Dataiku solving and how is that benefiting you?** The problem is solving of data integration. It's robust data integration tool for it integrate data from various data sources ## 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?filters%5Bsentiment_snippet%5D=1515432&qs=pros-and-cons§ion=pricing&secure%5Bexpires_at%5D=2026-08-14+15%3A36%3A58+-0500&secure%5Bsession_id%5D=edd95eff-47eb-40f8-a452-70f1fe13e0f4&secure%5Btoken%5D=67d6849c9c607a3d5c8b772da4a25260c4f19560fa9e678494f3be222a9e158c&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) - [Python](https://www.g2.com/products/python/reviews) - [RedShift](https://www.g2.com/products/redshift-redshift/reviews) - [Snowflake](https://www.g2.com/products/snowflake/reviews) - [Tableau](https://www.g2.com/products/tableau/reviews) ## Dataiku Features **Reports** - Reports Interface - Steps to Answer - Graphs and Charts - Score Cards - Dashboards - Customizable Reports - Marketing Reports - Sales Reports - Activity Dashboard - Interactive Reports - Customizable Reports - Customizable Reports - Activity Dashboard - Customizable Dashboard **Additional Functionality** - Tagging - Natural Language Processing - Data Extraction - Multi-Language - Predictive Analytics - Drag & Drop - Speech Recognition - Reporting/Analytics - Data Storage Management - Virtual Personal Assistant (VPA) - AI Copilot - Customer Segmentation - Collaboration Tools - Data Import/Export - Generative AI - For eCommerce - Role-Based Permissions - Customizable Branding - Search/Filter - Monitoring - Document Management - API - Data Visualization - Trend Analysis - Machine Learning - Access Controls/Permissions - Alerts/Escalation - Performance Metrics - Real-Time Data - Third-Party Integrations - Mobile App - Multiple Data Sources - For Sales Teams/Organizations - Sentiment Analysis - Activity Dashboard - Chatbot - Workflow Automation **Additional Functionality** - Code Generation - Text to Image - Generative AI - API - Natural Language Processing - Virtual Characters and Avatars - Content Generation - Personalization and Recommendation - Conditional Generation - Transformer Model - Automated Image & Video Editing - Interactive and Co-Creative Systems - Text Summarization - Data Augmentation - Variation Autoencoder Models - Adversarial Training - Transfer Learning and Fine-tuning - Simulation and Scenario Generation - Creative Design - AI Copilot - Prompt Engineering - Foundation Model **Deployment** - Language Flexibility - Framework Flexibility - Versioning - Ease of Deployment - Scalability **System** - Data Ingestion & Wrangling - Real-Time Data **Data Preparation** - Connectors - Data Governance **Responses** - Personalization - Route To Human - Natural Language Understanding (NLU) - Multi-Language **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 - Natural Language Processing - Simulation and Scenario Generation **Integration - Machine Learning** - Integration - Third-Party Integrations **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 - API Security - Data Security - Authentication **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 - Database Support - Multi-Language **Data Transformation** - Real-Time Analytics - Data Querying - Reporting/Analytics - Predictive Analytics - Visual Analytics **Management** - Cataloging - Monitoring - Governing - Model Registry **Model Development** - Feature Engineering **Data Modeling and Blending** - Data Querying - Data Filtering - Data Blending - Data Capture and Transfer **Platform** - Conversation Editor - Integration - Human-In-The-Loop - Drag & Drop Editor - Human-in-the-loop (HITL) **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 - Multimedia Support - Multi-Modal Input Support **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 **Additional Functionality** - Predictive Modeling - Configurable Workflow - Tagging - Data Import/Export - API - Predictive Analytics - Data Visualization - Endpoint Management - Multiple Data Sources - No-Code - Data Preparation - Auditing - Collaboration Tools - Big Data Analytics - ML Algorithm Library - Data Management - Activity Dashboard - Data Capture and Transfer - Activity Tracking - Data Connectors - Data Security - Data Extraction - Reporting & Statistics - Workflow Management - AI Copilot **Data Analysis** - Analysis - Data Interaction - Time Series Analysis - Trend Analysis **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 **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 **Additional Functionality** - Agent Collaboration - Data Governance - Prompt Engineering - Feedback Management - For No-Code Development - Version Control - Role-Based Permissions - Dynamic Modeling - API - Memory Management - Monitoring - Scalability - Cost Management - Scheduling - Natural Language Interface - Access Controls/Permissions - Multiple LLM Models - Integration Management - Customizable Frameworks - Pre-built Templates - Analytics - Intent Recognition - Contextual Guidance - Rules-Based Workflow - Attended Automation - Drag & Drop - For Sales/Marketing - Generative AI - Business Process Automation - AI Copilot - Third-Party Integrations - Multi-Channel Communication - Sentiment Analysis - Deep Learning - Code-free Development - Compliance Management - Automated Responses - Knowledge Management - Transfers/Routing - Personalization - Activity Dashboard - Customer Experience Management - Virtual Personal Assistant (VPA) - Chatbot - Speech Recognition - Reporting/Analytics - Task Management - Customer Engagement - AI/Machine Learning - Configurable Workflow - Unattended Automation - Conversation Intelligence - Workflow Automation - Proactive Error Detection **Decision Making** - Modeling - Data Visualizations - Report Generation - Data Unification - Customizable Reports **Deployment** - Managed Service - Application - Scalability **Connectivity** - Hadoop Integration - Spark Integration - Multi-Source Analysis - Data Lake - Real-Time Data - Data Capture and Transfer - Trend Analysis - What-if Analysis - Statistical Analysis - Data Blending - Ad hoc Analysis - Third-Party Integrations **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 - Third-Party Integrations **Agentic AI - Analytics Platforms** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Natural Language Interaction - Proactive Assistance - Decision Making - Third-Party Integrations **Additional Functionality** - Customizable Reports - Collaboration Tools - Data Extraction - Semantic Search - Data Storage Management - Ad hoc Reporting - Reporting/Analytics - Predictive Analytics - Activity Dashboard - Access Controls/Permissions - Visual Analytics - Data Mapping - Data Synchronization - Statistical Analysis - Categorization/Grouping - Trend Analysis - Data Profiling - Linked Data Management - Data Visualization - API - Multiple Data Sources - Sentiment Analysis - Search/Filter - Data Import/Export - Data Capture and Transfer - AI Copilot - Monitoring - Data Connectors - Ad hoc Analysis - Text Mining - Reporting & Statistics - Predictive Modeling - Real-Time Analytics - Configurable Workflow - Tagging - Endpoint Management - No-Code - Data Preparation - Auditing - Big Data Analytics - ML Algorithm Library - Data Management - Activity Tracking - Data Security - Workflow Management **Additional Functionality** - Version Control - Scalability - Personalization - Data Extraction - Webhooks - API - Natural Language Processing - Fallback Handling - Drag & Drop - Multiple LLM Models - Built-in AI Assistant - Automated Testing - Data Governance - Collaboration Tools - Pre-built Templates - Agent Design Tools - Deep Learning - Model Training - Analytics - Single Sign On - Debugging - Deployment Management - Proactive Error Detection **Self Service ** - Calculated Fields - Data Column Filtering - Data Discovery - Search - Collaboration / Workflow - Automodeling - Natural Language Search - Visual Discovery - Data Blending - Data Blending **Operations** - Data Visualization - Data Workflow - Governed Discovery - Embedded Analytics - Notebooks - Data Discovery **Generative AI** - AI Text Generation - AI Text Summarization - Generative AI **Generative AI** - AI Text Generation - AI Text Summarization - AI Text-to-Image - Generative AI **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 - Multiple Data Sources - Query Builder **Additional Functionality** - Data Warehousing - Drag & Drop - Activity Dashboard - Data Connectors - Ad hoc Reporting - Customizable Reports - Alerts/Escalation - Templates - Forecasting - Data Migration - Data Transformation - Access Controls/Permissions - API - Data Cleansing - Data Synchronization - AI Copilot - Dashboard Creation - Data Extraction - Data Security - SSL Security - Collaboration Tools - No-Code - High Volume Processing - Database Support - Search/Filter - Generative AI **Additional Functionality** - Demand Forecasting - AI/Machine Learning - Drag & Drop - For Healthcare - Forecasting - Data Extraction - Third-Party Integrations - Decision Support - Modeling & Simulation - Alerts/Notifications - Reporting/Analytics - Multiple Data Sources - For Retail - For Education - Data Discovery - Graphical Workflow Editor - Performance Metrics - Business Rules Management - AI Copilot - Natural Language Processing - Data Transformation - Data Clustering - API - Data Blending - Data Classification - Workflow Management - Workflow Automation **Advanced Analytics** - Predictive Analytics - Data Visualization - Big Data Services - Real-Time Analytics - Reporting/Analytics - Real-Time Analytics - Reporting/Analytics - Visual Analytics **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 - Third-Party Integrations **Performance & Scalability - Analytics Platforms** - Large data handling and Query Speed - Concurrent User Support - Database 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 - Ad hoc Query - Visual Analytics **Building Reports** - Data Transformation - Data Modeling - WYSIWYG Report Design - Integration APIs - Real-Time Data - Real-Time Data - Third-Party Integrations - Third-Party Integrations **Platform** - Customization - User, Role, and Access Management - Internationalization - Sandbox / Test Environments - Performance and Reliability - Breadth of Partner Applications - Mobile Access - Metadata Management **Additional Functionality** - Drag & Drop - Secure Data Storage - Data Import/Export - Customizable Branding - Dashboard Creation - Access Controls/Permissions - Real-Time Reporting - Metadata Management - Forecasting - Data Storage Management - Ad hoc Analysis - Ad hoc Reporting - Data Migration - AI Copilot - Self Service Data Preparation - Search/Filter - Data Mapping - Monitoring - Sentiment Analysis - User Management - Sales Trend Analysis - Charting - Reporting & Statistics - Performance Metrics - Alerts/Notifications - Data Extraction - Widgets - Storytelling - Trend Analysis - Collaboration Tools - Financing Management - Self-service Analytics - KPI Monitoring - Dashboard - API - Data Integration - Scorecards - Data Management - Task Management - Progress Tracking - Data Connectors - Data Visualization - Profitability Analysis - Project Tracking - Strategic Planning - Goal Setting/Tracking - Publishing/Sharing - Predictive Analytics - Real-Time Monitoring - Templates - Mobile Access - Customizable Templates - Workflow Management - Financial Reporting - Audit Management - OLAP - Single Sign On - Data Synchronization ## Top Dataiku Alternatives - [Gemini Enterprise Agent Platform](https://www.g2.com/products/gemini-enterprise-agent-platform/reviews) - 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