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A Complete and Intuitive Platform for Data Science Projects
What do you like best about the product?
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 the product?
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 the product 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.
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Great one-stop shop for all Data Science needs
What do you like best about the product?
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 the product?
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 the product 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.
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.
Agentic AI
What do you like best about the product?
Great use cases of Agentic AI were demostrated
What do you dislike about the product?
Nothing I can think of, I liked everything about the conference
What problems is the product solving and how is that benefiting you?
Troubleshooting becomes a lot easier when you have a visual interface
It's automation feature makes repetitive tasks seem less painful
It's automation feature makes repetitive tasks seem less painful
Data for everyone
What do you like best about the product?
I love the web platform that decreases peoples adversity to getting to explore. I love the visual recipes to make it easier to see what happening
What do you dislike about the product?
Getting organized from project to prokect
What problems is the product solving and how is that benefiting you?
Providing solutions to my business
Best AI/ML Engine
What do you like best about the product?
Best ai ml engine for getting your data from silver to gold, building and deploying your projects, and all in one centralized platform.
What do you dislike about the product?
Nothing, I love all the features of Dataiku
What problems is the product solving and how is that benefiting you?
Big into the pharma space, great for forecasting sales and goal targets or even building analytical webapps!
Enterprise ready data wrangler
What do you like best about the product?
Share analytic workflows with others in the firm
What do you dislike about the product?
Not opinionated enough to start a fresher in the organization
What problems is the product solving and how is that benefiting you?
Marketing and risk identification
Great data exploration tool
What do you like best about the product?
Simplicity and quick data exploration and analytics
What do you dislike about the product?
Needed training documents for quickly onboarding new users
What problems is the product solving and how is that benefiting you?
Marketing
Good tool for ai and gen ai llm but not etl and enginer
What do you like best about the product?
Gen ai possibilities, llm mesh possibilities and the ai stuf
What do you dislike about the product?
ETL and Data Engenireering part, specifically with big data
What problems is the product solving and how is that benefiting you?
ETL Problems
A partner worth its weight in gold
What do you like best about the product?
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.
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 the product?
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.
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 the product 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.
Would I recommend it? Absolutely—but with a caveat: Treat it as a marathon partner, not a sprinting miracle worker.
Data for all
What do you like best about the product?
Ability to scale analytics for our business
What do you dislike about the product?
Nothing I see at this moment except for using AI easier
What problems is the product solving and how is that benefiting you?
Client engagement and analytics
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