Reviews from AWS Marketplace
0 AWS reviews
-
5 star0
-
4 star0
-
3 star0
-
2 star0
-
1 star0
External reviews
External reviews are not included in the AWS star rating for the product.
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
- Leave a Comment |
- Mark review as helpful
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
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
Dataiku breaks down barriers
What do you like best about the product?
Dataiku creates a collaborative environment for technical and non-technical users to bring AI to life. Dataiku breaks down barriers to entry with low/no-code, allowing users of all levels to gain experience and confidence in data science.
What do you dislike about the product?
I'd be reaching for issues here but maybe some elements of the user interface could be improved?
What problems is the product solving and how is that benefiting you?
Democratizing analytics via agentic AI; saving time, bandwidth, etc. by outsourcing time-consuming and repetitive analyses (both via agentic AI and ML models)
Flexible, Usable but not perfect.
What do you like best about the product?
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 the product?
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.
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 the product 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.
One-stop shop for all analytics and ML needs
What do you like best about the product?
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 the product?
Dataiku has a decent learning curve if you are coming from a non-technical background.
What problems is the product 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.
Reduction of time to value
What do you like best about the product?
Visual layout makes data transformation more clear. Built in tools speed up development time in particular with tools like the LLM recipes and ML model "battle"
What do you dislike about the product?
I have noticed inconsistencies with how code is executed in a python notebook vs a python recipe. Sometimes that works in a recipe doesn't work in a notebook. Refusal to add global dark mode.
What problems is the product solving and how is that benefiting you?
Simplifying the use and deployment of LLM projects in friendly interface.
showing 1 - 10