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DataikuExternal reviews
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Influencial
What do you like best about the product?
Approach to Agentic AI, presented during the demos
What do you dislike about the product?
Lack of Hands On Labs on the website. For developers its useful to try the Agentic approach in identifying areas of innovation
What problems is the product solving and how is that benefiting you?
We do not use Data Iku for now
Data science for non data scientists
What do you like best about the product?
Very intuitive interface, easy to configure and build intelligent models with.
What do you dislike about the product?
It sometimes just doesn’t work when you are executing models.
What problems is the product solving and how is that benefiting you?
Improving marketplace approvals
Very organized and informative
What do you like best about the product?
Elastic and agnostic to supplemental technology.
What do you dislike about the product?
Dataiku is still not as well known in the technology sector.
What problems is the product solving and how is that benefiting you?
Predictive models and customer segmentation.
Great product with many flexibility and plug-ins
What do you like best about the product?
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.
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 the product?
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 the product 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.
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.
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
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.
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.
Very user friendly
What do you like best about the product?
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 the product?
So far I don’t have any complaints about Dataiku it’s working fine for me.
What problems is the product 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.
Great overall experience
What do you like best about the product?
The ease of use low code no code so all users can use
What do you dislike about the product?
Explanation on new capabilities because new ones come out
What problems is the product solving and how is that benefiting you?
Helping different teams connect on different projects regardless of level
Easy and flexible tool for any type of user
What do you like best about the product?
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 the product?
None.
Sometimes tool gets stuck in running complex jobs
Sometimes tool gets stuck in running complex jobs
What problems is the product solving and how is that benefiting you?
Quick analytical insights to daily business decisions
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