My usual use cases for Dataiku are mostly data science use cases, or model creation and training.
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Unified platform has accelerated model development and improved collaborative data science work
What is our primary use case?
What is most valuable?
The best features of Dataiku that I think are most important for me include the ETL, the Python, processing, database, and API services.
These features are so valuable to me because all these features are available in one place, which helps me create a solution quickly rather than having a couple of technologies together and integrating them.
I have used Dataiku's AutoML tools, and they definitely shorten the process; they have a lot of built-in intelligence which we can use, and they help to automate a lot of things.
Dataiku enhances collaboration within my team because it is one single tool; all the projects are in one place. People can share each other's workbooks and reusable codes.
What needs improvement?
I think Dataiku could be improved or enhanced in future releases with more 'talk to my data' capabilities, maybe more NLP features, and maybe a platform to build agents.
These improvements would benefit me and my processes because they will help us to continue using Dataiku as one platform; right now we are exploring other platforms for the features which are missing, and if they are available within the same platform, I think it will increase the usage of Dataiku further.
I think the pricing and licensing of Dataiku is a bit expensive; it could be improved further, and I think they should have a different kind of licensing model as well.
For how long have I used the solution?
I have been working with Dataiku for four years.
What do I think about the stability of the solution?
As for stability and reliability, so far so good; after the installation, I really had no problems. Dataiku works very well.
What do I think about the scalability of the solution?
Dataiku is quite scalable, as long as I can pay for more licenses, there is no technical limitation.
How are customer service and support?
I would rate the technical support from Dataiku around seven.
I would like them to improve something about the support because it was just our experience that we were complaining about a few things which were not working, and it took a very long time for them to acknowledge that it was not working. It was back and forth, trying this and trying that, and so on. They should not take the complaints so lightly.
How would you rate customer service and support?
Positive
Which solution did I use previously and why did I switch?
Before working with Dataiku, I did not use a different solution for the same use cases; we were doing many things ad hoc, writing notebooks, putting it on, and deploying it to the server ourselves and so on.
How was the initial setup?
The challenges I faced were a lot of technical challenges; there is generally a guide on how to set up and install and configure, and we were following those guides, but we were not able to do it ourselves. When we purchased the licenses, we believed that we could do it ourselves and that it would be a straightforward installation, but then we had to extend the contract with Dataiku to get their experts in order to set up everything. The challenges were mostly that things were not running, there were a lot of technical exceptions, and there was a lot of correlation between the server's version and Dataiku's version. They also had to fix a couple of things on their side because things were not working.
What about the implementation team?
I participated in the initial setup and deployment of Dataiku, and the process was somewhere in the middle; it was not as straightforward as we thought it would be. We had team members who had worked on it before, but we still had to get experts from Dataiku in order to help us, and it took a bit longer than expected.
What was our ROI?
I consider the return on investment with Dataiku valuable because for us, it is one single platform where all our data scientists come together and work on any model building, so it is collaboration, plus having everything in one place, organized, having proper project management, and then built-in capabilities which help to facilitate model building.
Other than time efficiency and everything being in one place, the other return on investment I observed with Dataiku is that these are the major aspects; if I have to think about taking out Dataiku and putting my own tools and practices in place, it will be a lot of work to build everything together. These for me are the main things.
Which other solutions did I evaluate?
Before choosing Dataiku, I evaluated other options, specifically Databricks, Dataiku, and our vanilla solutions with Azure and AWS.
The reason I chose Dataiku is that we had team members who had used Dataiku before, which gave us more confidence that we would be successful.
What other advice do I have?
Within the last twelve months, I'm mostly working with Snowflake and Dataiku.
I'm a customer of this solution. I don't know where I purchased Dataiku from.
Dataiku's data source integration flexibility has not benefited my data projects much because we are using our own tools for that.
The valuable insights I have derived from using Dataiku's machine learning capabilities include the fact that we are building our own proprietary model, and Dataiku's machine learning capabilities help us to build those models and create insights. It's very proprietary and we have not used any out-of-the-box insights that are available, but the whole Dataiku application helps to speed up the process.
Dataiku's governance and security controls have helped to maintain data privacy. All those features we are using, and again, this is all part of the applications.
Before Dataiku's implementation, all of this used to be thought of separately and implemented using some tools or technologies, but with Dataiku, all of this comes together in one platform. Once my data is in Dataiku, I know that I can put in security, I can integrate it with my IAM and everything, so I don't have to think of all these features independently. Dataiku provides everything in one platform.
I rate this solution an eight overall.
Unified data projects have accelerated development and simplified architecture for higher ROI
What is our primary use case?
We are a consulting firm for BFSI customers for the FSI value chain use cases, which is what we use Dataiku for, based on the problem statement the customer comes up with.
What is most valuable?
Dataiku is a complete platform to build ETL and data pipeline and deploy it, which I appreciate. It gives the complete solution to the customer and it is easy to use. Although it is expensive, the ease of use and the higher ROI for the customer make it worthwhile.
Dataiku's role in enhancing collaboration within the teams is good.
What needs improvement?
I do not see anything that I would improve or enhance in Dataiku at this time; overall, it is a good tool to incorporate and to suggest to customers.
Currently, I do not see anything specific that I would include or any functionality that requires enhancement. Dataiku gives the complete picture of the AI universe, and we have not faced any glitches, so I do not have recommendations or suggestions for improvement.
All products have room for improvement, and I would like to see their pricing simplified, as it is somewhat complex. In terms of documentation, there is substantial documentation available. Customer support is good, the product is scalable, and it provides flexibility to develop. Someone who needs to do coding can do it, and someone who does not know coding can also build solutions, but the pricing is complex, which I believe should be improved.
For how long have I used the solution?
I have been using Dataiku for the last two years, but in data science, my experience spans more than ten years.
What do I think about the stability of the solution?
I have not experienced downtimes, crashes, or glitches with Dataiku in the project that we implemented.
What do I think about the scalability of the solution?
Dataiku appears scalable, and in the project that we implemented, we did not face any scalability issues.
How are customer service and support?
I am aware of the tech support and customer service team of Dataiku.
As a partner with Dataiku, my experience with them is good; they are supportive, and when we contact them, we receive a quick response.
How would you rate customer service and support?
Positive
Which solution did I use previously and why did I switch?
Before Dataiku, I used R, R Shiny, and Python for data science, but there is no comparison between Dataiku and these tools; these are standard typical tools used to build data science projects.
How was the initial setup?
The initial setup and deployment of Dataiku is straightforward because it provides a web-based interface; it does not come with complexity for the setup.
What was our ROI?
In terms of ROI, the use of Dataiku simplifies the architecture of customers, which helps them to decommission some of their existing tools; this faster development, along with simplified architecture, gives them the ROI they need.
Which other solutions did I evaluate?
I compared Dataiku with SAS and Alteryx, and Dataiku was better than them.
I have not conducted a detailed comparison of Dataiku with Alteryx, but when it comes to governance, Dataiku surpasses Alteryx, especially in terms of compliance and governance-related features it provides for the model; I see that as the gap in Alteryx.
What other advice do I have?
Dataiku's data source integration flexibility has not benefited our data projects significantly because customers primarily use Databricks and Snowflakes, but for data science, AI, ML, and GenAI projects, it provides nice compatibility.
Dataiku is mainly used to connect the teams together and it helps to document the project details. Usually in companies, I do not see customers use many chatting tools or the tools that Dataiku provides. They are still dependent on more in-house tools that the company provides. However, because it is a platform, different teams and different verticals can come into one place and build the project. This is where I think communication is better, and they have shared data sources, which makes it more communicable among teams across verticals.
I cannot share many details on the valuable insights I have derived from using the machine learning capabilities in Dataiku because that is based more on the customer's use cases, but the value Dataiku provides is more on faster development and better ROI.
Given my experience with Dataiku, we present our sales pitch for Dataiku to customers, considering it gives the full picture or the full platform to build AI, ML, and GenAI-based applications. It is one platform where you can build entire data and AI projects or solutions. I would rate this review as a nine out of ten.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Has enabled reliable data pipeline creation and supports rule-based alerts for quality monitoring
What is our primary use case?
My main use cases in Dataiku include ensuring a strong data pipeline ingestion. We have people from data management, so we need to take care of the pipeline, their data quality, data drifting, all these things. We are taking care of it with the Dataiku rule-based alert systems we have created.
What is most valuable?
The best feature in Dataiku is that once the data is connected in the underneath layer, it flows exceptionally smoothly if you know how to tweak it. If you don't know, then it will create a mess. If you know how to tweak it and make the data according to your requirement, then it will be good. If you don't know and are trying to learn on the production, then it is a disaster.
I have used Dataiku's AutoML tools. The AutoML tools have helped me on the fly, as you can apply the machine learning models. They are continuously reading your data and then creating the feature enablement. The moment feature enablement has happened, then you can do the model registry on the fly. Those model registries can trigger your new data. Imagine whatever the data test and train that is passed. Your operational data which is coming new every day, then that feature is enabled and it will give the reasonable amount of prediction and reasonable amount of value on the column so that you can utilize those. You can consume those in the application layer.
Dataiku's data source integration flexibility is completely up to the requirement. We are not using it for ourselves. We are using it for business teams, and they are sending the requirement and we are ingesting according to their requirement. The important thing is, imagine raw data is coming A, but they need A plus B plus C multiply by D. All those kinds of enablement we are doing with the help of Dataiku.
Our source system, the core system, is continuously throwing the raw data on the landing layer. Then from the landing layer, we are converting those raw data and making it as a consumption layer, consumable data. With the help of this, we are doing it.
What needs improvement?
In terms of enhancing collaboration within my team, I would not say Dataiku is the best one because it's so expensive. We are not able to provide it to everyone. There are very few people who have the developer license and are using it. Once the data pipeline is created, then we are directly handing over that data pipeline to our user on the ingestion layer. It is not a very cost-effective solution, I must say, though it is good for developing purposes only.
Pricing can be improved.
For how long have I used the solution?
I have been using this product for four years.
What do I think about the stability of the solution?
In my opinion, Dataiku is stable because we know how to use it. There are many unstable things happening, so it's not that only the application is stable or unstable. Even so many other things, we are facing challenges. I cannot only blame one thing.
In terms of stabilization, if my data has no outlier creation in the raw data, then it is quite stable. I would rate it a seven.
How are customer service and support?
For support, I haven't created any support tickets, so I really don't know about it, but it is quite good.
How would you rate customer service and support?
Positive
How was the initial setup?
The initial setup started with HANA. Then they introduced Databricks. When Databricks got live, then they started giving this license for Dataiku. We got the Dataiku license and learning. Everything went smoothly. Now Databricks is replaced by Snowflake. Even on Snowflake, we can do many things.
What was our ROI?
It is hard to say if I've seen a return on investment in Dataiku because we are far away from the monetization of the data. There are other teams who are taking care of the monetization. We are not from resource management, so it becomes very hard for us to calculate the ROIC on this at each and every application level. We are not using only Dataiku, we are using many other products.
Which other solutions did I evaluate?
In my opinion, it is good, not bad. I must say because I'm using many other tools as for a data operating model. It is much better than other tools because it has a clickable solution. Most of our data citizens who really don't know the coding thing can easily do things with the help of the mouse. Most of the things are working fine, so there is nothing to complain about.
What other advice do I have?
Overall, Dataiku is really good. I would rate it an 8 out of 10.
Which deployment model are you using for this solution?
Hybrid Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Intuitive Visual Interface, Powerful pipelines, but Needs Better History Management
What do you like best about the product?
I really appreciate how the graphical user interface handles paths and threads. It allows you to manage all your code and datasets visually, and everything is automatically aligned, which makes the experience very soothing to use.
What do you dislike about the product?
There isn't anything in particular that I dislike about Dataiku. However, one area for improvement would be better management of the history and recent code I've worked on. It would be helpful if this information were more easily accessible and visually highlighted.
What problems is the product solving and how is that benefiting you?
Managing large datasets was my primary challenge. Having access to a unified portal for both geospatial and other data, along with the required processing power, has been crucial for achieving my objectives as a data scientist. The ability to test various machine learning models in one shot is simply revolutionary. I cannot imagine ever going back to working on my PC for these tasks.
Dataiku : Making your Data Science work easy
What do you like best about the product?
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 the product?
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 the product 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.
Dataiku review
What do you like best about the product?
I like that its basically ran by Ai and you don't have to do a whole lot
What do you dislike about the product?
Nothing its a great app maybe a little costly but worth it
What problems is the product solving and how is that benefiting you?
It solved my issue with keeping track of all my paperwork it does it all for me
Dataiku for Data Science/AI projects
What do you like best about the product?
SImple to use & scale. Flexibity & integrated well into the any infra.
What do you dislike about the product?
The main drawbacks of Dataiku is cost, scalability limitations, integration complexity, performance issues, and the need for user training.
What problems is the product solving and how is that benefiting you?
Dataiku addressed critical issues in data quality, operational efficiency, analytics collaboration, AI scalability, compliance, and business-user empowerment, serving as a unified platform for enterprise data innovation and value generationtion.
A Powerful Platform for End-to-End Data Science & Collaboration
What do you like best about the product?
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 the product?
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 the product 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.
Great website and great platform!!
What do you like best about the product?
It brings together data people analysts, engineers, scientists on one platform
What do you dislike about the product?
honestly, is that it can feel a bit heavy and slow, especially on large projects with a lot of visual recipes or datasets.
What problems is the product solving and how is that benefiting you?
Collaboration gaps, i think it brings data scientists, analysts, engineers, and business users into one shared workspace
Functionality
What do you like best about the product?
Strong version control, shared projects, and role-based access enhance teamwork across data and business teams
What do you dislike about the product?
Some powerful capabilities are only available in higher-tier or enterprise versions, which may not be cost-effective for smaller teams
What problems is the product solving and how is that benefiting you?
Covers the full data lifecycle: ingestion, preparation, modeling, deployment, and monitoring
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