Dataiku is an AI platform that we use for oil and gas exploration. Even though I can't provide specific details, this is the primary use case for us.
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The platform organizes workflows visually and efficiently
What is our primary use case?
What is most valuable?
One of the valuable features of Dataiku is the workflow capability. It allows us to organize a workflow efficiently. The platform has a visual interface, making it much easier for educated professionals to organize their work. This feature is useful because it simplifies tasks and eliminates the need for a data scientist. If you are knowledgeable about AI, you can directly write using primitive tools like Pantera flow, PyTorch, and Scikit-learn. However, Dataiku makes this process much easier.
What needs improvement?
One area for improvement is the need for more capabilities similar to those provided by NVIDIA for parallel machine learning training. We still encounter some integration issues.
For how long have I used the solution?
We have been using Dataiku for three years.
How are customer service and support?
Customer service is somewhat different because Dataiku partners with local industry experts who understand the business better and provide support. It can be challenging to determine the provider of better support, however, overall, the support is good.
How would you rate customer service and support?
Positive
Which solution did I use previously and why did I switch?
We are only using Dataiku.
What was our ROI?
I believe the return on investment looks positive.
Which other solutions did I evaluate?
I considered another option that excels in parallel processing. However, it falls short in other areas. No product is perfect. If these two solutions worked together, it would be advantageous. Unfortunately, one has strengths in certain areas while the other excels in another.
What other advice do I have?
Why not? BHP sold the energy part to a company called Woodside. It has changed because they are now part of Woodside.
Overall, I rate the product eight out of ten.
Dataiku for Grad Student
What do you like best about the product?
Dataiku's interface is intuitive and easy to navigate, making it accessible for students who are new to data science. The drag-and-drop functionality and clear visual representation of data pipelines help in understanding complex workflows. Even with limited coding experience, I was able to build robust data models and analytics pipelines with ease.
What do you dislike about the product?
Can be resource-intensive, requiring robust hardware for optimal performance
Advanced features might have a steep learning curve for beginners
Pricing can be high for students and small projects
Advanced features might have a steep learning curve for beginners
Pricing can be high for students and small projects
What problems is the product solving and how is that benefiting you?
As an Indian student in the ML domain, I focus on solving business problems such as predicting consumer behavior to improve marketing strategies, performing sentiment analysis for brand monitoring, and optimizing SEO to enhance website visibility. I also work on fraud detection in online transactions, developing personalized recommendation systems to boost user engagement, and optimizing supply chain operations through predictive analytics. These solutions help businesses in the confectionery and CPG sectors drive growth and efficiency.
ML
What do you like best about the product?
model implementation is very good to understand
What do you dislike about the product?
website design may be modified in few areas
What problems is the product solving and how is that benefiting you?
learning about data and ML
very good
What do you like best about the product?
it is really helpful in cleaning and processing data
What do you dislike about the product?
i have nothing to dislike about Dataiku as of now
What problems is the product solving and how is that benefiting you?
Organising data
Data okie for Project Management
What do you like best about the product?
Great for building and refining project management documents and data analysis in consulting work to help business grow
What do you dislike about the product?
Relative standing as compared to other well known platforms
What problems is the product solving and how is that benefiting you?
Project management
Streamlining Data Science Workflows
What do you like best about the product?
The ability to automate repetitive tasks like model deployment and report generation is a game-changer. Dataiku frees up data scientists to focus on higher-level analysis and innovation, which is what I find most valuable.
What do you dislike about the product?
While Dataiku excels at streamlining many aspects of the data science workflow, optimizing workflows involving large datasets can be cumbersome. When dealing with a massive number of data partitions, I encountered processing issues that caused delays. A more efficient way to handle big data within the platform would be ideal for streamlining workflows with these complex datasets.
What problems is the product solving and how is that benefiting you?
Communication and collaboration between data scientists and business users were clunky. Dataiku's intuitive interface and collaborative features bridge this gap, allowing everyone to stay on the same page and contribute effectively.
Helpful for project collaboration
What do you like best about the product?
It allows us to apply current project workflow to new data without any need of low level abstarction of project.
What do you dislike about the product?
It is very useful software but processing charges are a little high.
What problems is the product solving and how is that benefiting you?
Dataiku simplifies data integration, enhances collaboration, and automates machine learning workflows, allowing users to efficiently prepare, analyze, and model data, thereby accelerating insights and improving decision-making.
Gives different aspects of modeling approaches and good for multiple teams' collaboration
What is our primary use case?
My current client has Dataiku. We do sentiment analysis and some small large language models right now. We use Dataiku as a Jupyter Notebook.
We use it a lot for marketing and analytics. The marketing and sales team uses Dataiku.
What is most valuable?
It's got good feature selection and creation of feature stores, and it also gives different aspects of modeling approaches. There are a lot of similarities with DataRobot.
So feature selection, different modeling, and financial metrics are good aspects.
What needs improvement?
The no-code/low-code aspect, where DataRobot doesn't need much coding at all.
Dataiku still needs some coding, and that could be a difference where business data scientists would go for DataRobot more than Dataiku because you still have to code and use either Python or R, or Scala. However, with DataRobot, you don't have to do that at all.
For how long have I used the solution?
I've used Dataiku for about four years.
How are customer service and support?
The company is based in France. But they're more and more in America as well.
So, the support was okay.
How would you rate customer service and support?
Positive
Which solution did I use previously and why did I switch?
I used DataRobot. Dataiku has a different kind of structure to it. It's not financially heavy like DataRobot, which caters more to financial companies, like banks. Dataiku doesn't have that yet. I think they are also working on that area. But yeah, there are some key differences between the two products.
DataRobot has an additional feature with financial firms that it creates all these financial metrics when you run a time series analysis. Those things I have not seen in Dataiku.
If any financial company is choosing between DataRobot and Dataiku, they will definitely go for DataRobot because it creates all these financial metrics. It creates deltas, time series, time difference fields, and things like that. So, that is an added feature that DataRobot has.
What's my experience with pricing, setup cost, and licensing?
Pricing is pretty steep. Dataiku is also not that cheap. It depends on the client and how much they want to spend towards a tool.
What other advice do I have?
Overall, I would rate it an eight out of ten, except for some coding things that are there, which some people may not want to do, like certain business data scientists.
Dataiku is good for multiple teams' collaboration. If many teams are collaborating and sharing Jupyter notebooks, it's very useful. It has a good data processing structure and includes most of the models. I haven't checked the large language models in it yet, but it's a pretty good tool. It does well with analytics and has a sound structure on the back end.
Some coding aspects are necessary, but it generates SQL code, which is an added feature. A lot of data engineers like Dataiku because it generates SQL code on the right side.
Best tool to Analysis and Presenting data in different Dimention
What do you like best about the product?
It is the best tool to analyze huge data based on the level of organization for decision-making.
Dataiku is the best tool to present data different views and representation data.
Dataiku is the best tool to present data different views and representation data.
What do you dislike about the product?
The tool can be more user friendly where citizen developers can analyze the data of their own business data.
It can made available with other tool where they can connect the date to analyze it.
It can made available with other tool where they can connect the date to analyze it.
What problems is the product solving and how is that benefiting you?
We are using dataiku to integrate with our own product to analyze the data and present it dashboard for decision making quickly and present in more GUI representation
All-in-one data pipeline management tool
What do you like best about the product?
Dataiku DSS provides a central platform to manage your data pipelines, ML models and more.
What do you dislike about the product?
UI could use some work. Also, the support for using multi-file projects as a recipe or pipeline is limited.
What problems is the product solving and how is that benefiting you?
Dataiku shows the flow diagram as a DAG which provides a good visualization. Moreover, its recipe build features allow you to create most data pipelines without writing any code.
Its recently added features like ML Notebooks are very useful for running Machine Learning classification or regression tasks on a dataset. Dataiku has complete support for training and evaluating Machine Learning models.
Its recently added features like ML Notebooks are very useful for running Machine Learning classification or regression tasks on a dataset. Dataiku has complete support for training and evaluating Machine Learning models.
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