We use the solution for data science and machine learning.
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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.
Saves a lot of time because I can quickly handle all the data preparation tasks and concentrate on building my machine learning algorithms
What is our primary use case?
How has it helped my organization?
We were a team of six Dataiku scientists and one data engineer. We focused on fully leveraging Dataiku for all our data science-related tasks. This included data preparation, preprocessing, benchmarking machine learning algorithms, handling everything related to production, and making our algorithms available to stakeholders.
What is most valuable?
The advantage is that you can focus on machine learning while having access to what they call 'recipes.' These recipes allow me to preprocess and prepare data without writing any code. This saves a lot of time because I can quickly handle all the data preparation tasks and concentrate on building my machine learning algorithms.
What needs improvement?
One of the main challenges was collaboration. Developers typically use GitHub to push and manage code, but integrating GitHub with Dataiku was complicated. While it was theoretically possible to use GitHub with Dataiku, in practice, it was difficult to manage our code effectively and push it from Dataiku to GitHub.
Another limitation was its ability to handle different types of data. While Dataiku is powerful for working with structured data, like regular or geospatial data, it struggled with more complex data types such as text and image. In addition to the challenges with GitHub integration, the limited support for diverse data types was another feature lacking at that time.
For how long have I used the solution?
I have been using Dataiku for over a year.
What do I think about the stability of the solution?
Since Dataiku relies on various open-source libraries and tools, updates or upgrades to these components can sometimes impact the stability of Dataiku's features. This can make it challenging to maintain consistent stability, as changes in the underlying open-source tools can affect how Dataiku functions.
I rate the stability as six out of ten.
What do I think about the scalability of the solution?
There are some scalability issues.
I rate the scalability as seven out of ten.
How are customer service and support?
Technical support was very good compared to other tools. We had access to chat and support.
How would you rate customer service and support?
Positive
How was the initial setup?
The initial setup is very easy. It has many tutorials and many guidelines. After the initial deployment, it took about a week to manage all the setup and resolve various issues before we had a stable version of Dataiku that we could use consistently.
I rate it as eight out of ten, whereas ten is easy.
What's my experience with pricing, setup cost, and licensing?
It is very expensive.
What other advice do I have?
I wouldn't recommend using Dataiku if only one data scientist is on the team. However, having a larger team—let's say more than five data scientists—can be very helpful. Dataiku offers features that are especially useful when multiple people are working on the same project, and it also has tools that make it easier to move from the proof of concept stage to production.
Overall, I rate the solution as seven out of ten.
Which deployment model are you using for this solution?
On-premises
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.
Robust tool for data science engineer
What do you like best about the product?
Frequently Use: this tool we use very frequently for data analysis and optimise an d filtering.
Easy to use: this tool is easy to use.
It's very user friendly
Easy to integration: this tool is easy to integration with other platforms.
So that we could collaborate with other team.
No of features: there are no of features .like
1.robust data integration
2. scalability
3. Visual data preparation
4.modelmonitering
Etc
Easy to implementation: dataiku dss is easy to implementation
Customer support: this is very good customer support.
Easy to use: this tool is easy to use.
It's very user friendly
Easy to integration: this tool is easy to integration with other platforms.
So that we could collaborate with other team.
No of features: there are no of features .like
1.robust data integration
2. scalability
3. Visual data preparation
4.modelmonitering
Etc
Easy to implementation: dataiku dss is easy to implementation
Customer support: this is very good customer support.
What do you dislike about the product?
Although this tool is very easy to use nd user friendly but some people who are new in data science get some challenging to use.
Cost: cost is also high .this is not suitable for small organization .
Limited free version:there are limitations of free version.
Cost: cost is also high .this is not suitable for small organization .
Limited free version:there are limitations of free version.
What problems is the product solving and how is that benefiting you?
The problem is solving of data integration.
It's robust data integration tool for it integrate data from various data sources
It's robust data integration tool for it integrate data from various data sources
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.
Recipes for everything to everyone
What do you like best about the product?
The way that Dataiku can use tools like Python, R, SQL and the built-in tools to make even the most unorganized data source to information with a lot of value, undestandable for anyone that can read a dashboard.
What do you dislike about the product?
Sometimes the processing time can be prolonged for multi-user purposes; even with training data and a reduced number of users, it could be a very stressful task, going from 10 seconds to 1:30 minutes.
What problems is the product solving and how is that benefiting you?
The day-to-day information that we use in the manufacturing, marketing and salesforce are crucial for decision-making; having all the information stored on the same platform with prediction models included is a must-have combined with Qlik sense, tableau or similar softwares
Dataiku for super interpretable pipelines
What do you like best about the product?
Dataiku helps to make your hefty data pipelines readable and easier to manage. There are a lot of inbuilt recipes which helps you make your pipeline modular. It is definitely one of the nest place to productionise your systems.
What do you dislike about the product?
Not all recipes help you do the exact thing you want to do. For that you have to code the entire pipeline in a say, python recipe which again makes it less modular. Also there is less documentation available.
What problems is the product solving and how is that benefiting you?
Dataiku is beneficial in breaking your entire code into pieces called zones which makes it super interpretable for the client to understand what each piece actually does. Also, it has a good UI which makes easy to track the flow of your pipeline.
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