My company sells licenses for both Dataiku and Alteryx, and we have clients who use them. I engage with several companies in telecommunications, retail, and energy to assess how our clients are utilizing these platforms.
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Dataiku for Advanced Analytics
What do you like best about the product?
Dataiku excels at creating a centralized platform for democratizing data for every user to collaborate regardless of their skill level. The platform is suitable for coders and non-coders, developers and business users, etc. Each release contains new Gen AI, machine learning or analytics capabilities and allows you to connect to source systems, build data workflows, interactive visualizations and automate the entire end-to-end life cycle.
What do you dislike about the product?
I think the user documentation can be challenging to sift through, I wish information was collated within one repository instead of many different links.
What problems is the product solving and how is that benefiting you?
Dataiku allows me to build end-to-end solutions leveraging big data at scale.
Dataiku is a great ML Flow and ML OPs tool all the way from a beginner to an expert
What do you like best about the product?
I started using Dataiku as a junior data analyst. The visual recipes have turned around how you built an analytics project from end to end. As I started tackling complex projects and started expanding my knowledge of data science and the domain I am working on, I started to discover the latitudes of capabilities that I can adopt from dataiku tools and api. It has immensely helped me to expedite my career goals. Another fantastic aspect would be the consistent upgradation of the features and tools like Data quality management, LLM mesh and Agentic AI in the studio which becomes an inspiration for me to tryout and implement additional steps (in the ML flow) that helps me increase business value in the projects I am working on. I enrolled in the dataiku academy too.
What do you dislike about the product?
As I described dataiku is fantastic to start with as a beginner but as the project gets more complex, as I started using dataiku apis in python I started feel a lack of detail in the documentation availability. For example, I wish that the dataiku apis for python to have a clearer documentation as we can observe in some libraries like pytorch, Scikit learn, Scipy or plotly. Details like all the parameters available for a specific function and additional parameters which can be used with an example or two explaining what each parameter mean for implementing of the function. The documentation currently available is highly limited in helping me understand the complete capabilities of a specific function or api. So, my best resource for referral often is the blog post answers that the dataiker provides. With gratitude I would request the team to improve the documentation to such an extent it would add value to an experienced ML Ops developer.
What problems is the product solving and how is that benefiting you?
Dataiku simplifies ML Flow and ML Ops process which enables me to focus on data preparation, building models, validating them and implementation. I would like to appreciate the availability of dataiku functionality through dataiku apis which makes it easier of me to create and deploy projects just with python.
Rapid AI with DataIku
What do you like best about the product?
DataIku allows me to quickly train and evaluate multiple models on given data. The results immediately reveal dominant features. This allows me to better understand the data that I'm dealing with.
DataIku accepts many data formats and sources.
DataIku accepts many data formats and sources.
What do you dislike about the product?
In DataIku, Chart styling is not as intuitive as I'd expect. Feedback loops are not allowed/implemented.
DataIku does not allow CTEs in sql queries, which has consequential unwanted data processing.
DataIku does not allow CTEs in sql queries, which has consequential unwanted data processing.
What problems is the product solving and how is that benefiting you?
DataIku speeds up my selection of AI strategies and dominant feature discovery.
Data pipeline and machine learning done easily
What do you like best about the product?
Dataiku helps you abstract from pipelining focus on creating value-adding data products.
What do you dislike about the product?
I dislike the most the lack of visibility over what is happening in the backend and, particularly, the price.
What problems is the product solving and how is that benefiting you?
Demand forecasting: we have built the forecasting pipeline for half a million SKUs in three months.
Price determination engine: we have automated pricing for a subset of SKUs, all made easy since we could abstract from pipelining.
Price determination engine: we have automated pricing for a subset of SKUs, all made easy since we could abstract from pipelining.
Drag-and-drop platform accelerates model development with distributed compute engine
What is our primary use case?
What is most valuable?
The most valuable feature of Dataiku, in my opinion, is the possibility to use Spark, which is a distributed compute engine. This is a feature that is usually appreciated by our customers.
Additionally, the automation features have been impactful, particularly in the deployment phase, as we use what Dataiku calls deployer nodes. Dataiku primarily enhances the speed at which our customers can develop or train their machine learning models since it is a drag-and-drop platform. Our clients can easily drag and drop components and use them on the spot.
What needs improvement?
There is room for improvement in terms of allowing for more code-based features. I would love for Dataiku to allow more flexibility with code-based components and provide the possibility to extend it by developing and integrating custom components easily with existing ones.
For how long have I used the solution?
I have been working with Dataiku for about three years.
What's my experience with pricing, setup cost, and licensing?
I find the pricing of Dataiku quite affordable for our customers, as they are usually large companies. However, it is a pricey solution and I primarily recommend it to bigger companies.
Which other solutions did I evaluate?
I researched products like Dataiku, Cloudera, and Databricks.
What other advice do I have?
I would give Dataiku an eight out of ten. Although I generally recommend Dataiku, it is mainly suited for companies that can afford it as it is a pricey solution.
Collaboration and traceability boost team's efficiency
What is our primary use case?
I use that IQ since I am preparing cohorts for health investment research.
What is most valuable?
Traceability and collaboration are essential for me. I have eight or nine engineers working together. Integration with machine learning is also crucial for us.
Additionally, traceability is vital since I manage many cohorts, and collaboration is key as I have multiple engineers substituting for one another.
What needs improvement?
I need more experience in the sector, which is health. The license is very expensive. It would be great to have an intermediate license for basic treatments that do not require extensive experience.
For how long have I used the solution?
I have used the solution for six or seven years.
What do I think about the scalability of the solution?
The solution is scalable. I rate it nine out of ten.
How are customer service and support?
The customer service team is helpful and responsive, more or less on time. I rated them seven out of ten.
How would you rate customer service and support?
Neutral
How was the initial setup?
Deployment should take four or five hours, yet customization takes more time.
What about the implementation team?
Two or three engineers took part in the installation process.
What was our ROI?
I do not care about financial benefits, however, I am sure they exist. It has supported our compliance with industry regulations one hundred percent.
What's my experience with pricing, setup cost, and licensing?
There are no extra expenses beyond the existing licensing cost.
Which other solutions did I evaluate?
What other advice do I have?
The user interface is useful for collaborative tools that allow multiple professionals to work together.
I rate the overall product as eight out of ten.
The platform organizes workflows visually and efficiently
What is our primary use case?
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.
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
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