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A game changing data science platform
What do you like best about the product?
It's flexibility. I can code and I like that I can create recipes that uses code to process my data. However, I enjoy having the ability to just select a visual recipe and quickly apply transformations without writing lines of coding. This keeps my skills fresh and gives me a productivity boost when I need to deliver quickly. I use the platform on a daily basis and it forms part of my core tools to develop my projects. As a big organisation, we have our own internal support to deal with issues. However, I've attended a meet up and a conference in London and met the UK team. It was an amazing experience and they offered me a lot of support showcasing new features and facilitating the contact with the Dataiku user community. Another aspect that I enjoy is the seamlessly integration with our current data systems. Using Dataiku, I can connect with all of our data sources and develop projects that weren't even possible before.
What do you dislike about the product?
My only dislike about it is the cost. Although I think it delivers what it promises, the cost is a huge barrier within my organisation. I would like to have more of our analysts with access to a designer licence. That would empower them by developing new skills. Today, only a few data scientist (including myself) and a couple of analysts have full access to the tool.
What problems is the product solving and how is that benefiting you?
When I joined the company, we didn't have a ETL manager. I used to write my SQL queries and build the connections using Power Query. The process was cumbersome and used to take a lot of hours to make small progress. With Dataiku, I now can create my models, schedule the refreshes, save the data in a centralised repository and just expose it to my data visualisation tools (Power BI). Another issue was having the compute power to process our data. I work in the energy industry and our data is all half-hourly. With Dataiku, I can use spark on EKS and process huge amount of data in just a fraction of the time I used to.
Best end-to-end ML platform
What do you like best about the product?
Users of any technical ability can jump in and become self-sufficient. Our team has power excel users to python/R coders and we can all use this platform and be productive. Amazing self-learning materials and reference examples. Best version of an "ML Studio" that allows more control over experiment design.
What do you dislike about the product?
Setting up the roles for users and the seemingly endless discussions between IT and the business om who can have what rights and what the end user should be allowed to do within the system.
What problems is the product solving and how is that benefiting you?
Standardizing our forecasting models across regions and analysts
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.
A robust, complete, and highly customizable platform!
What do you like best about the product?
Dataiku has several great features. For me, the most important ones are the model version control, which allows you to track and compare different implementations, making it much easier to retrain and deploy models. Another key feature is the customizable recipes, especially in Python, a widely used language in data science. This brings great flexibility, along with numerous visually intuitive tools within the platform, enabling you to implement your code seamlessly within a data pipeline.
What do you dislike about the product?
I’m not sure if I would point out something I don’t like about Dataiku, but areas for improvement would be the statistical analysis of data within the platform. Sometimes, you might want to perform a test on a column, but the process for graphical visualization either includes only a subset of the data or requires a long path to get there.
What problems is the product solving and how is that benefiting you?
Dataiku is a comprehensive end-to-end platform, which makes it easy to ingest data and manage the entire pipeline until it is consumed by machine learning models. This is especially true for real-time models, where data can arrive through an endpoint, be processed, and then inserted into the model for inference.
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.
Dataiku enables all data users to seamlessly collaborate with data engineers and data scientists
What do you like best about the product?
How to makes back end complex data engineering work so much easier to understand
What do you dislike about the product?
More intuitive for end users, especially people with no coding background
What problems is the product solving and how is that benefiting you?
Reduces time and efforts required to collaborate with different parties across the entire data science ecosystem
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.
Integration with multiple platforms enhances capabilities for diverse data applications
What is our primary use case?
My primary use case for Dataiku is for data science, Gen AI, and data science applications. Our AGN team also uses it for various purposes.
What is most valuable?
Dataiku is highly regarded as it is a leader in the Gartner ranking. It offers most of the capabilities required for data science, MLOps, and LLMOps. Integration with public cloud and multiple other platforms is excellent. The product is easy to install and can be maintained by a single expert. It supports good functionalities that are essential in data visualization and responsible AI.
What needs improvement?
Dataiku's pricing is very high, and commercial transparency is a challenge. Support is also an area needing improvement. More features like LLM security, holographic encryption, and enhanced GPU integration would be beneficial.
For how long have I used the solution?
I have been familiar with Dataiku for the past four to five years.
What was my experience with deployment of the solution?
I have not encountered any deployment issues. It is very easy to install.
What do I think about the stability of the solution?
I have not used Dataiku at the level that would allow me to comment on performance latency for a Big Bang environment. However, the product is good, and the output meets our expectations.
What do I think about the scalability of the solution?
Dataiku is fully scalable, and I have not identified any limitations regarding scalability so far.
How are customer service and support?
The technical support from Dataiku is not good. The support team does not provide adequate assistance, and there are concerns about billing requests.
How would you rate customer service and support?
Negative
Which solution did I use previously and why did I switch?
There are many products available in the market like Converge.io, Domino Data Lab, and ClearML. Dataiku's pricing is not competitive with these solutions.
How was the initial setup?
The initial setup of Dataiku is very easy. A single person, if experienced, can handle the installation and maintenance.
What was our ROI?
Without a reduction in price, I doubt users will see a return on investment. The market is competitive, and Dataiku must adopt a consumption-based model instead of the current monthly model.
What's my experience with pricing, setup cost, and licensing?
The pricing for Dataiku is very high, which is its biggest downside. The model they follow is not consumption-based, making it expensive.
Which other solutions did I evaluate?
There are many products in the market like Converge.io, Domino Data Lab, and ClearML.
What other advice do I have?
Overall, Dataiku is a very good product except for the commercial aspect and the support. More features like LLM security and holographic encryption would be appreciated. I would rate the technical support three out of ten due to its current inefficacy. For pricing, on a scale of one to ten, where ten is expensive, I rate it around eight to nine. I rate the overall solution a 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?
Other
Drag-and-drop platform accelerates model development with distributed compute engine
What is our primary use case?
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.
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.
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Other
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.
Which deployment model are you using for this solution?
Private Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
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