Dataiku Reviews

My main use case for Dataiku is mostly based on the client's data where we look into life sciences data, mostly aligned to claims, campaign measurement, campaign targeting, IQVIA, LAD Epsilon data, and Komodo for instance.

Apart from this, I'm basically working on creating an end-to-end pipeline as a bundled unit project, which has been the overall case. We primarily work on Next Best Engagement and Next Best Actions, more or less aligned to the healthcare side, while sometimes working on the consumer front and on the professionals front, meaning healthcare professionals (HCP).

A specific project I built in Dataiku was on HCP campaign measurement. Our day-to-day cycle involves ingestion of data from our S3, which is the client's S3 storage. We fetch the data, perform some visual recipes to bring it onto Dataiku DSS, make preliminary changes, preprocess the data, do some data preparation, and perform feature engineering to have the final model ready dataset for modeling.

We create multiple iterations of the model where Dataiku is of great help, allowing us to try multiple modeling iterations with different hyperparameters, saving a lot of time and providing a visual overview for everyone to understand how the data is performing. Once the modeling is done, we push the data downstream through an API or use MLOps for productionization, either via CI/CD pipeline or just simple scenario triggers such as sending an email once a job gets done. This primarily results in our day-to-day activity.

The best features Dataiku offers include primarily the visual recipes, which ease data preparation greatly. It is very easy now to handle small tasks where you need to understand the shape of data; instead of writing a query, you can just use a visual recipe to create the views. You can also have multiple intermediate views, which is significantly helpful for larger streams, especially during reverse engineering.

Additionally, the automation piece and scenario triggering has been a boon for me, as my projects often involve weekly or monthly reporting. Everything is set up so that we just need a human in the loop to ensure everything follows properly, with time-based triggers automatically generating and sending reports to stakeholders.

Furthermore, the integration capabilities and the ability for multiple team members to access the same projects concurrently enhance collaboration, making it quite beneficial for data scientists such as myself as we progress in our careers.

Dataiku has positively impacted my organization, specifically in one project where we performed migration from AWS to Dataiku, speeding up the solution by close to 40%. We completed tasks that used to take 10 days in just four days. Moreover, the architecture costs associated with AWS were reduced by almost 70%, which was a significant benefit and greatly impacted our operations. This success has enabled us to pitch Dataiku to clients, who have actively incorporated it into their daily work streams, resulting in a win-win situation.

The 70% cost reduction and 40% faster delivery came primarily from the ease of use in how we were creating architecture. Since we were migrating, we leveraged the opportunity to improve and enhance the architecture. The earlier AWS architecture was hampered by multiple services leading to high costs, but moving to Dataiku streamlined everything into one platform. Consequently, the delivery time for generating reports for stakeholders decreased from 10 days to three to four days.

In terms of improvement, I cannot comment on the LLMs or the agentic view as I have not used them yet. However, I feel that better documentation is necessary. Dataiku should establish a stronger community since this is proprietary software, where users can share knowledge. Although they have some community interaction, it is often challenging to find assistance when stuck.

For example, when I was new to Dataiku and trying to use an external optimization tool such as CPLEX, I struggled with resource directory linking to a project's notebook. Detailed documentation and community discussions could have significantly alleviated these issues for users such as myself.

I have been using Dataiku for close to three and a half years.

There were a few challenges, but they were not from Dataiku's standpoint in terms of technicality; they were more related to the rapid updates where we currently work on version 10.2, and soon, we are on version 11.4, requiring things to be redone. The support for earlier projects created on the older version is something the team could look at, as it would help if there was a backup proposition in place to avoid hampering our work due to updates.

While I do not have a particular feature that surprised me, I found the plugins available in Dataiku to be very helpful. Not only can users leverage existing plugins, but we can also create our plugins based on the rules we use daily. This feature is quite handy and extends beyond just individual projects, as published plugins can be used by everyone across the board.

My advice to others considering Dataiku is to utilize the visual recipes, as they can significantly expedite project creation. Although the fundamental processes remain the same, leveraging elements in visual recipes can enhance efficiency, making it easier than writing code for basic queries, resulting in quicker execution. Dataiku encompasses everything from visualization to integrations and sharing the results, so once you dive in, it is important to familiarize yourself with the available features and make the most out of them.

I would rate this product a 9 out of 10.

My usual use cases for Dataiku are mostly data science use cases, or model creation and training.

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.

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.

I have been working with Dataiku for four years.

As for stability and reliability, so far so good; after the installation, I really had no problems. Dataiku works very well.

Dataiku is quite scalable, as long as I can pay for more licenses, there is no technical limitation.

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.

Positive

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.

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.

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.

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.

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.

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.

My main use case for Dataiku is general; I create ETL pipelines and then automate everything using that, along with ML modeling. These are the major use cases that I have for Dataiku. On a daily basis, we use Dataiku for ad hoc analysis for following the product lifecycle.

For one of my use cases with Dataiku, we are using it where the data resides in Snowflake and the expectation is to orchestrate and automate a complete CI/CD pipeline, with the final data residing on S3. In between, there are multiple logics and transformations that we have to build in. Along with that, we are supposed to do all the DQ checks, data quality framework, and data governance. We automated everything using Dataiku, and now the project is live, with overall efficiency being very good.

Automating that workflow with Dataiku increased the overall productivity of the team compared to the tasks that we used to do earlier using other ETL tools. Dataiku has optimized that, and data visualization became easy. The checkpoints that Dataiku provides, such as analyzing the data and finding the outliers, became easy, and sharing the data sets became easy as well. Now, with the visual recipes, even people who can't code can also do the transformation, so overall, it is a good tool.

We have created a few visual recipes that are not only limited to the project; we created a package so that they don't have to code that part of logic again and again. We have provided them as a recipe, which is a good thing.

The best features Dataiku offers include the data analysis part, the ETL, and the overall orchestration part. We can create a recipe and share it with others without having to code that again and again, and we can create an application and a dashboard in one single place. These are the very good features of Dataiku.

I find myself and my team relying the most on the data analysis part of Dataiku. We use it to visualize the data, find the outliers, and it helps us very well.

Dataiku has positively impacted my organization as most of our projects have been migrated to Dataiku, and now people are relying on it as a go-to tool for all our data use cases. This migration has led to measurable improvements, as most of the projects have been migrated and the overall efficiency has increased. Most people who used to do tasks manually are now working on automating that.

Dataiku can be improved from the dashboard perspective because right now it is very restricted, and I feel that can be improved. API integration and other aspects can also be enhanced, but I am pretty impressed with the rest of it.

I have been using Dataiku for four years now.

Dataiku is stable.

Dataiku's scalability is pretty good; I can scale the projects very easily, and clear guidance is given as well. I have no issues with that.

I need to stress upon the part about customer support because there are some product issues we have identified and raised with customer support, but sometimes the response is delayed, so that can be improved.

I previously used a different solution before Dataiku, and the other solution was not cloud-based; they were local, which made the license cost higher.

My experience with pricing, setup costs, and licensing is good because that was managed by my IT team, and overall it was seamless with clear guidance given.

We have a direct link with Dataiku; we did not purchase it through the AWS Marketplace.

I cannot share the numbers regarding return on investment.

My experience with pricing, setup costs, and licensing is good because that was managed by my IT team, and overall it was seamless with clear guidance given.

Before choosing Dataiku, I evaluated other options, specifically Databricks.

My advice to others looking into using Dataiku is to first understand the product, which is very important. You should first see what your use case is, what Dataiku is offering, and understand that it is a tool meant not only for coders but also for higher management, as they can do drag and drop to easily perform transformations without needing to write code. Dataiku is a tool for everyone. I would rate this product an 8.5 out of 10.

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.

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.

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.

I have been using Dataiku for the last two years, but in data science, my experience spans more than ten years.

I have not experienced downtimes, crashes, or glitches with Dataiku in the project that we implemented.

Dataiku appears scalable, and in the project that we implemented, we did not face any scalability issues.

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.

Positive

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.

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.

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.

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.

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.

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.

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.

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.

I have been using this product for four years.

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.

For support, I haven't created any support tickets, so I really don't know about it, but it is quite good.

Positive

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.

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.

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.

Overall, Dataiku is really good. I would rate it an 8 out of 10.

My main use of Dataiku is especially data preparation. I use Dataiku a lot for preparing my data, in particular processing and transforming my datasets by using recipes, creating recipes, and especially what I really value is being able to reuse the recipes already created for preparation on another dataset.

I was preparing for my Core Dataiku certificate, and all of the modules were focused on data preparation. I load the data into Dataiku, then I use the recipes and tools to add columns, unpivot columns, delete, and transpose columns so I can format them. Then I create groups of recipes and I also reuse them by importing another dataset into Dataiku, which gives me the ability to save time. I don't have to redo all the previous processes since I already have a recipe for data preparation that I can reuse.

After data preparation, I had the opportunity to carry out an end-to-end project with Dataiku. This involved first the data preparation and then I went on to set up a model to predict a stock index. I used the machine learning models for this project.

Let's suppose I have datasets that I use every time, and each time I'm going to check the data formats to format a certain number of columns, for example dates, to see if they are in date format or not, delete certain columns, rename certain columns, transform the data, and clean them. If I've done all these steps once and I manage to put all these recipes into a group, next time it's an enormous time saver not to have to repeat these steps one by one, but to use directly the recipe or the group of recipes created.

I want to emphasize again the recipes and how we can reuse them with Dataiku. In most data projects, data preparation takes a huge amount of time for professionals, and sometimes unfortunately we repeat the same tasks. Dataiku really brings a solution to this in that we can create groups of recipes or recipes that we can reuse. The reuse of recipes is already a significant advantage. Additionally, Dataiku is one of the rare platforms that offers end-to-end services where we can carry out a data project from start to finish. For example, to carry out my personal project on predicting a stock index, I did practically everything on Dataiku, from data preparation to putting my model into production.

For example, we have an Excel file that will be incremented every month, a file that comes from outside where the format of the file is exactly the same. Each time we do the transformation, but we did it only on the first file. When the others come in, we apply the old recipes. This allowed us to save an enormous amount of time because we were able to automate everything with Dataiku. As soon as the file comes in, the recipes are automatically applied to these files. We no longer have to intervene at the end of each month and spend twenty to thirty minutes cleaning the files. Additionally, it ensures the validation of our data and consistency between the files.

What I especially prefer about Dataiku are the recipes for data preparation and also the feature we have to create groups of recipes and also to be able to reuse them again.

Once you get the hang of Dataiku, learning the features is also intuitive. It's really a very intuitive platform.

Dataiku is very scalable. It can easily adapt to the expansion of our datasets and it is very powerful. If we have more and more data, Dataiku is very scalable.

Currently, Dataiku is a platform that is almost perfect, and I don't see how to improve it further. I don't have suggestions for potential improvements.

Maybe on the interface in general, the information can easily get lost. If we could summarize the tools bar in a more organized way than what we currently have, that would be helpful.

I have been using Dataiku for one year and a half, and I already obtained my Core Dataiku certification about a month ago.

The licenses are a bit high for companies that are still hesitating to get started with using Dataiku. For my personal projects, I used the thirty-day free trial. Regarding my company, I did not have access to this pricing information.

Given our needs, the best tool, despite the licenses and the cost of the license, Dataiku turns out to be by far the favorite tool compared to the others.

Dataiku is really a very intuitive platform. It allows you to carry out data projects from end to end. We also have the opportunity to reuse templates, models, and recipes. That's one of the big advantages of using Dataiku.

In the context of my personal projects, I developed a pleasure in using Dataiku, which is not the case for other tools. Because the platform is intuitive, I can easily guide myself through it.

I find the documentation on Dataiku very informative and also very instructive.

I would tell others to go for it if Dataiku truly meets their needs. It's the best offer on the market with good documentation. My overall rating for Dataiku is nine out of ten.

My main use case for Dataiku is for data science and AI projects. I use Dataiku for a demand forecasting use case where the objective is to predict the demand for each product for the next four months. Demand forecasting is the primary focus where I use Dataiku.

The best features Dataiku offers that help me with my demand forecasting and data science projects include having a complete overview of the flow directly from the flowchart, allowing me to observe all the steps in a single overview, and the ability to use a no-code, low-code node.

Having that flow overview and the no-code, low-code nodes makes my work easier by allowing me to use a simple function without coding directly, meaning I can avoid using Python. In 80% of the project, we are using Python, but for very simple steps, we also use a low-code, no-code node, which can be simpler for users that are not technical and may want to do some preprocessing steps.

Dataiku has positively impacted my organization, but it is a tool that is very similar to others and it helps for what I mentioned before and not for other areas. The ability to use low-code or no-code nodes is more a convenience in that case, mainly for a non-technical user. We deliver this kind of solution for a client where the user is not so technical, and for this reason, it is better to have this kind of flow and tool.

To improve Dataiku, it could enhance its visualization features, as it is not possible in Dataiku to create direct visualizations or to integrate a web app directly or in a simpler way as it is possible for a preprocessing step. Visualization and integration are the main areas I would like to see enhanced.

In my experience, Dataiku can be more stable.

I have been using Dataiku for two years.

In my experience, Dataiku can be more stable.

Dataiku's scalability is not one of the best solutions to scale.

We used a lot of other solutions before Dataiku and we switched only so that non-technical users can improve and maintain this kind of flow.

My advice to others looking into using Dataiku is to use it for a simple flow in data science and to teach how to make a data science project or flow for non-technical users. I would rate this product a 7 out of 10.

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.

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.

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.

We have been using Dataiku for three years.

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.

Positive

We are only using Dataiku.

I believe the return on investment looks positive.

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.

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.

I use that IQ since I am preparing cohorts for health investment research.

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.

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.

I have used the solution for six or seven years.

The solution is scalable. I rate it nine out of ten.

The customer service team is helpful and responsive, more or less on time. I rated them seven out of ten.

Neutral

Deployment should take four or five hours, yet customization takes more time.

Two or three engineers took part in the installation process.

I do not care about financial benefits, however, I am sure they exist. It has supported our compliance with industry regulations one hundred percent.

There are no extra expenses beyond the existing licensing cost.

I work with other tools but mainly with Dataiku, and I also use Python and Azure Synapse.

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.

We used Dataiku for a demand forecasting project where the objective is to forecast the demand for each product for the next three months.

Dataiku has positively impacted my organization by allowing non-technical users to adapt a data science project and to maintain a part of a data science project.

To improve Dataiku, the company could enhance the capabilities related to integration and visualization.

Before choosing Dataiku, I evaluated KNIME.

I have not seen a return on investment with Dataiku in terms of time saved, money saved, or fewer employees needed.

Dataiku is deployed in my organization on a public cloud on Amazon Web Services.

Amazon Web Services is our cloud provider.

I am not the person involved in the process of determining whether we purchased Dataiku through the AWS Marketplace.

My review rating for Dataiku is 7.