Automating end-to-end data pipelines has boosted team productivity and simplified analytics
What is our primary use case?
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.
What is most valuable?
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.
What needs improvement?
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.
For how long have I used the solution?
I have been using Dataiku for four years now.
What do I think about the stability of the solution?
What do I think about the scalability of the solution?
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.
How are customer service and support?
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.
Which solution did I use previously and why did I switch?
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.
How was the initial setup?
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.
What about the implementation team?
We have a direct link with Dataiku; we did not purchase it through the AWS Marketplace.
What was our ROI?
I cannot share the numbers regarding return on investment.
What's my experience with pricing, setup cost, and licensing?
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.
Which other solutions did I evaluate?
Before choosing Dataiku, I evaluated other options, specifically Databricks.
What other advice do I have?
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.
Which deployment model are you using for this solution?
Hybrid Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
Visual workflows have streamlined daily ETL analysis and support collaborative project work
What is our primary use case?
My main use case for Dataiku involves ETL pipelines, mainly for data analysis, and I majorly use SQL queries for that.
For ETL pipelines and data analysis, I had to create the output by combining a few datasets and then running SQL queries, applying filters, joining the tables, and so on; so I used Dataiku for that.
Regarding my main use case with Dataiku, I primarily use it for analysis only, and the visual recipes of Dataiku and the SQL query are enough for that. No challenges have occurred so far, but the only challenge is that Dataiku gets slow sometimes and lags a lot.
What is most valuable?
The best features Dataiku offers in my experience are its visual recipes, which are very easy to use for analysis.
The visual recipes are easy and useful for my analysis because the Sync recipe is very useful if I want to download a table from the cloud into the Dataiku database and schema. Other recipes such as the Prepare recipe are also very useful since you don't have to write code; it's all visual and very easy to use. Recipes such as Stack are also very useful as you don't have to write full SQL code for it, allowing you to speed up the process.
Dataiku has positively impacted my organization since we use it majorly for our day-to-day work, and it is very helpful in creating and managing ETL pipelines to create a project flow, making it easy to go back to any step and then make edits if some changes occur.
What needs improvement?
I have no suggestions for improvements because it's all good; it just sometimes lags a lot, and I don't know if the server is full or what, but it sometimes takes a lot of time while loading and refreshing the page.
No additional thoughts on improvements have come to mind, but the performance can be more optimized to reduce the waiting time. Dataiku is down a lot of times, and we have to wait for sometimes five, ten, or fifteen minutes, after which it gets working again, and during those times, we are unable to get our work done.
For how long have I used the solution?
I have been using Dataiku for four years, so my experience with it is quite extensive.
What do I think about the stability of the solution?
Dataiku is stable for most of the time, but for around ten percent of the day, it is usually down, and we are unable to do work on it.
What do I think about the scalability of the solution?
Dataiku's scalability is good.
How are customer service and support?
I have never needed the requirement for customer support from Dataiku.
Which solution did I use previously and why did I switch?
I have been using Dataiku for the last four years, and I have not used any other solution besides Dataiku.
What was our ROI?
It is a good return on investment since it helps save a lot of time, and it's easy for my teammates to work cross-functionally on the same project.
Which other solutions did I evaluate?
I did not evaluate other options before choosing Dataiku because it was all managed by my organization, so I had to use Dataiku only.
What other advice do I have?
My advice for others looking into using Dataiku is that it's a good software, and I would suggest them to keep using it since it's a very good tool for data analysis uses.
I have no additional thoughts about Dataiku; it's all very good for the use cases, but if the performance can be improved to be more stable with lesser lags, it would be much better. I would rate my overall experience with Dataiku an 8 out of 10.
Visual workflows have streamlined healthcare analytics and have reduced reporting time significantly
What is our primary use case?
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.
What is most valuable?
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.
What needs improvement?
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.
For how long have I used the solution?
I have been using Dataiku for close to three and a half years.
What do I think about the stability of the solution?
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.
What other advice do I have?
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.
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?
Amazon Web Services (AWS)
Reusable preparation workflows have transformed recurring datasets and automate end to end projects
What is our primary use case?
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 is most valuable?
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.
What needs improvement?
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.
For how long have I used the solution?
I have been using Dataiku for one year and a half, and I already obtained my Core Dataiku certification about a month ago.
What's my experience with pricing, setup cost, and licensing?
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.
Which other solutions did I evaluate?
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.
What other advice do I have?
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.
Dataiku:A plug in tool for Data Science
What do you like best about the product?
What I like most about Dataiku is how it brings the entire data workflow into one place. It allows teams to easily prepare data, build machine learning models, and deploy them without switching between multiple tools. The visual interface makes it easy to understand data pipelines, while still allowing advanced users to write code when needed. This balance between visual tools and coding flexibility makes collaboration between data scientists, analysts, and engineers much smoother. It helps teams move faster from raw data to real insights and production-ready models.
What do you dislike about the product?
One thing I dislike about Dataiku is that it can feel a bit heavy and complex, especially when working with very large datasets or many workflows. Sometimes the interface becomes slower, and managing multiple projects can get confusing. Also, while the visual tools are helpful, certain advanced customizations still require coding, which might be challenging for non-technical users. Overall, it’s a powerful platform, but there is a bit of a learning curve when you first start using it.
What problems is the product solving and how is that benefiting you?
Dataiku helps solve the problem of managing the entire data and machine learning workflow in one platform. Instead of using separate tools for data preparation, analysis, model building, and deployment, Dataiku brings everything together. This makes it easier to organize projects, track data pipelines, and collaborate with other team members.
For me, it has been helpful because it simplifies the process of turning raw data into useful insights and models. It also improves collaboration between technical and non-technical teams, since analysts can use the visual interface while data scientists can still write code when needed. Overall, it helps speed up the development process and makes data projects more structured and easier to manage.
Dataiku: User-Friendly Collaboration Across the Full Data Lifecycle
What do you like best about the product?
What I like most about Dataiku is its user-friendly interface and strong collaboration features. It makes it easy for data scientists, analysts, and engineers to work together on the same projects. I also appreciate that it supports the full data lifecycle, from data preparation to machine learning and deployment.
What do you dislike about the product?
One thing I dislike about Dataiku is that it can be quite demanding on system resources, especially when I’m working with large datasets. In addition, some of the more advanced features come with a learning curve, so it can take time to fully understand how to use them effectively.
What problems is the product solving and how is that benefiting you?
Dataiku addresses the challenge of fragmented data workflows by bringing data preparation, analysis, machine learning, and deployment together in a single platform. It also makes it easier for teams to collaborate and automate key processes. For me, this translates into time savings, better productivity, and data projects that are simpler to manage end to end.
Centralized, Organized Data Platform with Powerful AutoML and Integrations
What do you like best about the product?
Dataiku demonstrates a satisfactory environment where data is centralized and organized
The program supports both coders and non coders, allowing them to use data in their different levels
Dataiku has a successful data lifecycle, something that collects, ingest, prepare and even analyze data
The program consists of an inbuilt Auto ML tools that speed u most of the operations
Dataiku has extensible APIs and plugins, all supporting success integrations
What do you dislike about the product?
Dataiku has challenges in cost management and estimating, where small companies fail to secure the app
The app demands extensive computer resources, something that amplifies the infrastructure costs
What problems is the product solving and how is that benefiting you?
Dataiku ensure solid data collaboration, where analysts, engineers and even business players access data in a centralized environment
Most of complex data workflows are significantly supported by this app, ensuring that no manual code needed to conduct a specific task
The presence of machine learning and AI support s the effectiveness of data processing and analysis
The app accommodates both technical and non technical users due to it’s effectiveness and simplicity
A Unified Platform That Bridges Data Experts and Business Teams Seamlessly
What do you like best about the product?
Its greatest strength is enabling true collaboration between data experts and business teams on a single platform. It seamlessly bridges technical work like coding and ML engineering with visual and no-code interfaces. This breaks down silos, accelerates project delivery and ensures AI solutions are built with crucial business context, making them more impactful and sustainable.
What do you dislike about the product?
For smaller teams or simpler projects, Dataiku will be premium. The platform's extensive features come with inherent complexity, which can lead to a steeper learning curve. Its pricing model is often seen as enterprise-focused, potentially making it less accessible for startups or individual users who don't need its full collaborative scale.
What problems is the product solving and how is that benefiting you?
Dataiku solves the critical challenges of fragmented data science workflows. It provides a unified, collaborative platform that connects data preparation, experimentation and deployment into one governed environment. This directly benefits us by drastically reducing project lead times, improving model governance and reproducibility and enabling both technical and business users to contribute effectively to data-driven outcomes.
End-to-End Data Science Platform That Makes Collaboration Easy
What do you like best about the product?
What I like best about Dataiku is its end-to-end data science and machine learning platform that brings data preparation, analysis, model building, and deployment into a single environment. The visual workflows combined with code-based options make it accessible for both technical and non-technical users. It also supports strong collaboration between data scientists, analysts, and business teams, which helps speed up model development and improve decision-making.
What do you dislike about the product?
While Dataiku is a powerful platform, it can feel complex for first-time users because of its extensive feature set. The initial setup and learning curve may take time, especially for non-technical users. In some cases, performance can slow down when handling very large datasets, and the pricing structure may not be ideal for smaller teams or limited use cases.
What problems is the product solving and how is that benefiting you?
It's solves the challenge of managing the entire data science and machine learning lifecycle in one platform. It brings together data preparation, analysis, model development, deployment, and monitoring, reducing the need for multiple disconnected tools. This benefits me by improving collaboration between teams, speeding up model development, and making it easier to turn data into actionable insights while maintaining consistency and governance across projects.
Effortless Data Collaboration with Robust Features
What do you like best about the product?
I like that Dataiku lets me handle data projects and build machine learning models by pulling in data from different sources, cleaning and organizing it, and experimenting with models all in one place. The combination of a visual interface with coding options makes it accessible for both technical and non-technical team members, smoothing out data project management. I love how it reduces repetitive tasks, decreases mistakes, and keeps complex projects organized and running smoothly. It's great that everyone on the team can contribute, no matter their technical skills, making data work easier and less stressful.
What do you dislike about the product?
One thing I’ve noticed about Dataiku is that it can feel a bit overwhelming at first because there are so many features and options. Working with really large datasets or complex workflows can sometimes be a little slow. I also think it could be a bit easier for new users to get started. Overall, it’s a great tool, but a little more guidance and smoother performance would make it even better.
What problems is the product solving and how is that benefiting you?
I use Dataiku to streamline data projects by integrating data sources, cleaning data, and building models in one platform. It allows team collaboration regardless of technical skills, saves time on repetitive tasks, reduces mistakes, and keeps complex projects organized.
