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.

View full review »

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.

View full review »

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.

View full review »

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.

View full review »

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.

View full review »

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.

View full review »

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.

View full review »

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.

View full review »

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.

View full review »

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.

View full review »

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.

View full review »

The best features Dataiku offers include the ability for users to use the node without having to code and the functionality related to low-code/no-code.

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.

View full review »

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. View full review »

The advantage is that you can focus on machine learning while having access to what they call 'recipes.' These recipes allow me to preprocess and prepare data without writing any code. This saves a lot of time because I can quickly handle all the data preparation tasks and concentrate on building my machine learning algorithms.

View full review »

It's got good feature selection and creation of feature stores, and it also gives different aspects of modeling approaches. There are a lot of similarities with DataRobot. 

So feature selection, different modeling, and financial metrics are good aspects. 

View full review »

Data Science Studio's data science model is very useful.

View full review »

The best feature is the user interface. It allows us to see the visual flows. 

The intuitiveness of the software itself is very good. 

It has both click or code flexibility, which is quite useful. I already have coding knowledge, so I go for coding as my go-to method of interaction. However, for a normal person that may not have a background in coding, the visual lists are really great. It makes it easy for non-IT individuals to navigate everything.

The pricing is very good.

The solution is quite stable.

You can scale the solution well.

View full review »

I like the interface, which is probably my favorite part of the solution. It is really user-friendly. I might think that it is user-friendly because I'm in IT and it seems familiar to me. It is colorful and I think it is really beautiful and well-designed.

View full review »

The most valuable feature is the set of visual data preparation tools. 

The solution supports code from different languages including Python and R. Whatever code you want to use, it works well.

This solution allows us to store and retrieve data directly into BigQuery.

The documentation and tutorials are quite good.

View full review »

The most valuable feature of this solution is that it is one tool that can do everything, and you have the ability to very easily push your design to prediction. 

View full review »

• Process scheduler (called Scenario).
• Cloud-based process run, which helps in not keeping the systems on while processes are running.

View full review »