Dataiku for Enterprise AI (Non U.S. Markets)

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.

Hybrid Cloud

Amazon Web Services  (AWS )

What do you like best about the product?

I find the platform very easy to use, which makes it great for quickly prototyping and getting your MVP out as soon as possible. It's also simple to plug and play, which really speeds up the process.

What do you dislike about the product?

I find the documentation somewhat incomplete, with few tutorials available. It can be a struggle to find solutions when I need help.

What problems is the product solving and how is that benefiting you?

Both MVP and end-to-end approaches allow for rapid use case development, but when it comes to building large-scale, scalable solutions with real impact, the process can be more challenging.

What do you like best about the product?

SImple to use & scale. Flexibity & integrated well into the any infra.

What do you dislike about the product?

The main drawbacks of Dataiku is cost, scalability limitations, integration complexity, performance issues, and the need for user training.

What problems is the product solving and how is that benefiting you?

Dataiku addressed critical issues in data quality, operational efficiency, analytics collaboration, AI scalability, compliance, and business-user empowerment, serving as a unified platform for enterprise data innovation and value generationtion.

What do you like best about the product?

Dataiku is excellent for managing the entire data pipeline from data preparation to machine learning and deployment. The best part is it easy to implement. The best part is how it allows both technical and non-technical users to collaborate on the same platform. Visual workflows make it easy to build projects without heavy coding, while advanced users can still dive deep with Python, R, or SQL. The integration with cloud platforms and version control is also very smooth.

What do you dislike about the product?

The platform can feel heavy for smaller projects, and the initial learning curve is a bit steep for beginners. Also, the licensing costs can be high for small companies or startups.

What problems is the product solving and how is that benefiting you?

For me, Dataiku mainly solves the problem of collaboration between technical and non-technical teams. Earlier, a lot of time used to get wasted when data scientists, analysts, and business teams worked separately and had to constantly exchange files and reports. With Dataiku, we can all work on the same platform data cleaning, model building, and visualization happen in one place. It also saves me from doing repetitive manual tasks since a lot of workflows can be automated. Overall, it has made our data projects faster, more transparent, and easier to manage.

What do you like best about the product?

It brings together data people analysts, engineers, scientists on one platform

What do you dislike about the product?

honestly, is that it can feel a bit heavy and slow, especially on large projects with a lot of visual recipes or datasets.

What problems is the product solving and how is that benefiting you?

Collaboration gaps, i think it brings data scientists, analysts, engineers, and business users into one shared workspace