I wouldn't recommend using Dataiku if only one data scientist is on the team. However, having a larger team—let's say more than five data scientists—can be very helpful. Dataiku offers features that are especially useful when multiple people are working on the same project, and it also has tools that make it easier to move from the proof of concept stage to production. Overall, I rate the solution as seven out of ten.
Overall, I would rate it an eight out of ten, except for some coding things that are there, which some people may not want to do, like certain business data scientists. Dataiku is good for multiple teams' collaboration. If many teams are collaborating and sharing Jupyter notebooks, it's very useful. It has a good data processing structure and includes most of the models. I haven't checked the large language models in it yet, but it's a pretty good tool. It does well with analytics and has a sound structure on the back end. Some coding aspects are necessary, but it generates SQL code, which is an added feature. A lot of data engineers like Dataiku because it generates SQL code on the right side.
While I started using version 5.1 of the solution, I've currently updated it to 7.0.1. I would recommend the solution. It's affordable and user-friendly. Overall, I'd rate it nine out of ten. I'd rate it higher, however, I don't have enough experience with other similar solutions, therefore, it's hard to compare.
Business Intelligence Developer/ Data Scientist at a tech services company with 11-50 employees
Real User
2019-12-04T05:40:00Z
Dec 4, 2019
Dataiku is a very broad solution that offers many possibilities. If you want to use it you must be fully committed to it. The biggest lesson I learned from using the product is that you can do many things with it. But you must commit the time to discover the tool. On a scale from one to ten where one is the worst and ten is the best, I would rate Dataiku as a seven. It is a little bit of a conservative rating because it is a nice solution and I just use it for a particular task.
At the moment, we haven't had any need to use containers or Spark because everything is included in BigQuery. My advice for anybody who is implementing this solution is to start with having somebody who can mentor you. Whether this is the case or not, the tutorial and documentation are quite good, so I would suggest going through the whole tutorial and academy material. This solution does have a learning curve, although it is not steep. I would rate this solution an eight out of ten.
Practice Manager Data Intelligence at a tech services company with 1,001-5,000 employees
Real User
2019-11-13T05:29:00Z
Nov 13, 2019
My advice to anybody who is implementing this solution is to use the tutorial first. There are lots of tutorials available that help to quickly explain the solution. This is a product that I recommend. I would rate this solution an eight out of ten.
Dataiku Data Science Studio is acclaimed for its versatile capabilities in advanced analytics, data preparation, machine learning, and visualization. It streamlines complex data tasks with an intuitive visual interface, supports multiple languages like Python, R, SQL, and scales efficiently for large dataset handling, boosting organizational efficiency and collaboration.
I wouldn't recommend using Dataiku if only one data scientist is on the team. However, having a larger team—let's say more than five data scientists—can be very helpful. Dataiku offers features that are especially useful when multiple people are working on the same project, and it also has tools that make it easier to move from the proof of concept stage to production. Overall, I rate the solution as seven out of ten.
Overall, I would rate it an eight out of ten, except for some coding things that are there, which some people may not want to do, like certain business data scientists. Dataiku is good for multiple teams' collaboration. If many teams are collaborating and sharing Jupyter notebooks, it's very useful. It has a good data processing structure and includes most of the models. I haven't checked the large language models in it yet, but it's a pretty good tool. It does well with analytics and has a sound structure on the back end. Some coding aspects are necessary, but it generates SQL code, which is an added feature. A lot of data engineers like Dataiku because it generates SQL code on the right side.
I rate Dataiku Data Science Studio nine out of 10.
While I started using version 5.1 of the solution, I've currently updated it to 7.0.1. I would recommend the solution. It's affordable and user-friendly. Overall, I'd rate it nine out of ten. I'd rate it higher, however, I don't have enough experience with other similar solutions, therefore, it's hard to compare.
Dataiku is a very broad solution that offers many possibilities. If you want to use it you must be fully committed to it. The biggest lesson I learned from using the product is that you can do many things with it. But you must commit the time to discover the tool. On a scale from one to ten where one is the worst and ten is the best, I would rate Dataiku as a seven. It is a little bit of a conservative rating because it is a nice solution and I just use it for a particular task.
At the moment, we haven't had any need to use containers or Spark because everything is included in BigQuery. My advice for anybody who is implementing this solution is to start with having somebody who can mentor you. Whether this is the case or not, the tutorial and documentation are quite good, so I would suggest going through the whole tutorial and academy material. This solution does have a learning curve, although it is not steep. I would rate this solution an eight out of ten.
My advice to anybody who is implementing this solution is to use the tutorial first. There are lots of tutorials available that help to quickly explain the solution. This is a product that I recommend. I would rate this solution an eight out of ten.