Dataiku Reviews

Our primary uses for this solution are data preparation and data modeling.

We have a testing environment, a production environment, and two API nodes.

Using Dataiku has meant that we spend less time on preparing and cleaning data, and we spend less time on blending models together. Ultimately, it means that we can spend more time modeling. 

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.

From an administrative point of view, I would like to be able to communicate with the users who are logged into the system. For example, I would like to be able to send a broadcast message that says "I am shutting down the system."

I would like to see more organization and better cohesion within the tool.

In the next release of this solution, I would like to see deep learning better integrated into the tool and not simply an extension or plugin.

I would like to have a better way to manage images and sound.

The error messages are not self explanatory and can sometimes be difficult to understand.

I have been using this solution for one year.

The stability is quite good.

This is a scalable solution where we integrate with BigQuery for storing and retrieving our data. There are only two of us in the company who use this solution, although we would like to increase our usage.

The technical support is quite good. We have had to open a few tickets and they replied in just a few minutes. They are quick and supportive.

I still use several other solutions for data science including RapidMiner 9 and Weka. I have also been using Octave and MATLAB for modeling.

I use the Community Editions for these other products, so I have restrictions when it comes to things like the size of the dataset. When I need to be free of restrictions then I use Dataiku Data Science Studio.

The initial setup is very, very simple.

To deploy the entire platform will take one or two days.

We handled the deployment ourselves. We can work totally independently.

The annual licensing fees are approximately €20 ($22 USD) per key for the basic version and €40 ($44 USD) per key for the version with everything.

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.

I just use the product for data migration. Once a month, I push data from one Redshift data warehouse to another Redshift data warehouse. I do that by using a simple SQL query.

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.

I think the interface is very nice, but for somebody who is not as familiar with IT as I am, it may be much more difficult for them. It is nice for me because I'm familiar with this type of software that falls in the realm of the data science platform. I can see how a client who really doesn't know anything about IT or computers might try to use it and find that it would be a little difficult to access some features. That type of user may really need training in order to work with Dataiku. So, in the next release of Dataiku DSS (Data Science Studio), they should make it more friendly for everybody to use, not just IT people. 

For me, I find that it is a little slow during use. When I use Dataiku to run my script to transfer data, it takes more time than I would expect for the operation to complete.

I have been using this solution for a year.

I have not done a lot of exploration into Dataiku and its other features as I only use it for a particular task. But from my experiences and from the review I'm seeing online, it is a stable solution.

I think there is some room for improvement in scalability as I already find it performs a little slowly. In my company, there are three of us. There is an IT manager, there is the data warehouse manager, and there is me. In the company that we use it for, there are more than 800 employees.

I have contacted customer service before. They respond quickly, so that is a plus.

We did not use other solutions before switching so much as we are using a few different solutions together. We use Knime and Alteryx for data science and analytics. I just use Dataiku for data migration for now. I just started using Knime, but I'm most familiar with Alteryx because I have used it for one-and-a-half-years to practice ATL (Active Template Library) on my data. Alteryx is somewhat the same as Knime, but it is more user-friendly.

The company I work for did the setup for me. It was already set up when I came.

We are consultants so we do the implementations ourselves. Another consultant introduced Dataiku to us initially. I guess they really appreciated the solution.

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.

I use this solution to make predictions from time-series data. I am a consultant and operate this solution for my clients.

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. 

I would like to have better exclusion of data capability.

The ability to have charts right from the explorer would be an improvement.

I would like to see additions to the architecture for specific business use cases.

I have been using this solution for two years.

This is a stable solution. I have not seen any bugs or glitches.

This platform is easy to scale. You can take full advantage of your office architecture.

My client has more than twenty people using this solution.

I have been in contact with technical support through the website.  They have chatbots that dispatch tickets to specific people, depending on the problem. They respond quickly and I am satisfied with the support.

The initial setup of this solution is straightforward.

I performed the implementation myself. By reading the documentation, it is simple.

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.

ETL development.

Compared to Informatica, this tool is extremely easy with its GUI-based functionality and large compatibility with various data sources. Also, maintenance processes are much more automated than ever, with fewer errors.

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

Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable.

There were stability issues.

• SQL operations, such as partitioning, had bugs and showed wrong results.
• Due to server downtime, scheduled processes used to fail.
• Access to project folders was compromised (privacy issue) with wrong people getting access to confidential project folders.

Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days).

I would rate tech support at six out of 10.

I have previously worked with Alteryx. I switched for these reasons: 

• Big Data tools like Python and Hive are already supported
• Cloud-based processing
• Better value for money for client.

Initial setup was done by the IT team from the client's side. No involvement in the initial setup from our side.

Since this was a client-advised tool, no information of costs is available to us.

Unfortunately, no.

Easy-to-use tool, but slightly unstable in terms of a few modules. Proceed with some amount of caution and knowledge.