Dataiku Reviews

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.

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.

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.

My company sells licenses for both Dataiku and Alteryx, and we have clients who use them. I engage with several companies in telecommunications, retail, and energy to assess how our clients are utilizing these platforms.

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.

There is room for improvement in terms of allowing for more code-based features. I would love for Dataiku to allow more flexibility with code-based components and provide the possibility to extend it by developing and integrating custom components easily with existing ones.

I have been working with Dataiku for about three years.

I find the pricing of Dataiku quite affordable for our customers, as they are usually large companies. However, it is a pricey solution and I primarily recommend it to bigger companies.

I researched products like Dataiku, Cloudera, and Databricks.

I would give Dataiku an eight out of ten. Although I generally recommend Dataiku, it is mainly suited for companies that can afford it as it is a pricey solution.