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Dataiku Data Science Studio OverviewUNIXBusinessApplication

Dataiku Data Science Studio is #8 ranked solution in top Data Science Platforms. PeerSpot users give Dataiku Data Science Studio an average rating of 10 out of 10. Dataiku Data Science Studio is most commonly compared to Databricks: Dataiku Data Science Studio vs Databricks. Dataiku Data Science Studio is popular among the large enterprise segment, accounting for 72% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a computer software company, accounting for 23% of all views.
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What is Dataiku Data Science Studio?

Dataiku Data Science Studio is the collaborative data science software platform for teams of data scientists, data analysts, and engineers to explore, prototype, build, and deliver their own data products more efficiently.

Dataiku Data Science Studio is also known as Dataiku DSS. This solution enables you to discover, share, and reuse code and applications so that you can deliver high-quality projects easily and streamline your path to production. As an enterprise leader, you can leverage the power of AI to confidently make business decisions.

With Dataiku, an intuitive interface is guaranteed and allows users the ability to access and work with data using a point-and-click method. Dataiku analyzes the data to suggest key transformations. Beyond offering 109 data transformation capabilities, Dataiku also includes pipelines that can be generated in SQL which can thereafter be scheduled for automated recomputation.

What's more, Dataiku allows you to create more than 20 different kinds of charts and also gives you the ability to deploy them into dashboards or create custom web applications for the use of interactive and sophisticated visualization tools.

In addition, with Dataiku you have the option of using an in-depth statistical analysis, including but not limited to: curves fitting, univariate and bivariate analysis, principal component analysis, correlation analysis, and statistical tests.

Dataiku Data Science Studio Consists Of:

  • Data preparation
  • Visualization
  • Machine Learning
  • Data Ops
  • ML Ops
  • Analytic Apps

With Dataiku Data Science Studio You Can:

  • Integrate any data 10x faster
  • Build and automate sophisticated data pipelines
  • Build and share insights in minutes
  • Perform in-depth statistical analysis
  • Create thousands of models to find the best ones
  • Explore and explain models

Dataiku Data Science Studio Benefits and Features:

  • Use your favorite languages and tools: You can create code working with tools you are already familiar with in the language you prefer (Python, R, SQL, etc.)
  • Easily reuse and share code: This feature helps you reduce inefficiencies and inconsistent data. Dataiku includes project libraries, allowing teams to centralize and share code. Although it comes pre-loaded with starter code for tasks, it also provides you with the ability to add your own code snippets.
  • Simplify complexities related to connecting to data and configuring computer resources: With this feature, data scientists can execute code in both a containerized and distributed way, while also selecting the runtime environment they want. Dataiku works to maintain those containers as well as shut them down when the job is completed.

Features Users Find Most Valuable:

  • API
  • Reporting/Analytics
  • Third-Party Integrations
  • Data Import/Export
  • Natural Language Processing
  • Search/Filter
  • Monitoring
  • Workflow Management

Reviews from Real Users

IT Central Station users note that Dataiku Data Science Studio has a fantastic interface and is also flexible, intuitive, and stable. One user said "I like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person." Another user mentioned “The best feature is the user interface. It allows us to see the visual flows.”

Dataiku Data Science Studio was previously known as Dataiku DSS.

Dataiku Data Science Studio Customers

BGL BNP Paribas, Dentsu Aegis, Link Mobility Group, AramisAuto

Dataiku Data Science Studio Video

Archived Dataiku Data Science Studio Reviews (more than two years old)

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ManuelaGhomsi - PeerSpot reviewer
Business Intelligence Developer/ Data Scientist at a tech services company with 11-50 employees
Real User
User interface is colorful, beautiful, and well-designed but sometimes the solution can be slow
Pros and Cons
  • "I like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person."
  • "I find that it is a little slow during use. It takes more time than I would expect for operations to complete."

What is our primary use case?

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.

What is most valuable?

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.

What needs improvement?

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.

For how long have I used the solution?

I have been using this solution for a year.
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June 2022
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610,229 professionals have used our research since 2012.

What do I think about the stability of the solution?

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.

What do I think about the scalability of the 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.

How are customer service and support?

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

Which solution did I use previously and why did I switch?

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.

How was the initial setup?

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

What about the implementation team?

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

What other advice do I have?

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.

Which deployment model are you using for this solution?

Private Cloud

If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

Amazon Web Services (AWS)
Disclosure: My company has a business relationship with this vendor other than being a customer: Partner.
Giuseppe-Naldi - PeerSpot reviewer
Principal Full Stack Data Scientist at ICTeam
Real User
Good data preparation tools and integrates well with BigQuery
Pros and Cons
  • "The most valuable feature is the set of visual data preparation tools."
  • "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."

What is our primary use case?

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

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

How has it helped my organization?

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. 

What is most valuable?

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.

What needs improvement?

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.

For how long have I used the solution?

I have been using this solution for one year.

What do I think about the stability of the solution?

The stability is quite good.

What do I think about the scalability of the solution?

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.

How are customer service and technical support?

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.

Which solution did I use previously and why did I switch?

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.

How was the initial setup?

The initial setup is very, very simple.

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

What about the implementation team?

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

What's my experience with pricing, setup cost, and licensing?

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.

What other advice do I have?

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.

Which deployment model are you using for this solution?

On-premises
Disclosure: My company has a business relationship with this vendor other than being a customer: Partner.
Buyer's Guide
Data Science Platforms
June 2022
Find out what your peers are saying about Dataiku, Alteryx, Databricks and others in Data Science Platforms. Updated: June 2022.
610,229 professionals have used our research since 2012.
Practice Manager Data Intelligence at a tech services company with 1,001-5,000 employees
Real User
An all-in-one data prediction tool that is simple to install and use
Pros and Cons
  • "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."
  • "The ability to have charts right from the explorer would be an improvement."

What is our primary use case?

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

What is most valuable?

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. 

What needs improvement?

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.

For how long have I used the solution?

I have been using this solution for two years.

What do I think about the stability of the solution?

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

What do I think about the scalability of the solution?

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.

How are customer service and technical support?

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.

How was the initial setup?

The initial setup of this solution is straightforward.

What about the implementation team?

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

What other advice do I have?

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.

Which deployment model are you using for this solution?

On-premises
Disclosure: My company has a business relationship with this vendor other than being a customer: Reseller.
it_user837543 - PeerSpot reviewer
Senior Business Technology Analyst at a consultancy with 5,001-10,000 employees
Real User
GUI-based functionality is easy to use, but server up-time needs to be improved
Pros and Cons
  • "Extremely easy to use with its GUI-based functionality and large compatibility with various data sources. Also, maintenance processes are much more automated than ever, with fewer errors."
  • "Cloud-based process run 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."
  • "Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."
  • "There were stability issues: 1) SQL operations, such as partitioning, had bugs and showed wrong results. 2) Due to server downtime, scheduled processes used to fail. 3) Access to project folders was compromised (privacy issue) with wrong people getting access to confidential project folders."

What is our primary use case?

ETL development.

How has it helped my organization?

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.

What is most valuable?

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

What needs improvement?

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

For how long have I used the solution?

One to three years.

What do I think about the stability of the solution?

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.

What do I think about the scalability of the solution?

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

How are customer service and technical support?

I would rate tech support at six out of 10.

Which solution did I use previously and why did I switch?

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.

How was the initial setup?

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

What's my experience with pricing, setup cost, and licensing?

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

Which other solutions did I evaluate?

Unfortunately, no.

What other advice do I have?

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

Disclosure: I am a real user, and this review is based on my own experience and opinions.
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