Dataiku Reviews

Dataiku is an AI platform that we use for oil and gas exploration. Even though I can't provide specific details, this is the primary use case for us.

One of the valuable features of Dataiku is the workflow capability. It allows us to organize a workflow efficiently. The platform has a visual interface, making it much easier for educated professionals to organize their work. This feature is useful because it simplifies tasks and eliminates the need for a data scientist. If you are knowledgeable about AI, you can directly write using primitive tools like Pantera flow, PyTorch, and Scikit-learn. However, Dataiku makes this process much easier.

One area for improvement is the need for more capabilities similar to those provided by NVIDIA for parallel machine learning training. We still encounter some integration issues.

We have been using Dataiku for three years.

Customer service is somewhat different because Dataiku partners with local industry experts who understand the business better and provide support. It can be challenging to determine the provider of better support, however, overall, the support is good.

We are only using Dataiku.

I believe the return on investment looks positive.

I considered another option that excels in parallel processing. However, it falls short in other areas. No product is perfect. If these two solutions worked together, it would be advantageous. Unfortunately, one has strengths in certain areas while the other excels in another.

Why not? BHP sold the energy part to a company called Woodside. It has changed because they are now part of Woodside. 

Overall, I rate the product eight out of ten.

I use that IQ since I am preparing cohorts for health investment research.

Traceability and collaboration are essential for me. I have eight or nine engineers working together. Integration with machine learning is also crucial for us. 

Additionally, traceability is vital since I manage many cohorts, and collaboration is key as I have multiple engineers substituting for one another.

I need more experience in the sector, which is health. The license is very expensive. It would be great to have an intermediate license for basic treatments that do not require extensive experience.

I have used the solution for six or seven years.

The solution is scalable. I rate it nine out of ten.

The customer service team is helpful and responsive, more or less on time. I rated them seven out of ten.

Neutral

Deployment should take four or five hours, yet customization takes more time.

Two or three engineers took part in the installation process.

I do not care about financial benefits, however, I am sure they exist. It has supported our compliance with industry regulations one hundred percent.

There are no extra expenses beyond the existing licensing cost.

I work with other tools but mainly with Dataiku, and I also use Python and Azure Synapse.

The user interface is useful for collaborative tools that allow multiple professionals to work together. 

I rate the overall product as eight out of ten.

Private Cloud

Microsoft Azure

My main use case for Dataiku involves ETL pipelines, mainly for data analysis, and I majorly use SQL queries for that.

For ETL pipelines and data analysis, I had to create the output by combining a few datasets and then running SQL queries, applying filters, joining the tables, and so on; so I used Dataiku for that.

Regarding my main use case with Dataiku, I primarily use it for analysis only, and the visual recipes of Dataiku and the SQL query are enough for that. No challenges have occurred so far, but the only challenge is that Dataiku gets slow sometimes and lags a lot.

The best features Dataiku offers in my experience are its visual recipes, which are very easy to use for analysis.

The visual recipes are easy and useful for my analysis because the Sync recipe is very useful if I want to download a table from the cloud into the Dataiku database and schema. Other recipes such as the Prepare recipe are also very useful since you don't have to write code; it's all visual and very easy to use. Recipes such as Stack are also very useful as you don't have to write full SQL code for it, allowing you to speed up the process.

Dataiku has positively impacted my organization since we use it majorly for our day-to-day work, and it is very helpful in creating and managing ETL pipelines to create a project flow, making it easy to go back to any step and then make edits if some changes occur.

I have no suggestions for improvements because it's all good; it just sometimes lags a lot, and I don't know if the server is full or what, but it sometimes takes a lot of time while loading and refreshing the page.

No additional thoughts on improvements have come to mind, but the performance can be more optimized to reduce the waiting time. Dataiku is down a lot of times, and we have to wait for sometimes five, ten, or fifteen minutes, after which it gets working again, and during those times, we are unable to get our work done.

I have been using Dataiku for four years, so my experience with it is quite extensive.

Dataiku is stable for most of the time, but for around ten percent of the day, it is usually down, and we are unable to do work on it.

Dataiku's scalability is good.

I have never needed the requirement for customer support from Dataiku.

I have been using Dataiku for the last four years, and I have not used any other solution besides Dataiku.

It is a good return on investment since it helps save a lot of time, and it's easy for my teammates to work cross-functionally on the same project.

I did not evaluate other options before choosing Dataiku because it was all managed by my organization, so I had to use Dataiku only.

My advice for others looking into using Dataiku is that it's a good software, and I would suggest them to keep using it since it's a very good tool for data analysis uses.

I have no additional thoughts about Dataiku; it's all very good for the use cases, but if the performance can be improved to be more stable with lesser lags, it would be much better. I would rate my overall experience with Dataiku an 8 out of 10.

My main use case for Dataiku is data science and AI projects.

We used Dataiku for a demand forecasting project where the objective is to forecast the demand for each product for the next three months.

The best features Dataiku offers include the ability for users to use the node without having to code and the functionality related to low-code/no-code.

Dataiku has positively impacted my organization by allowing non-technical users to adapt a data science project and to maintain a part of a data science project.

I think a pain point related to Dataiku is the visualization, which is not straightforward, and the integration, which is also not straightforward for non-technical users.

To improve Dataiku, the company could enhance the capabilities related to integration and visualization.

I have been using Dataiku for three years.

Dataiku is stable.

Dataiku's scalability can be better.

I have never tried Dataiku's customer support.

Before, we used a solution that I cannot mention, but the change is more related to using a more straightforward solution for non-technical users.

Before choosing Dataiku, I evaluated KNIME.

I have not seen any specific outcomes or metrics such as time saved, reduced costs, or improved project delivery.

I have not seen a return on investment with Dataiku in terms of time saved, money saved, or fewer employees needed.

I am not the person involved in the process regarding pricing, setup cost, and licensing.

My advice to others looking into using Dataiku is to use it principally to help and support non-technical users.

Dataiku is deployed in my organization on a public cloud on Amazon Web Services.

Amazon Web Services is our cloud provider.

I am not the person involved in the process of determining whether we purchased Dataiku through the AWS Marketplace.

My review rating for Dataiku is 7.

Public Cloud

Amazon Web Services (AWS)

My primary use case for Dataiku is for data science, Gen AI, and data science applications. Our AGN team also uses it for various purposes.

Dataiku is highly regarded as it is a leader in the Gartner ranking. It offers most of the capabilities required for data science, MLOps, and LLMOps. Integration with public cloud and multiple other platforms is excellent. The product is easy to install and can be maintained by a single expert. It supports good functionalities that are essential in data visualization and responsible AI.

Dataiku's pricing is very high, and commercial transparency is a challenge. Support is also an area needing improvement. More features like LLM security, holographic encryption, and enhanced GPU integration would be beneficial.

I have been familiar with Dataiku for the past four to five years.

I have not encountered any deployment issues. It is very easy to install.

I have not used Dataiku at the level that would allow me to comment on performance latency for a Big Bang environment. However, the product is good, and the output meets our expectations.

Dataiku is fully scalable, and I have not identified any limitations regarding scalability so far.

The technical support from Dataiku is not good. The support team does not provide adequate assistance, and there are concerns about billing requests.

Negative

There are many products available in the market like Converge.io, Domino Data Lab, and ClearML. Dataiku's pricing is not competitive with these solutions.

The initial setup of Dataiku is very easy. A single person, if experienced, can handle the installation and maintenance.

Without a reduction in price, I doubt users will see a return on investment. The market is competitive, and Dataiku must adopt a consumption-based model instead of the current monthly model.

The pricing for Dataiku is very high, which is its biggest downside. The model they follow is not consumption-based, making it expensive.

There are many products in the market like Converge.io, Domino Data Lab, and ClearML.

Overall, Dataiku is a very good product except for the commercial aspect and the support. More features like LLM security and holographic encryption would be appreciated. I would rate the technical support three out of ten due to its current inefficacy. For pricing, on a scale of one to ten, where ten is expensive, I rate it around eight to nine. I rate the overall solution a ten.

Public Cloud

Other

We use the solution for data science and machine learning.

We were a team of six Dataiku scientists and one data engineer. We focused on fully leveraging Dataiku for all our data science-related tasks. This included data preparation, preprocessing, benchmarking machine learning algorithms, handling everything related to production, and making our algorithms available to stakeholders.

The advantage is that you can focus on machine learning while having access to what they call 'recipes.' These recipes allow me to preprocess and prepare data without writing any code. This saves a lot of time because I can quickly handle all the data preparation tasks and concentrate on building my machine learning algorithms.

One of the main challenges was collaboration. Developers typically use GitHub to push and manage code, but integrating GitHub with Dataiku was complicated. While it was theoretically possible to use GitHub with Dataiku, in practice, it was difficult to manage our code effectively and push it from Dataiku to GitHub.

Another limitation was its ability to handle different types of data. While Dataiku is powerful for working with structured data, like regular or geospatial data, it struggled with more complex data types such as text and image. In addition to the challenges with GitHub integration, the limited support for diverse data types was another feature lacking at that time.

I have been using Dataiku for over a year.

Since Dataiku relies on various open-source libraries and tools, updates or upgrades to these components can sometimes impact the stability of Dataiku's features. This can make it challenging to maintain consistent stability, as changes in the underlying open-source tools can affect how Dataiku functions.

I rate the stability as six out of ten.

There are some scalability issues.

I rate the scalability as seven out of ten.

Technical support was very good compared to other tools. We had access to chat and support.

Positive

The initial setup is very easy. It has many tutorials and many guidelines. After the initial deployment, it took about a week to manage all the setup and resolve various issues before we had a stable version of Dataiku that we could use consistently.

I rate it as eight out of ten, whereas ten is easy.

It is very expensive.

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.

On-premises

My current client has Dataiku. We do sentiment analysis and some small large language models right now. We use Dataiku as a Jupyter Notebook. 

We use it a lot for marketing and analytics. The marketing and sales team uses Dataiku.

It's got good feature selection and creation of feature stores, and it also gives different aspects of modeling approaches. There are a lot of similarities with DataRobot. 

So feature selection, different modeling, and financial metrics are good aspects. 

The no-code/low-code aspect, where DataRobot doesn't need much coding at all. 

Dataiku still needs some coding, and that could be a difference where business data scientists would go for DataRobot more than Dataiku because you still have to code and use either Python or R, or Scala. However, with DataRobot, you don't have to do that at all.

I've used Dataiku for about four years.

The company is based in France. But they're more and more in America as well.

So, the support was okay.

Positive

I used DataRobot. Dataiku has a different kind of structure to it. It's not financially heavy like DataRobot, which caters more to financial companies, like banks. Dataiku doesn't have that yet. I think they are also working on that area. But yeah, there are some key differences between the two products.

DataRobot has an additional feature with financial firms that it creates all these financial metrics when you run a time series analysis. Those things I have not seen in Dataiku.

If any financial company is choosing between DataRobot and Dataiku, they will definitely go for DataRobot because it creates all these financial metrics. It creates deltas, time series, time difference fields, and things like that. So, that is an added feature that DataRobot has.

Pricing is pretty steep. Dataiku is also not that cheap. It depends on the client and how much they want to spend towards a tool.

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.

The use case is data science, and we've deployed Data Science Studio in multiple regions for four environments: dev, preset, pre-production, and production.

The solution responds to the requirements of the business team.

Data Science Studio's data science model is very useful.

I think it would help if Data Science Studio added some more features and improved the data model.

We've been using Data Science Studio for three years.

Data Science Studio is stable.

Data Science Studio is horizontally scalable.

Setting up Data Science Studio was simple, and deployment took about three months. You only need around three people to deploy and maintain.

I rate Dataiku Data Science Studio nine out of 10. 

On-premises