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Dataiku review
What do you like best about the product?
Dataiku is user friendly and easy to learn. You can combine lots of data resources into one simple platform. I frequently use this in data analytics at my company.
What do you dislike about the product?
I would suggest more open training online to help others see the possibilities of using Dataiku.
What problems is the product solving and how is that benefiting you?
It helps me to combine all of my data into one central location
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Dataiku for Advanced Analytics
What do you like best about the product?
Dataiku excels at creating a centralized platform for democratizing data for every user to collaborate regardless of their skill level. The platform is suitable for coders and non-coders, developers and business users, etc. Each release contains new Gen AI, machine learning or analytics capabilities and allows you to connect to source systems, build data workflows, interactive visualizations and automate the entire end-to-end life cycle.
What do you dislike about the product?
I think the user documentation can be challenging to sift through, I wish information was collated within one repository instead of many different links.
What problems is the product solving and how is that benefiting you?
Dataiku allows me to build end-to-end solutions leveraging big data at scale.
Great platform for practical EDA and ML purposes
What do you like best about the product?
You'd be hard pressed to create a platform with greater ease of use than Dataiku. Moving around the UI while performing data analytics or when showcasing the work performed by the team is seamless / effortless. New features also come out regularly and are constantly improving the platform. Ease of implementing the platform with other tools, such as Snowflake and Power BI, is also much appreciated.
What do you dislike about the product?
Projects can get unruly once they become large enough. This can be alleviated by creating many flow zones.
What problems is the product solving and how is that benefiting you?
Forecasting customer demand using data ingesting, prep, and time series analysis has been super valuable.
A game changing data science platform
What do you like best about the product?
It's flexibility. I can code and I like that I can create recipes that uses code to process my data. However, I enjoy having the ability to just select a visual recipe and quickly apply transformations without writing lines of coding. This keeps my skills fresh and gives me a productivity boost when I need to deliver quickly. I use the platform on a daily basis and it forms part of my core tools to develop my projects. As a big organisation, we have our own internal support to deal with issues. However, I've attended a meet up and a conference in London and met the UK team. It was an amazing experience and they offered me a lot of support showcasing new features and facilitating the contact with the Dataiku user community. Another aspect that I enjoy is the seamlessly integration with our current data systems. Using Dataiku, I can connect with all of our data sources and develop projects that weren't even possible before.
What do you dislike about the product?
My only dislike about it is the cost. Although I think it delivers what it promises, the cost is a huge barrier within my organisation. I would like to have more of our analysts with access to a designer licence. That would empower them by developing new skills. Today, only a few data scientist (including myself) and a couple of analysts have full access to the tool.
What problems is the product solving and how is that benefiting you?
When I joined the company, we didn't have a ETL manager. I used to write my SQL queries and build the connections using Power Query. The process was cumbersome and used to take a lot of hours to make small progress. With Dataiku, I now can create my models, schedule the refreshes, save the data in a centralised repository and just expose it to my data visualisation tools (Power BI). Another issue was having the compute power to process our data. I work in the energy industry and our data is all half-hourly. With Dataiku, I can use spark on EKS and process huge amount of data in just a fraction of the time I used to.
Best end-to-end ML platform
What do you like best about the product?
Users of any technical ability can jump in and become self-sufficient. Our team has power excel users to python/R coders and we can all use this platform and be productive. Amazing self-learning materials and reference examples. Best version of an "ML Studio" that allows more control over experiment design.
What do you dislike about the product?
Setting up the roles for users and the seemingly endless discussions between IT and the business om who can have what rights and what the end user should be allowed to do within the system.
What problems is the product solving and how is that benefiting you?
Standardizing our forecasting models across regions and analysts
Dataiku is a great ML Flow and ML OPs tool all the way from a beginner to an expert
What do you like best about the product?
I started using Dataiku as a junior data analyst. The visual recipes have turned around how you built an analytics project from end to end. As I started tackling complex projects and started expanding my knowledge of data science and the domain I am working on, I started to discover the latitudes of capabilities that I can adopt from dataiku tools and api. It has immensely helped me to expedite my career goals. Another fantastic aspect would be the consistent upgradation of the features and tools like Data quality management, LLM mesh and Agentic AI in the studio which becomes an inspiration for me to tryout and implement additional steps (in the ML flow) that helps me increase business value in the projects I am working on. I enrolled in the dataiku academy too.
What do you dislike about the product?
As I described dataiku is fantastic to start with as a beginner but as the project gets more complex, as I started using dataiku apis in python I started feel a lack of detail in the documentation availability. For example, I wish that the dataiku apis for python to have a clearer documentation as we can observe in some libraries like pytorch, Scikit learn, Scipy or plotly. Details like all the parameters available for a specific function and additional parameters which can be used with an example or two explaining what each parameter mean for implementing of the function. The documentation currently available is highly limited in helping me understand the complete capabilities of a specific function or api. So, my best resource for referral often is the blog post answers that the dataiker provides. With gratitude I would request the team to improve the documentation to such an extent it would add value to an experienced ML Ops developer.
What problems is the product solving and how is that benefiting you?
Dataiku simplifies ML Flow and ML Ops process which enables me to focus on data preparation, building models, validating them and implementation. I would like to appreciate the availability of dataiku functionality through dataiku apis which makes it easier of me to create and deploy projects just with python.
A robust, complete, and highly customizable platform!
What do you like best about the product?
Dataiku has several great features. For me, the most important ones are the model version control, which allows you to track and compare different implementations, making it much easier to retrain and deploy models. Another key feature is the customizable recipes, especially in Python, a widely used language in data science. This brings great flexibility, along with numerous visually intuitive tools within the platform, enabling you to implement your code seamlessly within a data pipeline.
What do you dislike about the product?
I’m not sure if I would point out something I don’t like about Dataiku, but areas for improvement would be the statistical analysis of data within the platform. Sometimes, you might want to perform a test on a column, but the process for graphical visualization either includes only a subset of the data or requires a long path to get there.
What problems is the product solving and how is that benefiting you?
Dataiku is a comprehensive end-to-end platform, which makes it easy to ingest data and manage the entire pipeline until it is consumed by machine learning models. This is especially true for real-time models, where data can arrive through an endpoint, be processed, and then inserted into the model for inference.
Rapid AI with DataIku
What do you like best about the product?
DataIku allows me to quickly train and evaluate multiple models on given data. The results immediately reveal dominant features. This allows me to better understand the data that I'm dealing with.
DataIku accepts many data formats and sources.
DataIku accepts many data formats and sources.
What do you dislike about the product?
In DataIku, Chart styling is not as intuitive as I'd expect. Feedback loops are not allowed/implemented.
DataIku does not allow CTEs in sql queries, which has consequential unwanted data processing.
DataIku does not allow CTEs in sql queries, which has consequential unwanted data processing.
What problems is the product solving and how is that benefiting you?
DataIku speeds up my selection of AI strategies and dominant feature discovery.
Dataiku enables all data users to seamlessly collaborate with data engineers and data scientists
What do you like best about the product?
How to makes back end complex data engineering work so much easier to understand
What do you dislike about the product?
More intuitive for end users, especially people with no coding background
What problems is the product solving and how is that benefiting you?
Reduces time and efforts required to collaborate with different parties across the entire data science ecosystem
Data pipeline and machine learning done easily
What do you like best about the product?
Dataiku helps you abstract from pipelining focus on creating value-adding data products.
What do you dislike about the product?
I dislike the most the lack of visibility over what is happening in the backend and, particularly, the price.
What problems is the product solving and how is that benefiting you?
Demand forecasting: we have built the forecasting pipeline for half a million SKUs in three months.
Price determination engine: we have automated pricing for a subset of SKUs, all made easy since we could abstract from pipelining.
Price determination engine: we have automated pricing for a subset of SKUs, all made easy since we could abstract from pipelining.
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