Dataiku Reviews and Ratings

Rating: 7.8 out of 10

Score

7.8 out of 10

Community insights

TrustRadius Insights for Dataiku are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.

Pros

Versatile Data Handling: Users have praised Dataiku DSS for its versatility in handling various data sources, including Python, R, SQL, and built-in tools. Some reviewers found this ability to transform unorganized data into valuable information through intuitive dashboards to be a crucial feature.

Manageable Data Pipelines: The presence of inbuilt recipes in Dataiku DSS has made data pipelines more manageable for users. This modular approach to pipeline creation and the availability of pre-built recipes for data transformation have been appreciated by several reviewers.

Ease of Use: Many users have highlighted the ease of use of Dataiku DSS. The platform's inclusion of all majorly applied operations as direct 'recipes' and the visual flow element that helps users keep track of their work intuitively are some factors that contribute to its user-friendly nature.

Reviews

4 Reviews

Dataiku DSS - One-Stop Solution for All Data Science Applications

Rating: 10 out of 10

Incentivized

November 9, 2021

Use Cases and Deployment Scope

Dataiku DSS is being used in my team to perform various tasks which ranges from data preprocessing to machine learning model creation. It provides a one-stop solution to fetch data from different sources such as Amazon S3, SQL Server databases, etc. and merge them onto a single platform. We use Dataiku DSS to perform data imputations, data cleaning and feature engineering to prepare datasets for creating machine learning models. We also extract business insights (data analytics) using various statistical methods and visual representations such as scatter plots, histograms, boxplots, etc. Furthermore, optimized ML models are created which are used to predict/forecast target variables and drive business decisions.

Pros

Allows users to collaborate and monitor individual tasks
Caters to both types of analysts, coders and non-coders, alike
Integrate graphs and plots with visualization tools such as Tableau

Cons

Its community support is very limited at the moment
Complex to integrate with automation tools such as Blue Prism

Likelihood to Recommend

Dataiku DSS is very well suited to handle large datasets and projects which requires a huge team to deliver results. This allows users to collaborate with each other while working on individual tasks. The workflow is easily streamlined and every action is backed up, allowing users to revert to specific tasks whenever required.

While Dataiku DSS works seamlessly with all types of projects dealing with structured datasets, I haven't come across projects using Dataiku dealing with images/audio signals. But a workaround would be to store the images as vectors and perform the necessary tasks.

Verified User

Analyst in Customer Service (5001-10,000 employees)

Vetted Review

2 years of experience

Dataiku DSS: Click or code--the choice is yours!

Rating: 9 out of 10

Incentivized

September 28, 2020

Use Cases and Deployment Scope

I work for a client who's implementing all their data science solutions on Dataiku DSS. I have been working on implementing these solutions for my client for over a year now. Currently, it is channeled through the IT department and we have expanded across multiple departments ranging from finance to sales. We have implemented multiple time series forecasting projects, NLP, business optimization as well as customized analytics data flow for the finance department.

Pros

The intuitiveness of this tool is very good.
Click or Code - If you are a coder, you can code. If you are a manager, you can wrangle with data with visuals
The way you can control things, the set of APIs gives a lot of flexibility to a developer.

Cons

The integrated windows of frontend and backend in web applications make it cumbersome for the developer.
When dealing with multiple data flows, it becomes really confusing, though they have introduced a feature (Zones) to cater to this issue.
Bundling, exporting, and importing projects sometimes create issues related to code environment. If the code environment is not available, at least the schema of the flow we should be able to import should be.

Likelihood to Recommend

I would recommend it because it's an amazing tool for different levels of users. From Business Analysts to Data Scientists to Managers, various employees can make use of this tool to make data-driven decisions. I'm not sure about where it would be less appropriate as I'm using it as Data Scientist and so far it pretty much caters to my need.

Devesh Singh

Data Scientist II in Information Technology at Alumnus Software Limited (51-200 employees)

Vetted Review

1 year of experience

View profile

Low-Code Open-Source Data Analytics Platform!

Rating: 8 out of 10

Incentivized

July 1, 2020

Use Cases and Deployment Scope

Platform is currently being used solely by our department due to licensing cost and budget reasons.

Platform offers a one stop shop for an (almost) end to end development of data analytics and machine learning products, including data import, manipulation, and visualization. It’s a low-code tool, and supports majority of workflow without the need for in-depth coding skills; this is a plus for exposing platform across a wider audience and use cases.

Pros

Low-code platform.
Open source version includes most valuable modules.
User friendly documentation.

Cons

End product deployment.

Likelihood to Recommend

For team(s) with varying levels of coding skills, the platform offers a one size fit all for most data analytics and machine learning projects that are of practical use in industrial settings (e.g. time series forecasting, predictive maintenance and production optimization). In research and development work, where projects are cutting edge and no out of the box solutions are available, then platform is of minimal use, since custom data ingestion and manipulation may be required.

Verified User

Engineer in Corporate (10,001+ employees)

Vetted Review

1 year of experience

Dataiku - a complete Data Analytic and AI/ML solution

Rating: 7 out of 10

Incentivized

June 30, 2020

Use Cases and Deployment Scope

Dataiku is being used as the integrated data analytic/AI/ML platform. It is a corporate-level standard solution, across multiple regions and business domains. The data scientists use this platform to develop various data pipelines, and/or train the AI/ML models, verify the model performances and eventually deploy the model as service to benefit business critical IT applications (majorly serve the predictive analysis/automation and integration with RPA).

Pros

Very intuitive and easy to use UI, making a lot of types of users can collaborate with each other easily, by visualizing the same workflow.
Many building blocks can be reused immediately, avoid a lot of non-standard boiler plate implementation.
Data pre-analysis and feature engineering assistance increase the productivity as well as the efficiency of data scientists.
Many data connectors support wide range of data storage, from SQL, TeraData, Hadoop Hive, etc.
Support from research till final MaaS solution deployment.

Cons

The visualization feature of flow still has a lot room to improve, when the flow is complex.
The "non-coding" template/building block for deep learning lack of many important configurable parameters.
Lack of the unified way to allow applying the "design pattern" on the Python codes (if we want to develop our own module or building blocks.

Likelihood to Recommend

Dataiku is suitable for many steps of data processing pipeline development (from data collecting, filtering till cleaning, transformation and enhancement), and it is also good for the user who doesn't have too much in-depth AI/ML knowledge to quickly jump into it and give a try to solve some real-world problem.

Verified User

Strategist in Information Technology (10,001+ employees)

Vetted Review

2 years of experience

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Reviews and Ratings

Dataiku DSS - One-Stop Solution for All Data Science Applications

Rating: 10 out of 10

Incentivized

November 9, 2021

Use Cases and Deployment Scope

Pros

Allows users to collaborate and monitor individual tasks
Caters to both types of analysts, coders and non-coders, alike
Integrate graphs and plots with visualization tools such as Tableau

Cons

Its community support is very limited at the moment
Complex to integrate with automation tools such as Blue Prism

Likelihood to Recommend

Verified User

Analyst in Customer Service (5001-10,000 employees)

Vetted Review

2 years of experience