Dataiku DSS

Dataiku DSS

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Score 9.0 out of 100
Dataiku DSS

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Product Details

What is Dataiku DSS?

Dataiku is a French startup and its product, DSS, is a challenger to market incumbents and features some visual tools to assist in building workflows.

Dataiku DSS Technical Details

Deployment TypesSaaS
Operating SystemsUnspecified
Mobile ApplicationNo

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Frequently Asked Questions

What is Dataiku DSS?

Dataiku is a French startup and its product, DSS, is a challenger to market incumbents and features some visual tools to assist in building workflows.

What is Dataiku DSS's best feature?

Reviewers rate Connect to Multiple Data Sources highest, with a score of 9.9.

Who uses Dataiku DSS?

The most common users of Dataiku DSS are from Enterprises (1,001+ employees) and the Information Technology & Services industry.

Reviews and Ratings

 (16)

Ratings

Reviews

(1-4 of 4)
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Score 10 out of 10
Vetted Review
Verified User
Review Source
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.
  • 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
  • Its community support is very limited at the moment
  • Complex to integrate with automation tools such as Blue Prism
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.
Devesh Singh | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
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.
  • 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.
  • 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.
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.
The amazing part of Dataiku DSS is their customer service. Based on urgency and technical level, you get a reply from the Dataiku engineer when you raise a query. So far, my queries have been pretty complex to solve, so I have received solutions even from the CTO of the company as well, which is why I would describe their customer support as very good.
Score 8 out of 10
Vetted Review
Verified User
Review Source
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.
  • Low-code platform.
  • Open source version includes most valuable modules.
  • User friendly documentation.
  • End product deployment.
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.
The open source user community is friendly, helpful, and responsive, at times even outdoing commercial software vendors. Documentation is also top notch, and usually resolves issues without the need for human interactions. Great product design, with a focus on user experience, also makes platform use intuitive, thus reducing the need for explicit support.
Score 7 out of 10
Vetted Review
Verified User
Review Source
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).
  • 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.
  • 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.
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.
The support team is very helpful, and even when we discover the missing features, after providing enough rational reasons and requirements, they put into it their development pipeline for the future release.