Dataiku DSS - One-Stop Solution for All Data Science Applications
Dataiku DSS is being used in my team to perform various tasks which ranges from data preprocessing to machine learning model creation. It …
Dataiku DSS
Dataiku DSS
Dataiku DSS
Overview
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.
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Entry-level set up fee?
- No setup fee
Offerings
- Free Trial
- Free/Freemium Version
- Premium Consulting / Integration Services
Product Details
- About
- Tech 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 Types | Software as a Service (SaaS), Cloud, or Web-Based |
|---|---|
| Operating Systems | Unspecified |
| Mobile Application | No |
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Reviews and Ratings
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November 09, 2021
Dataiku DSS - One-Stop Solution for All Data Science Applications
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
- Very friendly interface for users
- All data analytics services provided on a single platform
- Keeps track of all models created and every actions performed on a dataset
75%
7.5
100%
10.0
100%
10.0
87.5%
8.8
90%
9.0
- Customer satisfaction
- Timely project delivery
Strictly for Data Science operations, Anaconda can be considered as a subset of Dataiku DSS. While Anaconda supports Python and R programming languages, Dataiku also provides this facility, but also provides GUI to creates models with just a click of a button. This provides the flexibility to users who do not wish to alter the model hyperparameters in greater depths. Writing codes to extract meaningful data is time consuming compared to Dataiku's ability to perform feature engineering and data transformation through click of a button.
July 01, 2020
Low-Code Open-Source Data Analytics Platform!
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.
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.
85%
8.5
85%
8.5
80%
8.0
62.5%
6.3
65%
6.5
- Given its open source status, only cost is the learning curve, which is minimal compared to time savings for data exploration.
- Platform also ease tracking of data processing workflow, unlike Excel.
- Build-in data visualizations covers many use cases with minimal customization; time saver.
Open source availability is a critical factor given licensing cost of other platforms and budget reasons. Secondly, the available features in the community version covers most of the use cases, thus making it comparable or even outdo commercial versions of other software. Finally, being able to install and deploy platform in a plug and play mode really accelerates its adoption across wider audience.
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.
85%
8.5
80%
8.0
70%
7.0
70%
7.0
70%
7.0
- Dataiku provides a consistent platform, covering almost all needs from the data analytic till AI/ML areas.
- This platform "glues" all departments and business flows and IT data source together, making the data more exploitative.
Anaconda is mainly used by professional data scientists who have profound knowledge of Python coding, mainly used for building some new algorithm block or some optimization, then the module will be integrated into the Dataiku pipeline/workflow. While Dataiku can be used by even other kinds of users.