Overview
What is Dataiku?
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 - One-Stop Solution for All Data Science Applications
Dataiku DSS: Click or code--the choice is yours!
Low-Code Open-Source Data Analytics Platform!
Platform offers a one stop shop for an …
Dataiku - a complete Data Analytic and AI/ML solution
Pricing
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Business
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Enterprise
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Entry-level set up fee?
- No setup fee
Offerings
- Free Trial
- Free/Freemium Version
- Premium Consulting/Integration Services
Product Demos
Dataiku DSS Demo: End-to-End [Portuguese]
Demo Dataiku DSS ภาษาไทย
End-to-End ML with Dataiku DSS
Learn Data science Fast and Easy without code - Dataiku Demo
Product Details
- About
- Integrations
- Competitors
- Tech Details
- FAQs
What is Dataiku?
Dataiku Integrations
Dataiku Competitors
Dataiku Technical Details
Deployment Types | On-premise, Software as a Service (SaaS), Cloud, or Web-Based |
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Operating Systems | Windows, Linux, Mac, VirtualBox / VMWare |
Mobile Application | No |
Frequently Asked Questions
Comparisons
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Reviews and Ratings
(21)Community Insights
- Pros
- Cons
- Recommendations
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.
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User Interface: Some users have mentioned that the user interface of Dataiku DSS could use some improvements as it is not intuitive or easy to navigate. They have found it challenging to locate certain features and perform tasks efficiently.
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Limited support for multi-file projects: Several users have expressed frustration with the limited support for using multi-file projects as a recipe or pipeline in Dataiku DSS. They feel that this feature is not robust enough, making it difficult to handle complex workflows involving multiple files.
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Processing time for multi-user purposes: A number of users have experienced prolonged and stressful processing times when using Dataiku DSS for multi-user purposes, even with reduced users and training data. This has resulted in delays and inefficiencies in their workflow management.
Users highly recommend using Anaconda and RStudio for data transformation and analysis, as they believe these tools are the best in the industry for these tasks.
DSS is recommended over other tools for handling big data and sharing flows. Users suggest that it provides better functionality and performance compared to its competitors.
To gain a better understanding of all the components of the data, users suggest getting certification and using a data catalog. This helps users navigate and comprehend complex datasets more effectively.
Attribute Ratings
Reviews
(1-4 of 4)Dataiku DSS - One-Stop Solution for All Data Science Applications
- 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
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.
- 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
- Connect to Multiple Data Sources
- 100%10.0
- Extend Existing Data Sources
- 100%10.0
- Automatic Data Format Detection
- 100%10.0
- MDM Integration
- N/AN/A
- Visualization
- 100%10.0
- Interactive Data Analysis
- 100%10.0
- Interactive Data Cleaning and Enrichment
- 100%10.0
- Data Transformations
- 100%10.0
- Data Encryption
- 100%10.0
- Built-in Processors
- 100%10.0
- Multiple Model Development Languages and Tools
- 50%5.0
- Automated Machine Learning
- 100%10.0
- Single platform for multiple model development
- 100%10.0
- Self-Service Model Delivery
- 100%10.0
- Flexible Model Publishing Options
- 90%9.0
- Security, Governance, and Cost Controls
- 90%9.0
- Customer satisfaction
- Timely project delivery
Dataiku DSS: Click or code--the choice is yours!
- 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.
- Connect to Multiple Data Sources
- 100%10.0
- Extend Existing Data Sources
- 100%10.0
- Automatic Data Format Detection
- 100%10.0
- MDM Integration
- N/AN/A
- Visualization
- 60%6.0
- Interactive Data Analysis
- 80%8.0
- Interactive Data Cleaning and Enrichment
- 100%10.0
- Data Transformations
- 90%9.0
- Data Encryption
- 90%9.0
- Built-in Processors
- 100%10.0
- Multiple Model Development Languages and Tools
- 90%9.0
- Automated Machine Learning
- 100%10.0
- Single platform for multiple model development
- 100%10.0
- Self-Service Model Delivery
- 100%10.0
- Flexible Model Publishing Options
- 90%9.0
- Security, Governance, and Cost Controls
- 90%9.0
- So far it has had a positive impact. Multiple departments are coming to us with their business problems.
- I can't specifically say about ROI as I'm a developer, though I have heard this solution is economical compared to other AI/ML enterprise tools.
- By using this tool, my client has let go of software that was used earlier, and we have created a simpler framework to replace that software.
Low-Code Open-Source Data Analytics Platform!
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.
- Connect to Multiple Data Sources
- 90%9.0
- Extend Existing Data Sources
- 90%9.0
- Automatic Data Format Detection
- 90%9.0
- MDM Integration
- 70%7.0
- Visualization
- 90%9.0
- Interactive Data Analysis
- 80%8.0
- Interactive Data Cleaning and Enrichment
- 90%9.0
- Data Transformations
- 90%9.0
- Data Encryption
- 70%7.0
- Built-in Processors
- 70%7.0
- Multiple Model Development Languages and Tools
- 70%7.0
- Automated Machine Learning
- 60%6.0
- Single platform for multiple model development
- 60%6.0
- Self-Service Model Delivery
- 60%6.0
- Flexible Model Publishing Options
- 60%6.0
- Security, Governance, and Cost Controls
- 70%7.0
- 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.
- 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.
- Connect to Multiple Data Sources
- 100%10.0
- Extend Existing Data Sources
- 90%9.0
- Automatic Data Format Detection
- 90%9.0
- MDM Integration
- 60%6.0
- Visualization
- 80%8.0
- Interactive Data Analysis
- 80%8.0
- Interactive Data Cleaning and Enrichment
- 80%8.0
- Data Transformations
- 80%8.0
- Data Encryption
- 50%5.0
- Built-in Processors
- 70%7.0
- Multiple Model Development Languages and Tools
- 70%7.0
- Automated Machine Learning
- 70%7.0
- Single platform for multiple model development
- 70%7.0
- Self-Service Model Delivery
- 70%7.0
- Flexible Model Publishing Options
- 70%7.0
- Security, Governance, and Cost Controls
- 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.