Dataiku vs. Shakudo Platform

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Dataiku
Score 8.5 out of 10
N/A
The Dataiku platform unifies data work from analytics to Generative AI. It supports enterprise analytics with visual, cloud-based tooling for data preparation, visualization, and workflow automation.N/A
Shakudo Platform
Score 6.0 out of 10
N/A
The Shakudo platform ensures compatibility across data tools to allow companies to build the best data infrastructure for their needs. It supports over 60 popular data tools, and enables users to create the optimal data stack for any custom needs, and helps users to create a more reliable and performant stack. And it helps users to: Build a stack that works precisely for the company without worrying about maintenance costs or stability Using a single UI, let teams use and gain…N/A
Pricing
DataikuShakudo Platform
Editions & Modules
Discover
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Business
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Enterprise
Contact sales team
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Offerings
Pricing Offerings
DataikuShakudo Platform
Free Trial
YesNo
Free/Freemium Version
YesNo
Premium Consulting/Integration Services
NoYes
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
DataikuShakudo Platform
Features
DataikuShakudo Platform
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Dataiku
8.6
5 Ratings
3% above category average
Shakudo Platform
-
Ratings
Connect to Multiple Data Sources8.05 Ratings00 Ratings
Extend Existing Data Sources10.04 Ratings00 Ratings
Automatic Data Format Detection10.05 Ratings00 Ratings
MDM Integration6.52 Ratings00 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Dataiku
10.0
5 Ratings
17% above category average
Shakudo Platform
-
Ratings
Visualization10.05 Ratings00 Ratings
Interactive Data Analysis10.05 Ratings00 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Dataiku
9.5
5 Ratings
15% above category average
Shakudo Platform
-
Ratings
Interactive Data Cleaning and Enrichment9.05 Ratings00 Ratings
Data Transformations9.05 Ratings00 Ratings
Data Encryption10.04 Ratings00 Ratings
Built-in Processors10.04 Ratings00 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Dataiku
8.5
5 Ratings
1% above category average
Shakudo Platform
-
Ratings
Multiple Model Development Languages and Tools8.05 Ratings00 Ratings
Automated Machine Learning8.05 Ratings00 Ratings
Single platform for multiple model development8.05 Ratings00 Ratings
Self-Service Model Delivery10.04 Ratings00 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
Dataiku
8.0
5 Ratings
6% below category average
Shakudo Platform
-
Ratings
Flexible Model Publishing Options8.05 Ratings00 Ratings
Security, Governance, and Cost Controls8.05 Ratings00 Ratings
Best Alternatives
DataikuShakudo Platform
Small Businesses
Jupyter Notebook
Jupyter Notebook
Score 8.5 out of 10
Jupyter Notebook
Jupyter Notebook
Score 8.5 out of 10
Medium-sized Companies
Posit
Posit
Score 10.0 out of 10
Posit
Posit
Score 10.0 out of 10
Enterprises
Posit
Posit
Score 10.0 out of 10
Posit
Posit
Score 10.0 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
DataikuShakudo Platform
Likelihood to Recommend
10.0
(4 ratings)
-
(0 ratings)
Usability
10.0
(1 ratings)
-
(0 ratings)
Support Rating
9.4
(3 ratings)
-
(0 ratings)
User Testimonials
DataikuShakudo Platform
Likelihood to Recommend
Dataiku
Dataiku is an awesome tool for data scientists. It really makes our lives easier. It is also really good for non technical users to see and follow along with the process. I do think that people can fall into the trap of using it without any knowledge at all because so much is automated, but I dont think that is the fault of Dataiku.
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Shakudo
No answers on this topic
Pros
Dataiku
  • 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
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Shakudo
No answers on this topic
Cons
Dataiku
  • 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.
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Shakudo
No answers on this topic
Usability
Dataiku
The user experience is very good. Everything feels intuitive and "flows" (sorry excuse the pun) so nicely, and the customization level is also appropriate to the tool. Even as a newer data scientist, it felt easy to use and the explanations/tutorials were very good. The documentation is also at a good level
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Shakudo
No answers on this topic
Support Rating
Dataiku
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.
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Shakudo
No answers on this topic
Alternatives Considered
Dataiku
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.
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Shakudo
No answers on this topic
Return on Investment
Dataiku
  • Customer satisfaction
  • Timely project delivery
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Shakudo
No answers on this topic
ScreenShots

Shakudo Platform Screenshots

Screenshot of Shakudo Platform Dashboard UIScreenshot of Jobs use the same environment config as Sessions, so users can have the same dependencies and configurations available as they had during development. Jobs can be run on demand using Immediate jobs, or scheduled with a Cron expression using Scheduled Jobs. The integrated Job system can be used on its own, or it can be used to run code with tools like Airflow or Prefect.Screenshot of When deploying a project and publishing the results, Services help to deploy custom long-running programs like web servers to host results, or integrations can be used. From publishing processed data to S3 buckets, to hosting trained models on Nvidia Triton, to publishing dashboards in Apache Superset, it helps data scientists deploy their projects and their artifacts with minimal configuration or maintenance required