Dataiku vs. dataTap

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
dataTap
Score 0.0 out of 10
N/A
dataTap is a user friendly visual data management platform from Zensors. The dataTap Python library is the primary interface for using dataTap's data management tools. Users can create datasets, stream annotations, and analyze model performance all with one library. Zensors states with dataTap, users can: - Begin training instantly - Work with all major ML frameworks…N/A
Pricing
DataikudataTap
Editions & Modules
Discover
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Business
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Enterprise
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Offerings
Pricing Offerings
DataikudataTap
Free Trial
YesYes
Free/Freemium Version
YesYes
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeOptional
Additional Details
More Pricing Information
Community Pulse
DataikudataTap
Considered Both Products
Dataiku
Chose Dataiku
Dataiku was selected for me, but I am happy about that. I like Dataiku for the user experience, it feels less code-y and I like to demo things to non technical stakeholders because they can still follow along. When you open some other notebooks, you can see that peoples eyes …
Chose Dataiku
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 …
Chose Dataiku
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. …
Chose 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 …
dataTap

No answer on this topic

Features
DataikudataTap
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Dataiku
8.6
Ratings
3% above category average
dataTap
-
Ratings
Connect to Multiple Data Sources8.00 Ratings00 Ratings
Extend Existing Data Sources10.00 Ratings00 Ratings
Automatic Data Format Detection10.00 Ratings00 Ratings
MDM Integration6.50 Ratings00 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Dataiku
10.0
Ratings
17% above category average
dataTap
-
Ratings
Visualization10.00 Ratings00 Ratings
Interactive Data Analysis10.00 Ratings00 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Dataiku
9.5
Ratings
15% above category average
dataTap
-
Ratings
Interactive Data Cleaning and Enrichment9.00 Ratings00 Ratings
Data Transformations9.00 Ratings00 Ratings
Data Encryption10.00 Ratings00 Ratings
Built-in Processors10.00 Ratings00 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Dataiku
8.5
Ratings
1% above category average
dataTap
-
Ratings
Multiple Model Development Languages and Tools8.00 Ratings00 Ratings
Automated Machine Learning8.00 Ratings00 Ratings
Single platform for multiple model development8.00 Ratings00 Ratings
Self-Service Model Delivery10.00 Ratings00 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
Dataiku
8.0
Ratings
6% below category average
dataTap
-
Ratings
Flexible Model Publishing Options8.00 Ratings00 Ratings
Security, Governance, and Cost Controls8.00 Ratings00 Ratings
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DataikudataTap
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User Ratings
DataikudataTap
Likelihood to Recommend
10.0
(0 ratings)
-
(0 ratings)
Usability
10.0
(0 ratings)
-
(0 ratings)
Support Rating
9.4
(0 ratings)
-
(0 ratings)
User Testimonials
DataikudataTap
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.
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Pros
  • Low-code platform.
  • Open source version includes most valuable modules.
  • User friendly documentation.
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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.
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Usability
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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Support Rating
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.
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Alternatives Considered
Dataiku was selected for me, but I am happy about that. I like Dataiku for the user experience, it feels less code-y and I like to demo things to non technical stakeholders because they can still follow along. When you open some other notebooks, you can see that peoples eyes start to glaze over
Read full review
No answers on this topic
Return on Investment
  • 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.
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No answers on this topic
ScreenShots

dataTap Screenshots

Screenshot of Install the client library.

`pip install datatap`

Register at [app.datatap.dev](https://app.datatap.dev/). Then, go to `Settings > Api Keys` to find your personal API key.

`export DATATAP_API_KEY="XXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXX"`

To begin with, select a dataset from the dataTap repositoryScreenshot of Copy the starter code based on your library preferenceScreenshot of Paste the starter code and start training.

from datatap import Api

api = Api()
coco = api.get_default_database().get_repository("_/coco")
dataset = coco.get_dataset("latest")
print("COCO: ", dataset)