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Dataiku VS Quantilytics

Compare Dataiku VS Quantilytics and see what are their differences

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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

Dataiku logo Dataiku

Dataiku is the developer of DSS, the integrated development platform for data professionals to turn raw data into predictions.

Quantilytics logo Quantilytics

We are a one stop shop for all your IT needs. We focus on startups and SME's. Our Mission is to deliver profitability and bringing efficiency through data-driven decisions.
  • Dataiku Landing page
    Landing page //
    2023-08-17
  • Quantilytics Landing page
    Landing page //
    2022-01-09

Quantilytics is your one stop solution to all your IT related needs. We provide IT services in the fields of mobile and web application design and development, business analytics, data science, software engineering and more. We also design campaigns and manage outbound and inbound calling for our clients and provide a plethora of communications services as well. When it comes to our IT team, our developers have years of experience designing and developing the best applications you can find today in the market. It is always a priority for us to make sure that our client always leaves happy.

Dataiku

Website
dataiku.com
Pricing URL
Official Dataiku Pricing
Release Date
2013 January
Startup details
Country
United States
State
New York
City
New York
Founder(s)
Clรฉment Stenac
Employees
500 - 999
Edit details

Quantilytics

Website
quantilytics.org
Pricing URL
-
Release Date
-
Edit details

Dataiku features and specs

  • User-Friendly Interface
    Dataiku offers an intuitive and easy-to-navigate visual interface that allows users of all technical backgrounds to create, manage, and deploy data projects without needing extensive coding knowledge.
  • Collaborative Environment
    The platform supports collaborative work, enabling data scientists, engineers, and analysts to work together on the same projects seamlessly, sharing insights and models easily.
  • End-to-End Workflow
    Dataiku provides tools that cover the entire data pipeline, from data preparation and cleaning to model building, deployment, and monitoring, making it a comprehensive solution for data teams.
  • Integrations and Extensibility
    The platform integrates with many data storage systems, machine learning libraries, and cloud services, allowing users to leverage existing tools and infrastructure.
  • Automation Capabilities
    Dataiku offers automation features such as scheduling, automation scenarios, and machine learning model monitoring, which can significantly enhance productivity and efficiency.
  • Rich Documentation and Support
    Dataiku provides extensive documentation, tutorials, and a strong support community to help users navigate the platform and troubleshoot issues.

Possible disadvantages of Dataiku

  • Pricing
    Dataiku can be expensive, particularly for small businesses and startups. The cost may be a barrier to entry for organizations with limited budgets.
  • Resource Intensive
    The platform can be resource-hungry, requiring significant computing power, which may necessitate additional investments in hardware or cloud services.
  • Learning Curve for Advanced Features
    Although the basic interface is user-friendly, mastering advanced features and customizations can require a steep learning curve and significant training.
  • Limited Offline Capabilities
    Dataiku relies heavily on cloud services for many of its functionalities. This dependence might be restrictive in environments with limited or no internet access.
  • Custom Model Flexibility
    While Dataiku supports many machine learning frameworks, the process of integrating custom or niche models can be cumbersome compared to using those frameworks directly.
  • Dependency on Ecosystem
    The seamless experience of Dataiku often relies on the broader cloud and data ecosystem. Changes or issues in integrated services can impact its performance and reliability.

Quantilytics features and specs

No features have been listed yet.

Analysis of Quantilytics

Overall verdict

  • I don't have verified, up-to-date information about a specific product or service called 'Quantilytics' at quantilytics.org, so I can't confidently confirm its legitimacy or quality. Before using it, independently verify the company's credentials, reviews, and regulatory status.

Why this product is good

  • No reliable independent reviews or verified data available to confirm quality or trustworthiness.
  • Domain names like this are sometimes used by unregulated or unlicensed financial/analytics services, so caution is warranted.
  • Legitimate analytics or fintech platforms typically have transparent company information, regulatory disclosures, and verifiable user reviews, which should be checked directly on the site.
  • Always cross-check for SSL security, business registration, and third-party review platforms (e.g., Trustpilot, BBB) before trusting or investing through such a service.

Recommended for

  • Not recommended without further due diligence
  • Suitable only for users who have independently verified the company's legitimacy, licensing, and reviews
  • Not suitable for making financial decisions or investments until proper verification is completed

Dataiku videos

+ Add

AutoML with Dataiku: And End-to-End Demo

More videos:

  • Review - Dataiku: For Everyone in the Data-Powered Organization
  • Tutorial - Dataiku DSS Tutorial 101: Your very first steps

Quantilytics videos

No Quantilytics videos yet. You could help us improve this page by suggesting one.

Add video

Category Popularity

0-100% (relative to Dataiku and Quantilytics)

Dataiku

Quantilytics

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Software Development
0 0%
Software Development
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Feature Flags
0 0%
Feature Flags
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Dataiku and Quantilytics

Dataiku Reviews

15 data science tools to consider using in 2021
Some platforms are also available in free open source or community editions -- examples include Dataiku and H2O. Knime combines an open source analytics platform with a commercial Knime Server software package that supports team-based collaboration and workflow automation, deployment and management.
Source: searchbusinessanalytics.techtarget.com
The 16 Best Data Science and Machine Learning Platforms for 2021
Description: Dataiku offers an advanced analytics solution that allows organizations to create their own data tools. The companyโ€™s flagship product features a team-based user interface for both data analysts and data scientists. Dataikuโ€™s unified framework for development and deployment provides immediate access to all the features needed to design data tools from scratch....
Source: solutionsreview.com

Quantilytics Reviews

We have no reviews of Quantilytics yet.
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What are some alternatives?

When comparing Dataiku and Quantilytics, you can also consider the following products

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

NumPy - NumPy is the fundamental package for scientific computing with Python

OpenCV - OpenCV is the world's biggest computer vision library

Exploratory - Exploratory enables users to understand data by transforming, visualizing, and applying advanced statistics and machine learning algorithms.

htm.java - htm.java is a Hierarchical Temporal Memory implementation in Java, it provide a Java version of NuPIC that has a 1-to-1 correspondence to all systems, functionality and tests provided by Numenta's open source implementation.

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