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Dataiku VS Microsoft Azure Notebooks

Compare Dataiku VS Microsoft Azure Notebooks and see what are their differences

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

Dataiku logo Dataiku

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

Microsoft Azure Notebooks logo Microsoft Azure Notebooks

You can enjoy powerful, integrated Jupyter notebooks with the following products and services from Microsoft and GitHub.
  • Dataiku Landing page
    Landing page //
    2023-08-17
  • Microsoft Azure Notebooks Landing page
    Landing page //
    2023-11-11

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.

Microsoft Azure Notebooks features and specs

No features have been listed yet.

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

Microsoft Azure Notebooks videos

No Microsoft Azure Notebooks videos yet. You could help us improve this page by suggesting one.

Add video

Category Popularity

0-100% (relative to Dataiku and Microsoft Azure Notebooks)

Dataiku

Microsoft Azure Notebooks

Data Science And Machine Learning
94 94%
Data Science And Machine Learning
6% 6
Data Science Tools
94 94%
Data Science Tools
6% 6
Python Tools
100 100%
Python Tools
0% 0
Data Science Notebooks
0 0%
Data Science Notebooks
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 Microsoft Azure Notebooks

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 company’s flagship product features a team-based user interface for both data analysts and data scientists. Dataiku’s unified framework for development and deployment provides immediate access to all the features needed to design data tools from scratch....
Source: solutionsreview.com

Microsoft Azure Notebooks Reviews

Jupyter Notebook & 10 Alternatives: Data Notebook Review [2023]
Microsoft Azure Notebooks is a cloud-based platform for data science projects and machine learning. It gives you access to hardware resources for running machine learning and deep learning models, as well as other useful features, such as integration with Microsoft Azure Storage, Git support, and the ability to easily share and collaborate on notebooks with other team members.
Source: lakefs.io
12 Best Jupyter Notebook Alternatives [2023] – Features, pros & cons, pricing
Microsoft Azure Notebooks is a cloud-based platform for data science projects and machine learning that is similar to Google Colab and Kaggle Notebooks. It provides access to powerful hardware resources, including GPUs and TPUs, for running machine learning and deep learning models, as well as a number of other useful features, such as integration with Microsoft Azure...
Source: noteable.io

What are some alternatives?

When comparing Dataiku and Microsoft Azure Notebooks, 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.

Kaggle Notebooks - Kaggle Notebooks are a computational environment that enables reproducible and collaborative analysis.

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

Paperspace Gradient - A Linux desktop in the cloud built for Machine Learning

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

Deepnote - A collaboration platform for data scientists

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