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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    Azure Databricks

    Score8.5 out of 10
    N/AAzure Databricks is a service available on Microsoft's Azure platform and suite of products. It provides the latest versions of Apache Spark so users can integrate with open source libraries, or spin up clusters and build in a fully managed Apache Spark environment with the global scale and availability of Azure. Clusters are set up, configured, and fine-tuned to ensure reliability and performance without the need for monitoring. The solution includes autoscaling and auto-termination to improve…N/A

    Jupyter Notebook

    Score8.6 out of 10
    N/AJupyter Notebook is an open-source web application that allows users to create and share documents containing live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, and machine learning. It supports over 40 programming languages, and notebooks can be shared with others using email, Dropbox, GitHub and the Jupyter Notebook Viewer. It is used with JupyterLab, a web-based IDE for…N/A
    Pricing
    Azure DatabricksJupyter Notebook
    Editions & Modules
    No answers on this topic
    No answers on this topic
    Offerings
    Pricing Offerings
    Azure DatabricksJupyter Notebook
    Free Trial
    NoNo
    Free/Freemium Version
    NoNo
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeNo setup feeNo setup fee
    Additional Details——
    More Pricing Information
    Community Pulse
    Azure DatabricksJupyter Notebook
    Considered Both Products
    Microsoft
    Chose Azure Databricks
    Against all the tools I have used, Azure Databricks is by far the most superior of them all! Why, you ask? The UI is modern, the features are never ending and they keep adding new features. And to quote Apple, "It just works!"
    Far ahead of the competition, the delta lakehouse …
    Incentivized
    Open Source
    No answer on this topic
    Key User Insights
    Would buy again
    No answers on this topic
    100%
    Would buy again
    23 Answers
    Delivers good value for the price
    No answers on this topic
    100%
    Delivers good value for the price
    23 Answers
    Happy with the feature set
    No answers on this topic
    96%
    Happy with the feature set
    22 Answers
    Lived up to sales and marketing promises
    No answers on this topic
    100%
    Lived up to sales and marketing promises
    17 Answers
    Implementation went as expected
    No answers on this topic
    95%
    Implementation went as expected
    20 Answers
    Features
    Azure DatabricksJupyter Notebook
    Platform Connectivity
    Comparison of Platform Connectivity features of Azure Databricks and Jupyter Notebook
    Feature
    Azure Databricks
    7.2
    4 Ratings
    15% below category average
    Jupyter Notebook
    9.0
    22 Ratings
    7% above category average
    Connect to Multiple Data Sources6.04 Ratings10.022 Ratings
    Extend Existing Data Sources7.64 Ratings10.021 Ratings
    Automatic Data Format Detection7.14 Ratings8.514 Ratings
    MDM Integration8.03 Ratings7.415 Ratings
    Data Exploration
    Comparison of Data Exploration features of Azure Databricks and Jupyter Notebook
    Feature
    Azure Databricks
    6.9
    4 Ratings
    19% below category average
    Jupyter Notebook
    7.0
    22 Ratings
    18% below category average
    Visualization6.04 Ratings6.022 Ratings
    Interactive Data Analysis7.93 Ratings8.022 Ratings
    Data Preparation
    Comparison of Data Preparation features of Azure Databricks and Jupyter Notebook
    Feature
    Azure Databricks
    8.7
    4 Ratings
    6% above category average
    Jupyter Notebook
    9.5
    22 Ratings
    15% above category average
    Interactive Data Cleaning and Enrichment8.44 Ratings10.021 Ratings
    Data Transformations9.04 Ratings10.022 Ratings
    Data Encryption9.54 Ratings8.514 Ratings
    Built-in Processors8.04 Ratings9.314 Ratings
    Platform Data Modeling
    Comparison of Platform Data Modeling features of Azure Databricks and Jupyter Notebook
    Feature
    Azure Databricks
    7.9
    4 Ratings
    7% below category average
    Jupyter Notebook
    9.3
    22 Ratings
    10% above category average
    Multiple Model Development Languages and Tools6.14 Ratings10.021 Ratings
    Automated Machine Learning8.54 Ratings9.218 Ratings
    Single platform for multiple model development8.54 Ratings10.022 Ratings
    Self-Service Model Delivery8.54 Ratings8.020 Ratings
    Model Deployment
    Comparison of Model Deployment features of Azure Databricks and Jupyter Notebook
    Feature
    Azure Databricks
    8.3
    4 Ratings
    3% below category average
    Jupyter Notebook
    10.0
    20 Ratings
    15% above category average
    Flexible Model Publishing Options8.04 Ratings10.020 Ratings
    Security, Governance, and Cost Controls8.54 Ratings10.019 Ratings
    Best Alternatives
    Azure DatabricksJupyter Notebook
    Small Businesses
    RapidMiner
    Score8.9 out of 10
    RapidMiner
    Score8.9 out of 10
    Medium-sized Companies
    Anaconda
    Score8.8 out of 10
    Anaconda
    Score8.8 out of 10
    Enterprises
    IBM Watson Studio
    Score10 out of 10
    IBM Watson Studio
    Score10 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    Azure DatabricksJupyter Notebook
    Likelihood to Recommend
    7.6
    (5 ratings)
    10.0
    (23 ratings)
    Usability
    7.5
    (3 ratings)
    10.0
    (2 ratings)
    Support Rating
    -
    (0 ratings)
    9.0
    (1 ratings)
    User Testimonials
    Azure DatabricksJupyter Notebook
    Likelihood to Recommend
    Microsoft
    Centralised notebooks are out directly into production. This can lead to poorly engineered code. It is very good for fast queries and our data team are always able to provide what we ask for. It is a big cost to our business so it is important it runs efficiently and returns on our investment.
    Incentivized
    Read full review
    Open Source
    I've created a number of daisy chain notebooks for different workflows, and every time, I create my workflows with other users in mind. Jupiter Notebook makes it very easy for me to outline my thought process in as granular a way as I want without using innumerable small. inline comments.
    Incentivized
    Read full review
    Pros
    Microsoft
    • Data Processing and Transformations based on Spark
    • Delta Lakehouse when clubbed with an external cloud storage
    • Governance using Unity Catalog to unify IAM
    • Delta Live Tables is a product, which although relatively newer, has a great potential with the visuals of a pipeline.
    Incentivized
    Read full review
    Open Source
    • Simple and elegant code writing ability. Easier to understand the code that way.
    • The ability to see the output after each step.
    • The ability to use ton of library functions in Python.
    • Easy-user friendly interface.
    Incentivized
    Read full review
    Cons
    Microsoft
    • Intuitive interface
    • Ease of use
    • Providing FAQ or QRGs
    Incentivized
    Read full review
    Open Source
    • Need more Hotkeys for creating a beautiful notebook. Sometimes we need to download other plugins which messes [with] its default settings.
    • Not as powerful as IDE, which sometimes makes [the] job difficult and allows duplicate code as it get confusing when the number of lines increases. Need a feature where [an] error comes if duplicate code is found or [if a] developer tries the same function name.
    Incentivized
    Read full review
    Usability
    Microsoft
    The developers are able to switch between Python and SQL in the Notebook which allows the collaboration of SQL analyst and Data scientist. The integration of Mosaic AI allows users to write complex codes in natural languages. Unity catalog has centralized the security and governance features and simplified the process of maintaining it
    Incentivized
    Read full review
    Open Source
    Jupyter is highly simplistic. It took me about 5 mins to install and create my first "hello world" without having to look for help. The UI has minimalist options and is quite intuitive for anyone to become a pro in no time. The lightweight nature makes it even more likeable.
    Incentivized
    Read full review
    Support Rating
    Microsoft
    No answers on this topic
    Open Source
    I haven't had a need to contact support. However, all required help is out there in public forums.
    Incentivized
    Read full review
    Alternatives Considered
    Microsoft
    I have found Azure Databricks to be much better than Snowflake for handling bigger, diverse data types. Snowflake is much simpler and better for smaller warehousing. The real time processing is much better in Azure Databricks and we have much more language options. Snowflake is more expensive but simpler to use. Both are great for different needs.
    Incentivized
    Read full review
    Open Source
    With Jupyter Notebook besides doing data analysis and performing complex visualizations you can also write machine learning algorithms with a long list of libraries that it supports. You can make better predictions, observations etc. with it which can help you achieve better business decisions and save cost to the company. It stacks up better as we know Python is more widely used than R in the industry and can be learnt easily. Unlike PyCharm jupyter notebooks can be used to make documentations and exported in a variety of formats.
    Incentivized
    Read full review
    Return on Investment
    Microsoft
    • The support team is amazing, they help you at every stage of the projects, from sales to delivery.
    • On a framework level, it has had an amazing impact and has reduced the clients overall data platform costs by a staggering 65%
    • There has been a 40% Manual work requirement on average for the clients when they move to Azure Databricks Data Platform
    Incentivized
    Read full review
    Open Source
    • Positive impact: flexible implementation on any OS, for many common software languages
    • Positive impact: straightforward duplication for adaptation of workflows for other projects
    • Negative impact: sometimes encourages pigeonholing of data science work into notebooks versus extending code capability into software integration
    Incentivized
    Read full review
    ScreenShots