IBM Analytics Engine vs. Jupyter Notebook

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
IBM Analytics Engine
Score 8.8 out of 10
N/A
IBM BigInsights is an analytics and data visualization tool leveraging hadoop.N/A
Jupyter Notebook
Score 8.9 out of 10
N/A
Jupyter 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
IBM Analytics EngineJupyter Notebook
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
IBM Analytics EngineJupyter 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
IBM Analytics EngineJupyter Notebook
Considered Both Products
IBM Analytics Engine
Chose IBM Analytics Engine
Our data analytics team happened to try IBM Analytics just to get acquainted with it & it turned out that this tool fits our business requirement better than the one which we were using in terms of the features along with the level of support that they provide. so, choosing the …
Jupyter Notebook

No answer on this topic

Top Pros
Top Cons
Features
IBM Analytics EngineJupyter Notebook
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
IBM Analytics Engine
-
Ratings
Jupyter Notebook
8.5
21 Ratings
1% above category average
Connect to Multiple Data Sources00 Ratings9.021 Ratings
Extend Existing Data Sources00 Ratings9.220 Ratings
Automatic Data Format Detection00 Ratings8.514 Ratings
MDM Integration00 Ratings7.415 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
IBM Analytics Engine
-
Ratings
Jupyter Notebook
9.6
21 Ratings
13% above category average
Visualization00 Ratings9.621 Ratings
Interactive Data Analysis00 Ratings9.621 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
IBM Analytics Engine
-
Ratings
Jupyter Notebook
9.0
21 Ratings
9% above category average
Interactive Data Cleaning and Enrichment00 Ratings9.320 Ratings
Data Transformations00 Ratings8.921 Ratings
Data Encryption00 Ratings8.514 Ratings
Built-in Processors00 Ratings9.314 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
IBM Analytics Engine
-
Ratings
Jupyter Notebook
8.9
21 Ratings
5% above category average
Multiple Model Development Languages and Tools00 Ratings9.020 Ratings
Automated Machine Learning00 Ratings9.218 Ratings
Single platform for multiple model development00 Ratings9.221 Ratings
Self-Service Model Delivery00 Ratings8.020 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
IBM Analytics Engine
-
Ratings
Jupyter Notebook
8.8
19 Ratings
3% above category average
Flexible Model Publishing Options00 Ratings8.819 Ratings
Security, Governance, and Cost Controls00 Ratings8.718 Ratings
Best Alternatives
IBM Analytics EngineJupyter Notebook
Small Businesses

No answers on this topic

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Score 7.8 out of 10
Medium-sized Companies
Cloudera Manager
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Score 9.7 out of 10
Mathematica
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Score 8.2 out of 10
Enterprises
Azure Data Lake Storage
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Score 8.4 out of 10
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Score 7.8 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
IBM Analytics EngineJupyter Notebook
Likelihood to Recommend
9.5
(9 ratings)
8.4
(22 ratings)
Usability
-
(0 ratings)
10.0
(1 ratings)
Support Rating
-
(0 ratings)
9.0
(1 ratings)
User Testimonials
IBM Analytics EngineJupyter Notebook
Likelihood to Recommend
IBM
  • Well suited for my big data related project or a static data set analysis especially for uploading huge dataset to the cluster.
  • But had some issues with connecting IoT real-time data and feeding to Power BI. It might be my understanding please take it as a mere comment rather than a suggestion.
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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.
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Pros
IBM
  • Jobs with Spark, Hadoop, or Hive queries are rapidly attained
  • Can collect, organize and analyze your data accurately
  • You can customize, for example, Spark or Hadoop configuration settings, or Python, R, Scala, or Java libraries.
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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.
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Cons
IBM
  • Easier pricing and plug-and-play like you see with AWS and Azure, it would be nice from a budgeting and billing standpoint, as well as better support for the administration.
  • Bundling of the Cloud Object Storage should be included with the Analytics Engine.
  • The inability to add your own Hadoop stack components has made some transfers a little more complex.
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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.
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Usability
IBM
No answers on this topic
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.
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Support Rating
IBM
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.
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Alternatives Considered
IBM
We initially wanted to go with Google BigQuery, mainly for the name recognition. However, the pricing and support structure led us to seek alternatives, which pointed us to IBM. Apache Spark was also in the running, but here IBM's domination in the industry made the choice a no-brainer. As previously stated, the support received was not quite what we expected, but was adequate.
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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.
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Return on Investment
IBM
  • This product has allowed us to gather analytics data across multiple platforms so we can view and analyze the data from different workflows, all in one place.
  • IBM Analytics has allowed us to scale on demand which allows us to capture more and more data, thus increasing our ROI.
  • The convenience of the ability to access and administer the product via multiple interfaces has allowed our administrators to ensure that the application is making a positive ROI for our business users and partners.
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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
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ScreenShots