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…
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OpenText Magellan
Score 9.0 out of 10
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OpenText Magellan Analytics Suite leverages a comprehensive set of data analytics software to identify patterns, relationships and trends through data visualizations and interactive dashboards.
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Pricing
Jupyter Notebook
OpenText Magellan
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Jupyter Notebook
OpenText Magellan
Free Trial
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Free/Freemium Version
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No
Premium Consulting/Integration Services
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Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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Jupyter Notebook
OpenText Magellan
Features
Jupyter Notebook
OpenText Magellan
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Jupyter Notebook
9.0
22 Ratings
8% above category average
OpenText Magellan
-
Ratings
Connect to Multiple Data Sources
10.022 Ratings
00 Ratings
Extend Existing Data Sources
10.021 Ratings
00 Ratings
Automatic Data Format Detection
8.514 Ratings
00 Ratings
MDM Integration
7.415 Ratings
00 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Jupyter Notebook
7.0
22 Ratings
19% below category average
OpenText Magellan
-
Ratings
Visualization
6.022 Ratings
00 Ratings
Interactive Data Analysis
8.022 Ratings
00 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Jupyter Notebook
9.5
22 Ratings
16% above category average
OpenText Magellan
-
Ratings
Interactive Data Cleaning and Enrichment
10.021 Ratings
00 Ratings
Data Transformations
10.022 Ratings
00 Ratings
Data Encryption
8.514 Ratings
00 Ratings
Built-in Processors
9.314 Ratings
00 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Jupyter Notebook
9.3
22 Ratings
11% above category average
OpenText Magellan
-
Ratings
Multiple Model Development Languages and Tools
10.021 Ratings
00 Ratings
Automated Machine Learning
9.218 Ratings
00 Ratings
Single platform for multiple model development
10.022 Ratings
00 Ratings
Self-Service Model Delivery
8.020 Ratings
00 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
Jupyter Notebook
10.0
20 Ratings
17% above category average
OpenText Magellan
-
Ratings
Flexible Model Publishing Options
10.020 Ratings
00 Ratings
Security, Governance, and Cost Controls
10.019 Ratings
00 Ratings
BI Standard Reporting
Comparison of BI Standard Reporting features of Product A and Product B
Jupyter Notebook
-
Ratings
OpenText Magellan
7.0
2 Ratings
16% below category average
Customizable dashboards
00 Ratings
7.02 Ratings
Report Formatting Templates
00 Ratings
7.01 Ratings
Ad-hoc Reporting
Comparison of Ad-hoc Reporting features of Product A and Product B
Jupyter Notebook
-
Ratings
OpenText Magellan
8.3
3 Ratings
3% above category average
Drill-down analysis
00 Ratings
8.03 Ratings
Formatting capabilities
00 Ratings
8.03 Ratings
Integration with R or other statistical packages
00 Ratings
9.01 Ratings
Report sharing and collaboration
00 Ratings
8.02 Ratings
Report Output and Scheduling
Comparison of Report Output and Scheduling features of Product A and Product B
Jupyter Notebook
-
Ratings
OpenText Magellan
8.3
2 Ratings
1% above category average
Publish to Web
00 Ratings
8.02 Ratings
Publish to PDF
00 Ratings
8.02 Ratings
Report Versioning
00 Ratings
9.02 Ratings
Report Delivery Scheduling
00 Ratings
8.02 Ratings
Data Discovery and Visualization
Comparison of Data Discovery and Visualization features of Product A and Product B
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.
If you do not have a large budget and are a large organization, I would steer clear of Actuate. If you are looking to do very complex washboarding, I would not use them. Your developers have to be very skilled to work with this. Plan to bring in consultants if necessary to help your process. Adhoc reporting is weak. If your pricing is user based and you expand, this could be very expensive.
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.
I am no longer working for the company that was using Actuate but I believe they would continue to use it because the stitching costs would be to high. It would require a complete rewrite of the reports and the never version of Actuate (BIRT) even required an almost complete report rewrite
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.
It is quite intuitive to use. It is fit specifically for doing sentiment, emotion, and intention analysis as well as text classification and text summarization. I would have given 10 if it is fit for the purpose of doing image processing and analysis as well. There is a huge market to analyze video and image data.
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.
It is vastly superior to these in many ways, for complex reporting it is a much more sophisticated solution. Visualizations are very good. Javascript extensibility is very powerful, others don't support this or as well. Pentaho and MS are both OLAP oriented. Pentaho is moving more toward big data, which was not our primary focus. Others are stuck in the Crystal Reports Band metaphor.
Actuate can handle 50 to 60 sub reports inside a report very well.
Dynamically creating the datasource, chart, graph, reports are the main advantages. We can do any level of drilling, and can create a performance matrix dashboard efficiently.