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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pandas
Score 10.0 out of 10
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pandas is an open source, BSD-licensed library providing high-performance data structures and data analysis tools for the Python programming language. pandas is a Python package providing expressive data structures designed to make working with “relational” or “labeled” data both easier. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python.
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Pricing
OpenText Magellan
pandas
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
OpenText Magellan
pandas
Free Trial
No
No
Free/Freemium Version
No
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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More Pricing Information
Community Pulse
OpenText Magellan
pandas
Features
OpenText Magellan
pandas
BI Standard Reporting
Comparison of BI Standard Reporting features of Product A and Product B
OpenText Magellan
7.0
2 Ratings
15% below category average
pandas
-
Ratings
Customizable dashboards
7.02 Ratings
00 Ratings
Report Formatting Templates
7.01 Ratings
00 Ratings
Ad-hoc Reporting
Comparison of Ad-hoc Reporting features of Product A and Product B
OpenText Magellan
8.3
3 Ratings
3% above category average
pandas
-
Ratings
Drill-down analysis
8.03 Ratings
00 Ratings
Formatting capabilities
8.03 Ratings
00 Ratings
Integration with R or other statistical packages
9.01 Ratings
00 Ratings
Report sharing and collaboration
8.02 Ratings
00 Ratings
Report Output and Scheduling
Comparison of Report Output and Scheduling features of Product A and Product B
OpenText Magellan
8.3
2 Ratings
0% below category average
pandas
-
Ratings
Publish to Web
8.02 Ratings
00 Ratings
Publish to PDF
8.02 Ratings
00 Ratings
Report Versioning
9.02 Ratings
00 Ratings
Report Delivery Scheduling
8.02 Ratings
00 Ratings
Data Discovery and Visualization
Comparison of Data Discovery and Visualization features of Product A and Product B
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.
There are a lot of libraries and ways to do visualization. Sometimes it is very confusing.
Error handling can be a challenge. Sometimes the error messages do not provide valuable clues for the debugging.
In our case, there are a bunch of different frameworks and libraries working together. I would rather work with one framework, well tuned for my use case
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
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.
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.
All these frameworks are great for gathering data and providing some initial analysis. But for real performance debugging work one needs more than tools provided by this tools. That's where the pandas excel.
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.
Performance debugging was time consuming and mostly poorly automated exploratory process. Once we started use pandas for these tasks, it really moved the needle. Pandas are instrumental to provide actionable insights. As a result we were able to improve notably cloud software resource utilization and performance
Analytics implemented with pandas allow us to detect and. address problems in our APIs before they are notable to our customers