Anaconda provides access to the foundational open-source Python and R packages used in modern AI, data science, and machine learning. These enterprise-grade solutions enable corporate, research, and academic institutions around the world to harness open-source for competitive advantage and research. Anaconda also provides enterprise-grade security to open-source software through the Premium Repository.
$0
per month
OpenText Magellan
Score 9.0 out of 10
N/A
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
Anaconda
OpenText Magellan
Editions & Modules
Free Tier
$0
per month
Starter Tier
$9
per month
Business Tier
$50
per month per user
Enterprise Tier
60.00+
per month per user
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Pricing Offerings
Anaconda
OpenText Magellan
Free Trial
No
No
Free/Freemium Version
Yes
No
Premium Consulting/Integration Services
Yes
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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Community Pulse
Anaconda
OpenText Magellan
Features
Anaconda
OpenText Magellan
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Anaconda
9.3
25 Ratings
11% above category average
OpenText Magellan
-
Ratings
Connect to Multiple Data Sources
9.822 Ratings
00 Ratings
Extend Existing Data Sources
8.024 Ratings
00 Ratings
Automatic Data Format Detection
9.721 Ratings
00 Ratings
MDM Integration
9.614 Ratings
00 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Anaconda
8.5
25 Ratings
1% above category average
OpenText Magellan
-
Ratings
Visualization
9.025 Ratings
00 Ratings
Interactive Data Analysis
8.024 Ratings
00 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Anaconda
9.0
26 Ratings
10% above category average
OpenText Magellan
-
Ratings
Interactive Data Cleaning and Enrichment
8.823 Ratings
00 Ratings
Data Transformations
8.026 Ratings
00 Ratings
Data Encryption
9.719 Ratings
00 Ratings
Built-in Processors
9.620 Ratings
00 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Anaconda
9.2
24 Ratings
9% above category average
OpenText Magellan
-
Ratings
Multiple Model Development Languages and Tools
9.023 Ratings
00 Ratings
Automated Machine Learning
8.921 Ratings
00 Ratings
Single platform for multiple model development
10.024 Ratings
00 Ratings
Self-Service Model Delivery
9.019 Ratings
00 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
Anaconda
9.5
21 Ratings
11% above category average
OpenText Magellan
-
Ratings
Flexible Model Publishing Options
10.021 Ratings
00 Ratings
Security, Governance, and Cost Controls
9.020 Ratings
00 Ratings
BI Standard Reporting
Comparison of BI Standard Reporting features of Product A and Product B
Anaconda
-
Ratings
OpenText Magellan
7.0
2 Ratings
15% 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
Anaconda
-
Ratings
OpenText Magellan
8.3
3 Ratings
4% 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
Anaconda
-
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 have asked all my juniors to work with Anaconda and Pycharm only, as this is the best combination for now. Coming to use cases: 1. When you have multiple applications using multiple Python variants, it is a really good tool instead of Venv (I never like it). 2. If you have to work on multiple tools and you are someone who needs to work on data analytics, development, and machine learning, this is good. 3. If you have to work with both R and Python, then also this is a good tool, and it provides support for both.
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.
Anaconda is a one-stop destination for important data science and programming tools such as Jupyter, Spider, R etc.
Anaconda command prompt gave flexibility to use and install multiple libraries in Python easily.
Jupyter Notebook, a famous Anaconda product is still one of the best and easy to use product for students like me out there who want to practice coding without spending too much money.
I used R Studio for building Machine Learning models, Many times when I tried to run the entire code together the software would crash. It would lead to loss of data and changes I made.
It's really good at data processing, but needs to grow more in publishing in a way that a non-programmer can interact with. It also introduces confusion for programmers that are familiar with normal Python processes which are slightly different in Anaconda such as virtualenvs.
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
I am giving this rating because I have been using this tool since 2017, and I was in college at that time. Initially, I hesitated to use it as I was not very aware of the workings of Python and how difficult it is to manage its dependency from project to project. Anaconda really helped me with that. The first machine-learning model that I deployed on the Live server was with Anaconda only. It was so managed that I only installed libraries from the requirement.txt file, and it started working. There was no need to manually install cuda or tensor flow as it was a very difficult job at that time. Graphical data modeling also provides tools for it, and they can be easily saved to the system and used anywhere.
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.
Anaconda provides fast support, and a large number of users moderate its online community. This enables any questions you may have to be answered in a timely fashion, regardless of the topic. The fact that it is based in a Python environment only adds to the size of the online community.
I have experience using RStudio oustide of Anaconda. RStudio can be installed via anaconda, but I like to use RStudio separate from Anaconda when I am worin in R. I tend to use Anaconda for python and RStudio for working in R. Although installing libraries and packages can sometimes be tricky with both RStudio and Anaconda, I like installing R packages via RStudio. However, for anything python-related, Anaconda is my go to!
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.
It has helped our organization to work collectively faster by using Anaconda's collaborative capabilities and adding other collaboration tools over.
By having an easy access and immediate use of libraries, developing times has decreased more than 20 %
There's an enormous data scientist shortage. Since Anaconda is very easy to use, we have to be able to convert several professionals into the data scientist. This is especially true for an economist, and this my case. I convert myself to Data Scientist thanks to my econometrics knowledge applied with Anaconda.
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.