Altair Monarch (formerly Datawatch Monarch, acquired by Altair in December, 2018) works with both relational and multi-structured data including support for a wide range of formats including PDF, XML, HTML, text, spool and ASCII files. The product can access data from invoices, sales reports, balance sheets, customer lists, inventory, logs and more. According to the vendor, the system is easy to use, allowing users to quickly select any data source and automatically convert it into…
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Anaconda
Score 8.7 out of 10
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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.
The product is especially useful when you have real-time and/or time series data to analyze. If you have more mundane, simpler requirements, other products might do the job you need for less money (there are even some decent open source visualization tools you can find.) I know the product is very widely used in capital markets applications to monitor and analyze risk and price and volume changes; if you're working in that area, I don't think there's a better tool to use.
As a Data Analyst, it is my job to analyze large datasets using complex mathematical models. Anaconda provides a one-stop destination with tools like PyCharm, Jupyter, Spyder, and RStudio. One case where it is well suited is for someone who has just started his/her career in this field. The ability to install Anaconda requires zero to little skills and its UI is a lot easier for a beginner to try. On the other hand, for a professional, its ability to handle large data sets could be improved. From my experience, it has happened a lot that the system would crash with big files.
Creating a basic model to extract data from a report is very easy.
Advanced features like Calculated Fields and External Lookups allow you to augment the raw data.
You can create a "project" to automate the data extraction. Combined with Datapump (a separate DW app), you can fully automate the process once the raw report is generated.
Recently, we had some major sticker-shock when we wanted to upgrade Data Pump. It is an exceptional product, but when the price jumped from $6,000 to over $60,000, it was impossible to get the funds approved internally for the upgrade.
We also paid for yearly maintenance contracts which included Professional Services, but rarely found those services beneficial. However, we did receive all software upgrades for Datapump as part of the contract which we found to be very beneficial. However, with the new pricing, that is not longer the case.
Although I have generally had positive experiences with Anaconda, I have had trouble installing specific python libraries. I tried to remedy the solution by updating other packages, but in the end, things got really messed up, and I ended up having to uninstall and reinstall a total of about 4 times over the past 2 years.
If you have the free version of Anaconda, there is not much support. Googling questions and error messages are helpful, but there were times when I wished I would have been able to ask technical support to help me troubleshoot issues.
There were a few times when I tried to install tensorflow and tensorboard via Anaconda on a PC, but I could not get them to install properly. Anaconda allows you to create 'environments' , which allow you to install specific versions of python and associated libraries. You can keep your environments separate so they do not conflict with one another. Anyway, I ended up having to create several 'conda envrionments' just so I could use tensforflow/tensorboard and a few other utilities to avoid errors. This was somewhat annoying, because every time I wanted to run a specific model, I'd have to open up the specific conda environment with the appropriate python libraries.
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.
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.
Datawatch is very good value of money compared to QlikView; QlikView is really more of a BI tool and has a lot of functions that I didn't need. Datawatch is very strong in the real-time area where Tableau, Panorama, and Qlik don't do very well. If you need to set up a visual monitoring dashboard, Datawatch is the best product I've seen for that. if you want to do a lot of in depth statistical analysis of large databases, Tableau is probably a good option.
ANACONDA VS Alteryx Analytics: Even though I find Alteryx to be an excellent tool for managing extremely massive data, Anaconda is much better and easy for analytics. Anaconda VS. MicroStrategy Analytics: Compared with Anaconda, MicroStrategy Analytics is very difficult to use and counter-intuitive Anaconda VS. Power BI For Office 365: One of the main advantages of BI for Office 364 is its capacity to data connectivity. However, it's very hard to edit data connections, once BI for Office is deployed in other platforms
