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
Jedox
Score 6.9 out of 10
N/A
Jedox is a Business Intelligence and Corporate Performance Management solution. According to the vendor, their solution’s unified planning, analysis and reporting empowers decision makers from finance, sales, purchasing and marketing. Additionally, the vendor says this solution helps business users work smarter, streamline business collaboration, and make insight-based decisions with confidence. The vendor also says 1,900 organizations in 127 countries are using Jedox for real-time planning…
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.
Best suited for financial consolidation and / or as a highly customized and compact EPM / BI solution (up to 100 CCU) with individual workflows, planning and reporting functionalities, with moderate number of users (no restrictions for any industry, all industries are covered well). It also has advanced reporting & data analysis requirements and provides an integration and reporting layer of imported data from different external systems (via ETL). It can help with migrating your legacy Excel-based business models to the Web. It is not well suited for Enterprise BI applications with expecting >500 CCU (users at the same time working with the system) - this may cause serious performance issues, as all data is kept in RAM. Jedox is also less suited for applications with heavy document management requirements (document management is not an out of the box functionality in Jedox and rather requires custom development through custom widgets etc.).
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.
Diversity. Jedox can be applied to many different use cases from small to large deployments and from budgeting to enterprise class BI solutions. But rarely is one tool able to fulfill all of these requirements in one organisation. This value proposition can be complicated for prospective users.
Awareness. Jedox punches above its weight in capability and scalability, but not enough people have heard about it and therefore procurement processes can be drawn out as a result.
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 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.
To me Jedox deserves 10/10 because it is a consistent one-in-all platform with a modern look and feel. It is intuitive to use and allows you to make intuitive applications integrating traditional business intelligence with performance management functionality. It certainly has a short learning curve, especially for those that are familiar with MS Excel. An example: I've lost count but Jedox it is available in more than 25 languages. Another: Jedox does not require programming skills... it is developed to be used by the business.
Jedox has very few bugs. Reports are available through an Excel add-in, the web and/or mobile device (IOS/Android). In my opinion, availability also means high performance, not having to wait for the system to give you the required reports, analysis, dashboards instantly.
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.
Jedox support in general is a professional and fast responding team. An easy-to-use ticketing system is in place. Bug-related questions are solved fast (responses come usually in a few hours after the question), but some questions / tickets, that are not Jedox-related bugs (for example some advanced questions about Jedox functionality), may be forwarded to Application Management team for further processing and then it may take several days or even weeks to get a response here -> there is room for improvement here.
The implementation of SSO, SAML Authentication, HTTPS, Server splitting (Frontend / Backend servers) could be more standardized and made more user friendly to set up (e.g. via setup guide). Otherwise the implementation of Jedox is quick and simple when compared to other similar technologies.
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!
Calumo is similar product to Jedox. I have used it extensively in my previous role. It was a major contender when we evaluated a BI platform for NIDA. Calumo is a great product as well and it was a very close call. Where we found Jedox to be a better fit for NIDA was the ability to prepare dynamic reports with ease without the need to learn MDX which was used extensively by Calumo to make dynamic reports which expand or shrink based on the underlying data. Another major benefit we saw in Jedox was the whole ETL process could be managed within Jedox instead of doing it in SQL server which negates having a dedicated SQL specialist role when the scale expands.
Scalability is often another word for speed. Given enough data, enough users or enough calculations, the tool becomes slower and slower. You will find that Jedox has a very high performance that can even be increased by the use of grafical cards. Other thaen that it does not only offer BI (looking back based on historical ERP data) but also allows you to look forward through integrated budgetting, planning, forecasting, workflow and collaboration. Not easy to find a tool that can support so much business functionality. So, also pretty scalable in that respect.
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.
Financial budgeting and Forecasting are done in a centralized fashion in Jedox now instead of a decentralized excel based approach. A lot of cost savings and improved reliability
Easy to use self-help Dashboards and detailed reports