Anaconda vs. Oracle Analytics Server

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.

Incentivized

The entire deployment and configuration stage was easy and it was accessible across the organization in a matter of days. Oracle Analytics Server being a cloud-based solution helped right from Input of Bulk Data to Info preparation, Data cleaning, and finally Data Modeling. Everything was visual and the Help Wizard was very intuitive.

Incentivized

• 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.

Incentivized

• Oracle helps with easy customization of reports.
• It helps with the creation of dashboards and the integration of different data sources.
• Oracle BI was a package used for multiple users across our organization - publishing and distributing reports was a breeze.

Incentivized

• It can have a cloud interface to store the work.
• Compatible for large size files.
• 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.

Incentivized

• More AI but with human back up.
• Less mumbo jumbo so even IT guys/gals can more easily work.
• More statistics (math) and orientation for those not familiar with it.
• More live as well as virtual (live) show casing of real situations in business.

Incentivized

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.

Incentivized

Scalability and rich integration capabilities. In the future, if we go with Hyperion for the Financial Consolidation and planning purposes -BI integration with Hyperion is going to be much simpler as it has native interface connectivity and even integration capabilities with well known CRM products (Siebel) and ERP Products (Oracle EBS, Peoplesoft, SAP) is going to be easy and straight forward.

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.

Incentivized

Great, if you are limited to using it along with other Oracle products; sadly, not if you are integrating with other products, which can be a challenge. It is a great product with tons of functionality and great integration with other in-house platforms. Great visuals and customization for data and analytics to provide decision-making data and analysis.

Incentivized

No problems, it's that simple.

Incentivized

Other than anomalies or expected quirks, there were never any earth-shattering delays.

Incentivized

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.

Incentivized

It's fast, efficient and easy to work with. Scheduling could help when planning out a day however overall it's a big plus when rolling out and supporting Oracle.

Incentivized

Oracle University offers very in-depth training.

It was a private training site.

Incentivized

A properly implemented Endeca solution performs extremely well on the largest of datasets and it positions your organization to immediately achieve your ROI.

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!

Incentivized

Oracle BI works pretty well and has been well acknowledged and appreciated by the business users and power users who develop reports and dashboards. Compared to other tools, Oracle BI object development is easier and has a quick turnout. Using Oracle BI with Essbase and Oracle Exadata db, we believe the performance rendering data is quite good. We are using MicroStrategy as well almost equally, but for critical reporting needs considering the back end is Oracle and Oracle Financial Services Analytical Applications (OFSAA) we have chosen OBIEE.

Incentivized

We have seen the results of this in our initial research and are not surprised that Oracle does this like it does soo many other things in this area, so well.

Incentivized

• 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.

Incentivized

• We've used OBIEE (or it's previous named product) for over 13 years and it's still the most used tool for BI by the business.
• We moved our largest business system off of Business Object into OBI so we could gain improved performance, reliability, and easier management of metadata.