Anaconda vs. Splunk Enterprise

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

I'm liking the newer products, and I'm looking forward to how they integrate with the overall product when they come together. Just log in and be able to query a large number of systems for similar issues or a unique one. That is a great fit for Splunk Enterprise, looking for a simple case or a simple String or something of that nature across multiple machines. It's a great fit for that to identify issues or particular software, whatever your scenario is, String, to find it across any particular server or group of servers, so that you can update or do a deployment or whatever it is you're looking to do.

Incentivized

• Installing packages is very easy with Anaconda. Anaconda comes with 'anaconda navigator', a terminal-like utility from which you can easily install R packages and python libraries.
• Launching R and python IDEs as well as Jupyter notebooks from anaconda navigator is simple, and Anaconda makes it very easy to keep these packages up-to-date.
• I really like the fact that if you don't want to install the full version of Anaconda, you can opt to install a lightweight version (called Miniconda) that includes less python libraries and only core conda. I've installed it when I didn't want to take up as much disk space as Anaconda requires, but it works just the same.

Incentivized

• Real-time + Scheduled alerts - i-e you can set up alerts which are actively monitoring your logs
• Pretty good response time for search results. With our key/value logging, Splunk makes it blazing fast to query the data.
• Dashboards provide insights into historical data
• Love how Splunk indexes all of the data and provides keys to search on

Incentivized

• More graphics need in Spyder book. If you work for couple of years then you will be bored with the graphics.
• Extra tools are required for making it secure. We uses extra tools for adding Username /Password to Jupyter.
• R Studio Hangs a lot when open from Anaconda Navigator.

Incentivized

• At times some queries can run slowly if indices are not on a portion of the query you use.
• Setup time initially can be difficult if your logs aren't stored in common locations or in a common way to write the log.
• Ability to ingest logs from different locations without having to change code to put logs in a certain place (pro and con).
• Searches can be a bit more difficult to look through if your log isn't pulled in a manner that is easy to read through splunk.

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

We are using Splunk extensively in our projects and we have recently upgraded to Splunk version 6.0 which is quite efficient and giving expected results. We keep track of updates and new features Splunk introduces periodically and try to introduce those features in our day to day activities for improvement in our reporting system and other tasks.

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

Splunk usability could be a little more friendly on correlations and log analysis. not for end users, but for the tech team, sometimes is hard to find good professionals to set up integrations and new dashboards when needed. For the high-level management, it can be overwhelming to read Splunk dashboards.

Incentivized

When properly setup and configured, Splunk is extremely reliable.

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

Splunk maintains a well resourced support system that has been consistent since we purchased the product. They help out in a timely manner and provide expert level information as needed. We typically open cases online and communicate when possible via e-mail and are able to resolve most issues with that method.

Incentivized

The online course was simple clear and described the main capabilities of the solution. There is also an initial module that can be done for free so anyone can familiarize themselves with the functionality of this solution. On the other hand, however, there could be more free online courses. Maybe even with a certificate, this would broaden the group of people who are familiar with the platform while increasing familiarity with the solution itself.

Smooth without too many major issues.

Incentivized

One of the main competitors to Anaconda can be Google products such as Colab. Colab gives you the flexibility to handle large datasets gives it an edge over Anaconda. But again, the ease of access and usability of Anaconda stacks up against Colab. Besides, Anaconda relies more on your machine which makes it safe to use.

Incentivized

A lot of products have natively inside their own dashboards and or their own logging repositories. And each one is difficult to learn or they're too complex or they're not verbose in the sense that they're not easy to mine the data that you're looking for. So that could be anything from the native logging that you find in other Cisco products. It's easier to use Splunk to draw the data that you're looking for as opposed to going to the individual's products themselves to get the logs that you're looking for.

Incentivized

Splunk can scale in to the petabyte per day range which of course is awesome

• Positive impact - Multiple options for data presenting , visualizing and sharing. (Eg: R-Markdown).
• Positive impact - Ease of access to build complex machine learning models. (I work in NLP, it has multiple built in models to analyze the various contexts).
• Positive impact - Conda package let's to deal with external packages which can be used in Jupyter.

Incentivized

• Ease of collecting IP for blacklisting.
• Generation of metrics against compromised accounts based on location and time of the year. It helped in launching phishing education campaign before hitting the most vulnerable month of the year.
• It helped in neutralizing vulnerable word-press sites across the campus, leading to the decrease of account compromise.

Incentivized