Anaconda is an enterprise Python platform that provides access to open-source Python and R packages used in AI, data science, and machine learning. These enterprise-grade solutions are used by corporate, research, and academic institutions for competitive advantage and research.
$0
per month
Splunk Enterprise Security
Score 8.3 out of 10
N/A
Splunk Enterprise Security is an analytics-driven SIEM that helps to combat threats with actionable intelligence and advanced analytics at scale.
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Pricing
Anaconda
Splunk Enterprise Security
Editions & Modules
Free Tier
$0
per month
Starter Tier
$15
per month per user
Business
$50
per month per user
Custom
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Offerings
Pricing Offerings
Anaconda
Splunk Enterprise Security
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
Users within organizations with 200+ employees/contractors (including Affiliates) require a paid Business license. Academic and non-profit research institutions may qualify for exemptions.
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.
Based on my experience, Splunk is a strong git for some environments and a poor match for others. The distinction is primarily based on infrastructure complexity and budget. It's perfect for large enterprises with a mix of on-prem/cloud infrastructure. It's not a perfect match for small teams with restricted resources.
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.
Writes Powerful Queries: The queries that can be written using the Splunk Query Language are very powerful and highly customizable to meet every need. Ex: Writing queries to search the intersection of two different sources like Network and Endpoint Logs.
Offers Dashboard Abilities: Helps build complex panels for Dashboards in addition to providing several out-of-the-box panels. Ex: creating panels to calculate the performance of analysts in a given timezone.
Helpful Search Aids: It helps to set up complex custom alerts very easily. The interesting fields section is very helpful while threat hunting. Ex: It shows all the users and the frequency of each in a failed login event. The user list on the interesting fields is useful to look for suspicious logins.
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.
Improved User Interface Customization: While the interface is generally intuitive, providing more options for users to customize their dashboards and views would enhance the overall user experience. Tailoring the interface to specific roles or use cases could be a valuable addition.
Simplified Alert Management: Streamlining the process of managing alerts, such as grouping or categorizing them based on severity or type, would make it easier for security teams to prioritize and respond to incidents effectively.
Expanded Threat Intelligence Feeds: Increasing the variety and sources of threat intelligence feeds available within ES would provide a broader context for identifying and mitigating emerging threats, ensuring a more comprehensive defense against evolving attack vectors.
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.
Maintaining hundreds or even 1000+ SOC use cases is really difficult, considering that the Data sources may not always send the data. A module that detects data freshness issues and detect data format changes would be a great help. the main challenge today using Splunk Enterprise Security is making sure that the detection rules are still working properly given all the changes that occur in data source applications. Also, maintaining the data collects on tens of thousands of servers and more than 100k workstations is a real company IT challenge: the splunkbase forwarder may not support old OS anymore, while these are the most important to monitor. Moving to the Open Telemetry collector has become essential so that only 1 agent is required for both SIEM and application observability.
It takes a long time for items to load if you are just generally searching through logs. It is best to use the data models which load faster but can be strange in terms of what is coming from which logs where. Yes, you can look it up, but this also requires familiarity with where things are and how to look them up.
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.
It's good when it's responsive, but I've had times where I had to wait quite a while for a response. But these are typically the exceptions rather than the rule. When you do get a response it is always well-informed and appropriate. I would say they've been trending better over time with this.
I experienced only on-line training, but the trainers were very professional and competent. Maybe it could be more useful if they also have an experience in projects because sometimes they didn't have a real project experience to communicate to the students. Anyway, it was very interesting and I learned many thing that's very difficoult (or maybe impossible!) to have by myself, aven if I have more than 10 years of Splunk activity experience.
It was very interesting and I learned many thing that's very difficoult (or maybe impossible!) to have by myself. The only problem was that, when I worked with the Splunk Professional Services, I found some difference between the training contents and the information from PS. In addition is required a long experience on Splunk Enterprise for the data ingestion part, in other words I'm able to work with ES because I'm worling on Splunk since 11 years, otherwise I'd some problem.
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!
Splunk enterprise is the only solution that we’ve been able to identify that provides risk based alerting, which allows our SOC to reduce analyst fatigue which would be a huge problem without it. Before RBA, there were thousands of alerts a day and it was impossible to review all of them
for my exterience, unit pricing and billing frequency are correct. As I already said, I hint to have more discount flexibility, expecially with new customers, because there are competitors less expensive and very aggressive that are dangerous. In addition the possibility to don't pay the license for the development period could be a very interesting feature for the final customers.
- 8 out of 10 and took 2 for the data pipeline and administration part. Even if you'd like to improve yourself or your team, you have to pay a lot of money and it could be more than GIAC education + cert. - Normalization for Data models and CPU-based searches can be a problem sometimes.
I had a fantastic experience with Splunk Professional Services: they worked with us in our last SON project (a SOC migration for a very large customer) and helped to build a multi tenent environment even if ES isn't a multi tenant platform. Th Splunk PS was a very professional and competent people, he is italian and was able to speak with our italian customers.
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.
We have a 100% success rate on all our ES implementations due to the amazing documentation and Splunk enablement on the subject.
Our Splunk ES business has grown 100% YoY for the last 3 years.
In terms of long term management and maintenance, ES has been highly stable and predictable, reducing our overhead on costly services team for ad hoc maintenance work.
