Logstash vs. Splunk Cloud Platform

Logstash is a must in an ELK stack, which I am sure is going to be the #1 case. At any point when you have several sources, Logstash can be the common point to aggregate, and categorize those data. Then send this new data to its destination. Very handy. It is free and open source. It may not be appropriate to analyze data-sets dependent on each other but from a different data source. Reason being Logstash works on data at hand, and not wait for other data to arrive. It would be unwise for Logstashh to handle complicated, long-running transformations because this is injected and ejected. The faster you do it, the safer.

Incentivized

I will highly recommend this software because using Splunk
Cloud has helped us become more proactive about handling our security concerns
and better manage our environment. It is one of the finest security software
that is easy to use and also provides analytics. It has excellent features like
creating dashboard security and managing features etc. So you must give it a try once!

Incentivized

• Plugin ecosystem allows modular extensions.
• Tight integration into the Elastic.com products of Beats and Elasticsearch, so minimal setup is required when using those tools.
• Filter plugins are powerful for extracting and enriching input data.

Incentivized

• With Splunk Cloud you get the advantage of moving from POC to Production in a matter of days rather than in months allowing the Business to gain a lot.
• Takes you away from managing infrastructure/administration, allows saving time & money. Reduce the overall TCO (Total Cost of Ownership)
• Move from Reactive to Proactive Monitoring
• Highly secure environment at your finger-tips

Incentivized

• Memory: Logstash is a HOG, if you are deploying it on commodity (i.e. cheap and old) hardware: You will need at least 2GB, just for Logstash. So don't expect to run your entire ELK stack on one AMD Athlon machine.
• Overlap: Logstash fills in an area of the ELK stack that makes the most sense: as a log file transformer / shipper. However, if you start breaking that stack, with the addition of other components- you start seeing where features of Logstash may be implemented or solved in the additional components much easier (or better, or to a higher degree of resolution)
• More Overlap: Since my team employs Syslog-ng extensively- Logstash can sometimes get in the way (and this may be a problem for DevOps stacks overall): You can configure Syslog to record certain information from a source, filter that data, and even export that data in a particular format. Logstash will pick that data up, and then parse it. However, if you don't keep your Syslog-ng configuration files, and your Logstash configuration files in sync, your results will not be what you expected, and this will translate into (sometimes) hours/days of work, hunting down a line item in a configuration file.

Incentivized

• Some of their more advanced features, like ITSI, Machine Learning, or Security Analytics, can be very challenging to setup and configure.
• Splunk Cloud support has been a challenge in the past. They are getting better, but they have had problems responding in a timely manner to issues.
• These are only some minor observations of things I have had to deal with. In general, Splunk is a solid product that is fantastic to use.

Incentivized

Ease of use and have all the features we need

Incentivized

As I said earlier, for a production-grade OpenStack Telco cloud, Logstash brings high value in flexibility, compliance, and troubleshooting efficiency. However, this brings a higher infra & ops cost on resources, but that is not a problem in big datacenters because there is no resource crunch in terms of servers or CPU/RAM

What it does well:

- Powerful SPL query language for advanced users

- Excellent visualization dashboards

- Comprehensive documentation and community support

Where it needs work:

- Steep learning curve for SPL syntax

- Non-Intuitive UI for beginners

- Complex administration and data model configuration

- Search performance degrades with poor query optimization

Bottom line: Enterprise-grade tool requiring dedicated training investment. Best for teams with experienced analysts.

Incentivized

Splunk Cloud support is sorely lacking unfortunately. The portal where you submit tickets is not very good and is lacking polish. Tickets are left for days without any updates and when chased it is only sometimes you get a reply back. I get the feeling the support team are very understaffed and have far too much going on. From what I know, Splunk is aware of this and seem to be trying to remedy it.

Incentivized

MongoDB and Azure SQL Database are just that: Databases, and they allow you to pipe data into a database, which means that alot of the log filtering becomes a simple exercise of querying information from a DBMS. However, LogStash was chosen for it's ease of integration into our choice of using ELK Elasticsearch is an obvious inclusion: Using Logstash with it's native DevOps stack its really rational

Incentivized

Microsoft Dynamics is far more complex and far more costly to implement and configure in comparison to Splunk. It can be useful for integrations into existing Microsoft databases. Grafana is quick and easy to deploy and configure. However, it lacks the scale required for an enterprise of our size. It is great for smaller test cases.

Incentivized

• It is very difficult to give any figures on ROI, as it depends on many factors, and in a Telcocloud environment, it is much complex to find out; however, I would give some points below on ROI
• ROI based on flexibility is very high, as it reduces the time to find RCA
• ROI based on integration is very high because it supports multi-vendor environments, avoiding vendor lock-in & works across multi-cloud setups
• ROI on resource consumption is less because Logstash in 2-3 times more resource-intensive as compared to its lightweight alternatives resulting in latency

• Splunk Cloud has had a positive ROI in helping more efficiently track the cause of Help Desk Tickets.
• The billing model which is based on the amount of data from logs uploaded doesn't alert if a threshold is approaching. This can have a negative ROI.
• The training that I have taken while in-depth and focused is pretty expensive.

Incentivized