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Here are some stack decisions, common use cases and reviews by companies and developers who chose Splunk in their tech stack.
We are currently exploring Elasticsearch and Splunk for our centralized logging solution. I need some feedback about these two tools. We expect our logs in the range of upwards > of 10TB of logging data.
I am using Splunk, but trying to change to an open-source software; what I need is something that reads in "real-time" an archive, it's not exactly a log, it's actually data itself (there is a lot of data, by the way), stores this data/log for a long time (more than 2 years), has a pretty easy way to search the data read (so some search language where I can search by IP for example), and a way to connect to the software, where I can search by API.
I am designing a Django application for my organization which will be used as an internal tool. The infra team said that I will not be having SSH access to the production server and I will have to log all my backend application messages to Splunk. I have no knowledge of Splunk so the following are the approaches I am considering: Approach 1: Create an hourly cron job that uploads the server log file to some Splunk storage for later analysis. - Is this possible? Approach 2: Is it possible just to stream the logs to some splunk endpoint? (If yes, I feel network usage and communication overhead will be a pain-point for my application)
Is there any better or standard approach? Thanks in advance.
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Splunk's Features
- Predict and prevent problems with one unified monitoring experience
- Streamline your entire security stack with Splunk as the nerve center
- Detect, investigate and diagnose problems easily with end-to-end observability