Which method is used to reduce data input costs in Splunk?

Implementing data compression techniques is an effective method for reducing data input costs in Splunk. When data is compressed before it is ingested into Splunk, the overall storage requirements decrease, allowing organizations to maximize their use of available resources and reduce costs associated with data storage and input. Compression techniques can significantly minimize the amount of data transferred and stored, leading to cost savings especially when dealing with large volumes of data.

This method is particularly important in environments where data retention and performance are critical. By efficiently managing data size through compression, organizations can ensure that they optimize storage space, improve data processing times, and maintain lower costs associated with both storage and indexing.

Other methods, such as manual uploads, may not be efficient for large-scale data input and could increase costs rather than reduce them. Creating redundant data sources typically leads to increased storage and management costs. Limiting the number of users accessing data does not directly relate to data input costs and instead may affect data accessibility and collaboration.