In this chapter we discussed where the lion's share of application data comes from and how that data gets into Splunk and how Splunk reacts to it. We mentioned good ways and things to keep in mind when developing applications, or scripts, and also how to adjust Splunk to handle some non-standardized logging. Splunk is as turnkey as the data you put into it. Meaning, if you have a 20-year-old application that logs unstructured data in debug mode only, your Splunk instance will not be turnkey. With a system like Splunk, we can quote some data science experts in saying garbage in, garbage out.

While I say that, I will add an addendum by saying that Splunk, mixed with a Splunk expert and the right development resources, can also make the data I just mentioned extremely valuable. It will likely not happen as fast as they make it out to be at a presentation, and it will take more resources than you may have thought. However, at the end of your Splunk journey, you will be happy. This chapter was to help you understand the importance of logs formatting and how logs are written. We often don't think about our logs proactively and I encourage you to do so.

Now that we have this understanding about logging and the types of data that applications generally write, we can move on to understanding what kinds of data inputs Splunk uses in order to get data in.

Determining which data input suits your use case is often the first part of getting data into Splunk, and with a wide variety of ways to do this, Splunk enables us to use the methods that they have developed, as well as allowing room for us to develop our own if necessary.