Preface
[ 2 ]
The goal of this book is to give you some techniques to build a bridge in your
organization. We will assume you have a base understanding of what Splunk does, so that
we can provide a few tools to make your day to day life easier with Splunk and not get
bogged down in the vast array of SDK's and matching languages, and API's. These tools
range from intermediate to expert levels. My hope is that at least one person can take at
least one concept from this book, to make their lives easier.
What this book covers
Chapter 1, Application Logging, discusses where the application data comes from, and how
that data gets into Splunk, and how it reacts to the data. You will develop applications, or
scripts, and also learn how to adjust Splunk to handle some non-standardized logging.
Splunk is as turnkey, as the data you put it into it. This means, if you have a 20-year-old
application that logs unstructured data in debug mode only, your Splunk instance will not
be a turnkey. With a system such a Splunk, we can quote some data science experts in
saying "garbage in, garbage out".
Chapter 2, Data Inputs, discusses how to move on to understanding what kinds of data
input Splunk uses in order to get data inputs. We see how to enable Splunk to use the
methods which they have developed in data inputs. Finally, you will get a brief
introduction to the data inputs for Splunk.
Chapter 3, Data Scrubbing, discusses how to format all incoming data to a Splunk, friendly
format, pre-indexing in order to ease search querying, and knowledge management going
forward.
Chapter 4, Knowledge management, explains some techniques of managing the incoming
data to your Splunk indexers, some basics of how to leverage those knowledge objects to
enhance performance when searching, as well as the pros and cons of pre and post field
extraction.
Chapter 5, Alerting, discusses the growing importance of Splunk alerting, and the different
levels of doing so. In the current corporate environment, intelligent alerting, and alert
'noise' reduction are becoming more important due to machine sprawl, both horizontally
and vertically. Later, we will discuss how to create intelligent alerts, manage them
effectively, and also some methods of 'self-healing' that I've used in the past and the
successes and consequences of such methods in order to assist in setting expectations.
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