We now have enough understanding of how to use Splunk for analytics and visualization. In this chapter, we will go through how Splunk can be integrated with any present/legacy proprietary applications in detail along with examples. Splunk provides an Software Development Kit (SDK) on almost all programming languages, such as .NET, Java, Python, and so on. The SDK can be used to integrate with applications to get better, efficient, and faster (real-time) results in the applications. You will also learn how Splunk can be integrated with other tools such as R for analytics and Tableau for visualization.
The following are the topics that will be covered in this chapter:
- The Splunk SDK
- Installing the Splunk SDK
- The Splunk SDK for Python
- Splunk with R for analytics
- Splunk with Tableau for visualization
An SDK plays a very important role in integrating the power of Splunk's real-time analytics and visualization in legacy/proprietary applications. Industries and organizations use some or the other tool to generate analytics and visualization. However, legacy/propriety tools may not be scalable to handle big data and provide real-time analytics, and hence Splunk comes to the rescue. It may not be possible to replace the current tools used in the workflow, and hence, the Splunk SDK can be used to integrate with the current tool to utilize the power of Splunk.
The Splunk SDK is available in almost all the programming languages, such as C#, Java, PHP, Ruby, Python, and JavaScript.
The following are the scenarios where the Splunk SDK can be useful:
- It can be integrated with current workflow tools seamlessly to utilize the power of Splunk's big data analytics and visualization in real time.
- An SDK can help in logging data in the Splunk server directly from the application; that is, rather than storing the logs in a text file and then uploading data logs on Splunk, the logs can be directly sent on the Splunk server using an SDK.
- It can be integrated with other analytics and big data tools, such as R, Tableau, and so on.
Let's understand the use of the Splunk SDK with the help of an example. Let's assume that we have a banking tool that is used in ABC Bank to do all kinds of transactions, internal process management, inventory and asset management, and so on. The tool logs all the transactions in a database and is used for various purposes, such as fraud detection, fraudulent transactions, cash inflow and outflow, analytics, and various other insights required by the bank. The database logging mechanism can handle a few thousand to a few lac transactions in a day, but due to advancements in technology, the number of transactions and workflow has increased to millions of transactions per minute. If the bank continues to use the pre-existing legacy tool, then it would take hours to a few days to generate insight. Let's say that there was a fraudulent transaction, but what is the use of finding that when the loss is already done. Here, the Splunk SDK comes to our rescue, using which informed business decisions can be taken in real time. In these scenarios, the logs can be directly sent to Splunk using the Splunk SDK, and the generated analytics and visualization can be shown in the pre-existing application. Also, Splunk's capability of alerting, custom alert action, can be used to take critical business decisions automatically.