Nowadays, enterprises worldwide are becoming more data-driven and agile, trying to effectively meet their business objectives and better cater to consumer demands. However, this has resulted in the need to manage an excessive amount of data with precision. This has been made possible by DevOps and various other associated methodologies introduced in the past few years.

Splunk is one such method for managing machine-generated data without compromising its quality, performance, and more. Today, organizations are increasingly switching to or adopting Splunk to automate data assessment, monitoring, and analysis.

In this article, you will learn the basics of Splunk, as well as what makes it the best solution for complete data management.

But, let’s first understand:

What is Splunk?

An advanced and scalable technology and software platform, co-founded by Rob Das and Eric Swan in 2003, Splunk is used by individuals and organizations for managing, monitoring, searching, analyzing, and visualizing any machine-generated data in real-time.

Currently, Splunk is the most widely used platform for parsing, capturing, indexing, and correlating volumes of real-time machine-generated data and getting insightful results in the form of reports, graphs, dashboards, alerts, and more. Organizations are leveraging this software to analyze the logs, structured, or semi-structured data, generated in various processes, with proper data modeling.

Moreover, it has built-in features to recognize the data types, field separators, and optimize the search process, which provides easy access to data to the organizations. This enables organizations to perform easy diagnostics and get solutions to various business problems, which further helps them stay ahead of the competition.

Splunk Products:

Developed initially to meet the need for comprehensible and actionable data reporting for executives outside the IT department, Splunk has since introduced various products that capture, index, and correlate real-time data in a searchable repository and identify data patterns, provide metrics, diagnose problems and provide intelligence for business operations.

These have gained tremendous popularity worldwide due to the numerous benefits they offer.

Hence, the various versions of Splunk are:

  • Splunk Enterprise: Splunk Enterprise is the easiest way to aggregate and analyze data from websites, applications, mobile apps, etc., and take meaningful action for your business quickly. Used by large-scale IT businesses, this tool helps turn data into effective outputs with intuitive Machine Learning powered analytics.
  • Splunk Cloud: This is a fast, flexible, secure, and cost-effective hosted platform that has fewer infrastructure requirements and can be availed from Splunk or using AWS cloud platform. Moreover, it offers the same features as the enterprise version of Splunk.
  • Splunk Light: A comprehensive and free version of Splunk Illumination, Splunk Light is the one-stop solution for small IT environments that need to automate log search, analysis, and server and network monitoring. It scans, records, and edits log data, enabling powerful search queries, dynamic alerts, and visual dashboards for real-time reporting and analysis. However, compared to others, it offers limited features and functionalities.

Splunk Features:

Splunk is a popular data analysis and monitoring system that is gaining momentum among IT and Big Data organizations because of its performance, scalability, and innovative style of collecting and presenting the data. However, the features that play a key role in its popularity are:

  • Data Indexing: Splunk helps index data, enabling faster searching and querying on different conditions.
  • Data Searching: It uses indexed data to create metrics, predict future trends, and identify patterns, which plays an integral role in the searching process.
  • Data Ingestion: Can ingest different data formats such as JSON, XML as well as unstructured data.
  • Data Model: With Splunk, one can model indexed data into one or more data sets, based on specialized domain knowledge, which enables end-users to navigate it easily.
  • Dashboard: One of the prominent features of Splunk, it offers a Dashboard that depicts the search results in the form of charts, reports, pivots, graphs, etc. helping businesses to make decisions effectively.
  • Alerts: Splunk offers alerts in the form of trigger emails or RSS feeds whenever specific criteria are found in the data being analyzed.
  • Intuitive User Experience: Enables instant access to relevant apps and content to improve user productivity.
  • Powerful Analytics: Offers faster and easier data analysis and visualization for business users.

With the basics of Splunk clear, let us move on to defining its major components.

Splunk Components:

There are three major components critical for Splunk to work accurately and perform precise data aggregation and analysis. These components and their functions are:

  • Splunk Forwarder: As suggested by its name, Splunk forwarder forwards data collected from the remote machines for indexing. This is further categorized into:
  • Splunk Universal Forward
  • Splunk Heavy Forward

These are further explained below in the architecture section.

  • Splunk Indexer: It parses and indexes the data fed from Splunk forwarder in real-time, making it easy to execute search operations efficiently.
  • Splunk Search Head: This is the user interface where users interact with Splunk and perform data searching, analysis, visualization, and reporting. There are two types of search head:
  • Search Head: In this user interface, only the data is retrieved based on the keywords. It does not involve data indexing.
  • Search Peer: It accommodates both search results and performs indexing.

Splunk Architecture:

Splunk has a robust architecture that helps retrieve the desired output from the complex data, as can be seen in the following image.

Splunk Architecture, in short, involves various components and functionalities that act as different phases of the process that plays a major role in retrieving the desired result. These components are:

  • Universal Forward (UF): A lightweight element that can easily be installed at the client-side or application-side, Universal Forwarder helps push data to the Heavy Splunk Forwarder. Its main purpose is to forward the log data from the server.
  • Load Balancer (LB): The objective of this phase of the Splunk architecture is to distribute the network or application traffic over a cluster of servers. Splunk uses a default load balancer, which enhances the distribution of workloads over multiple computing resources.
  • Heavy Forward (HF): The second type of Splunk Forwarder, Heavy Forwards is the heavy element that enables teams to filter data.
  • Indexer (LB): This element of Splunk stores and automatically indexes the filtered data to improve the Splunk search performance.
  • Search Head (SH): Helps distribute the searches to the other indexers, gain intelligence, and perform reporting.
  • Deployment Server (DS): It helps deploy configurations and share data between components.
  • License Master (LM): It controls one or more license slaves and regularly checks the details of the license, which is based on volume and usage. In short, it defines stacks, pools, adds licensing capacity, and manages license slaves.

How Splunk Works: Different Stages in Data Pipeline

There are three different stages in Splunk Data Pipeline, each of which is concerned with different aspects of the data. These three stages are:

  • Data Input Stage: The first stage of the pipeline involves ingesting all the raw data stream from the source and breaking it into 64k blocks. Moreover, it adds the metadata, including:
    • Hostname
    • Source
    • Character Encoding
    • Source Type & Data
  • Data Storage Stage: This stage of the pipeline is divided into stage stages:
  • Parsing: Here, Splunk parses the log data and breaks it into lines to identify timestamps, create individual events, and annotate them with metadata keys. Then it transforms the even data with the help of transformation rules provided by the operator. This stage is also known as the event processing stage, as all the data sets are broken down into different events here.
  • Indexing: In this stage, the parsed events are written to the index queue to ensure the data is easily accessible to everyone at any time of the search.
  • Data Searching Stage: The last stage of the pipeline that controls how the data is accessed, used, and viewed by the users. Here user-defined knowledge objects are created and stored in the form of reports, event types, dashboards, and alerts.

Splunk Application:

From Big Data to day to day data assessment, organizations worldwide are applying Splunk to perform a variety of log and data-associated tasks. Therefore, here are two prominent applications of Splunk:

  • IT Operations: Helps detect network and system intrusion and breaches.
  • Web Analytics: Deployed to understand KPIs and improve performance.

Difference Between Splunk & ELK (Elastic, Logstash, Kibana):

Any discussion on Splunk is incomplete without its comparison with ELK Stack, another data and log management platform. Therefore, here is a side-by-side comparison of these two platforms, highlighting their prominent features and qualities.

Criteria Splunk ELK
Loading Data

Shipping data is easy, as it has forwarders that are pre-configured for a wide selection of data sources.

ELK relies on data processing pipelines like Logstash to ship data from the source to the destination, which can become tricky.


Uses C++-based proprietary for indexing.

Used Java-based Apache Lucene for indexing.


Performs MapReduce-based searching.

Performs Apache Lucene-based searching.


Uses Splunk Processing language.

Uses Query DSL.


It has a term license fee based on daily consumption.

It is open-source and free.


Its UI is equipped with flexible controls that enable users to edit and add new components to the dashboard.

Uses Kibana for the creation of visualizations such as line charts, area arts, tables, etc. in a dashboard.

Integrations & Plugins

Can integrate with around 600 plugins for IT operations, security, etc.

Does not support as many integrations as Splunk, which can integrate with around 160 plugins.

Splunk Best Practices:The process of data assessment with Splunk can further be enhanced by following a set of best practices, as they not only ensure the quality of the process but also allow teams to save efforts, time, and cost. Therefore, listed below are some of the critical Splunk best practices:

  • Test the index to ensure quick and thorough testing.
  • Configure timestamp, if your log format has a different timestamp.
  • Make sure Splunk accurately detects the event beginning and end.
  • Plan indexes and source types.
  • Keep search heads as close to indexers to improve the search heads’ speed in accessing the events.
  • Carefully plan the deployment of event collection to ensure success.
  • While deploying indexers, consider indexer clustering.

Splunk Advantages:

From supporting root cause analysis and providing enhanced GUI and real-time visibility to enabling the creation of a central repository for searching Splunk data from various sources, the benefits offered by Splunk are innumerable. A few of these are:

  • It is scalable and easy to implement.
  • Creates and shares analytical reports with interactive charts, graphs, and tables.
  • Automatically finds useful information.
  • Helps save your searches and tags to make the system smarter.
  • Troubleshoots any failure condition.
  • Helps analyze system performance and monitor business metrics.
  • Creates dashboards to visualize and analyze results.
  • Stores and retrieves data for later use.
  • Enables teams to incorporate Artificial Intelligence (AI) into their data strategy.
  • Offers real-time screen visibility and better interface.
  • Accelerates software development and testing.
  • It can work with any form of data, like a log file, .cvs, JSON, etc.

Disadvantages of Splunk:

Now that we understand the various benefits offered by Splunk, let us also have a look at some of its disadvantages to get a thorough understanding of its abilities.

  • Splunk is expensive for large data volumes.
  • Optimizing searches for speed is difficult.
  • Its dashboards, though useful, are not reliable.


As one of the leading distributed systems, Splunk is helping individuals and organizations worldwide aggregate, parse, and analyze data to take the necessary steps to improve their overall efficiency and business outcomes. Be it big data analysis or large volume data assessment, this advanced technology is witnessing a tremendous rise in its popularity, due to the numerous beneficial features it offers as well as its ability to turn raw data into insights.

Though today, Splunk is still in its infancy, the coming years will further witness its colossal growth across the spectrum and far beyond the IT industry.