Elasticsearch vs Splunk

Can't you decide which one is better - Elasticsearch or Splunk? No worries! You can find the answer in this blog. This blog will take you through the various aspects of the tools in-depth. This blog will help you to know where both the tools shine and where they lack. Let’s dive deep into the blog!

Data is the new fuel for businesses in the market. Every organization is constantly looking to churn valuable insights from the data. Splunk and Elasticsearch are the two prominent platforms for data analysis. We can use both tools to collect, process, and store large volumes of data. The tools have a rich set of functionalities to search, filter, and analyze data. You can visualize the data using the tools. Though the tools have many similar features, they have distinct differences. Let’s look at the differences in the following.

Table of Contents

What is ElasticSearch?

Elasticsearch is an analytics engine that you can use to design horizontal search.it is an open-source tool that you can use to make full-text search, real-time data analysis, and operational intelligence. Elasticsearch is essentially a NoSQL database that helps to store data in unstructured format. So, you cannot use SQL to query the data stored in elaticsearch. With elastic search’s aggression capabilities, you can make complex analytical tasks and extensive text analysis. Moreover, It offers REST API that simplifies system interaction.

If you want to enrich your career and become a professional in Splunk, then enroll in "Splunk Training". This course will help you to achieve excellence in this domain.

What is Splunk?

Splunk is a software designed for searching, monitoring, and even analyzing data generated from the machine. Splunk is primarily used for log management, real-time data analysis, and monitoring various information technology infrastructures & applications (on both iOS and Android). It helps users manage the applications and security and troubleshoot network and server problems. Splunk is a prevalent software used in various IT, healthcare, finance, and cyber security industries.

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Elasticsearch VS Splunk: The Key Differences

  • Pricing

Splunk: Splunk is a paid software that comes with many pricing options. You can choose your plan based on your data consumption needs. The pricing will be made based on the selected features and options.

Elasticsearch: If you plan to use Elasticsearch and Elastic stack tools widely, you must get the company's subscription. You will need to select the features and options that you require. Similarly, you can contact the sales agent for the quote per your requirements.

Elastic stack is cheaper as compared to Splunk. But the cost will depend on your tailored requirements and the usage.

  • Data collection

Splunk: Data collection in Splunk depends on the user's data sources. Splunk uses various methods of collecting data, which are as follows.

  • The ingest service 
  • The forwarder service
  • The DSP HTTP event collector
  • Steaming connectors  

Elasticsearch: In Elasticsearch, Rest calls are one of the methods of collecting the data and ingesting it into the tool. Beats serve as an alternative method for gathering the data. They are designed to sit on the user's device, which collects all the logs and forwards them to the Logtash( aggregator). 

  • Setup and Maintenance

Splunk: Setting up Splunk is easy. You can install Splunk by simply following the installation process. There is no complex step involved in the installation process. You can easily log in to the Splunk web interface once the installation is over. After that, you can start data ingestion and data analysis.

Elasticsearch: This tool can be run across many servers on a single cluster.

Therefore, it serves as a search and analytics engine that is distributed. To set up the software, you must install it on every cluster server and configure the servers to facilitate communication within them. 

Setting up Splunk is thus easier compared to Elasticsearch as it does not require much technical expertise. On the other hand, Elasticsearch demands knowledge of distributed systems.

  • Indexing

Splunk: It uses an Indexer for indexing the data arriving from the Splunk forwarder. The indexer splits log data into lines, recognizes timestamps to generate separate events, and then adds metadata to these events. Splunk keeps various copies of indexed data. Therefore, you need not worry about data loss. This is called data replication. 

Elasticsearch: Indexing in Elasticsearch refers to a collection of interconnected documents. The inverted Index is the data structure used by Elasticsearch, which facilitates users with full-text searches. Therefore, Indexing is achieved via the index API, allowing users to add or update a JSON Document within a specific index.

  • Storage

Splunk: Data in Splunk is organized and stored within indexes, which are composed of file buckets. With the help of Data structure in these buckets, Splunk determines whether the data contains words or terms. Buckets also hold compressed and unprocessed data. To store data efficiently, Splunk reduces the data size to 15% of the original size.

Elasticsearch: Elasticsearch stores data in the form of unstructured JSON documents. Every document is associated with a collection of keys with their respective values, such as strings, geolocation, Booleans, arrays of value, dates, and other data types. All the content of the stored documents is indexed.

  • User Interface

Elasticsearch and Splunk offer a user-friendly web interface allowing users to perform numerous tasks like analysis, visualization, and data ingestion. However, the UI of both tools is different in terms of looks and capability for what they can do. 

Splunk: The Splunk UI is mainly designed for searching and analyzing the data. It has a search bar at the top of the screen. This helps in entering queries related to searches and having the results in real time. The UI has numerous screens and pictures that help you see and understand your data without much effort.

Elasticsearch: The UI of Kibana in Elasticsearch's primary focus is the discovery of data and exploration. It also has numerous pre-built analysis and data visualization tools like Dashboard apps, Discover, and Visualize. These tools make it simple to quickly look at and study your data without creating complicated search questions.

  • Query Language

Splunk: Splunk utilizes its Search-processing language known as SPL. SPL is a specialized language designed to support the search pipeline and is exclusive to this purpose. 

Elasticsearch: The search language used in Kibana is built upon the Lucene Query syntax. The syntax is similar to the scripting language. Therefore, anyone who has previously worked on scripting language can easily work on Kibana.  

The primary difference is that SPL (Search Processing Language) lets you create a search flow where you connect multiple actions using a pipe symbol. This way, the results of one action can be fed into the next action. On the other hand, Lucene query syntax can help you directly produce results straight from the query.

[ Check out Elasticsearch Interview Questions and Answers ]

Frequently Asked Questions

1. What is the key difference between Elasticsearch and Splunk?

Scalability and real-time are the key aspects of Elasticsearch. On the other hand, Splunk offers a complete platform for analyzing logs, ensuring security, and gaining insights for business intelligence.

2. How many logs can Elasticsearch handle?

The limit is around 2 billion documents. However, it is suggested that you create smaller indexes primarily for better recovery management.

3. How is data stored in Elasticsearch?

Elasticsearch uses a data structure known as an inverted index, empowering fast full-text searches. It consists of all the distinct words that appear in all the documents.

4. Why are businesses switching from Splunk to Elastic?

Splunk users are switching to Elastic because of Pricing, slow searches, data handling, adaptability, etc.

5. Explain a few disadvantages of using Splunk.

Complex setup, slower searches, Outdated UI, the requirement of specialized training, and cost are a few of its drawbacks.


The choice between Elasticsearch and Splunk depends on the specific requirements of the organization and the resources available. If you want a user-friendly solution to handle various analysis work and log management tasks, then you should go for Splunk. 

It is easy to plug in Splunk, but it comes at a cost. If you want a tool that can be scaled and customized as per the organization's needs, then Elasticsearch will be a better choice. Elasticsearch is a tool that requires a lot of effort at the beginning for the entire setup and also resources for maintenance. 

If you want to learn more about Elasticsearch and Splunk, you can enroll for training in MindMajix. It will help you to get a thorough understanding of the tools and make a solid decision.

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Last updated: 19 Dec 2023
About Author

Soujanya is a Senior Writer at Mindmajix with tons of content creation experience in the areas of cloud computing, BI, Perl Scripting. She also creates content on Salesforce, Microstrategy, and Cobit. Connect with her via LinkedIn and Twitter.

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