ELK Stack vs Splunk

Difference Between ELK Stack vs Splunk

Most of the Business transactions today take place in Online Digital mode. Any glitch in such online applications will force the customers to shift the loyalty to the competitor and the business will effectively lose the revenue. It’s paramount that application should be available to the users 24 x7 at the expected performance level at the same it should be compliant to the regulatory conditions and security.

The organization suffers a huge loss if there is an issue in performance even momentarily or in regulatory compliance or insecurity. Monitoring the performance of the modern application has become a tough task due to its complex landscape dealing with microservices, Kubernetes, containers, and cloud. It’s impossible to monitor the applications manually with the internal team. IT team needs a tool that will pull data or log from applications/surrounding infrastructure and analyze the data for monitoring and resolves issues.

The log management tool should be capable of handling huge data from the monitored application and hardware environment and monitor the performance of applications. ELK stack and Splunk are the leading tool in this category and let’s analyze their features in detail in this article.

Head to Head Comparisons Between ELK Stack vs Splunk (Infographics)

Below are the top comparisons between ELK Stack and Splunk:

Different Tools

Below are the different tools for ELK Stack vs Splunk

Elk Stack

Elk denotes three products of a company by name Elastic. The three open-sourced products are

Elasticsearch, Logstash, Kibana. With the addition of the fourth product Beats one year ago, this tool is renamed as Elastic stack. A brief about all these 4 products

• Elasticsearch: The most important player in Elk stack is Elasticsearch and due to its critical functionalities the stack itself is named after it. An open-sourced product built in Java is a contemporary search/analytics engine built on Apache Lucene under the NoSQL category. Logically partitioned data is stored as Index and documents. This tool provides efficient storage and index layer.
• Logstash: This open sourced tool processes large log data collected from multiple sources. It is known for aggregation of data and analyzing it. This layer creates a solid data structure using the input data and storing it in NoSQL database in Elastic search. A strong structure enables easy search.
• Kibana: It is an analysis and visualization arm of Elk stack. A browser bases open sourced tool helps analysis of the data stored in index in Elasticsearch layer and present the results in a rich graphical mode. This layer enables users to query large volume of data.
• Beats: This tool focusses on log collection from various sources. This open sourced, light weight log shippers pose as agents in different servers to pump the data either into Elasticsearch indexes or Logstash for additional processing before storing.

Splunk

Headquartered in San Francisco, Splunk deals with Big-data analytics. This tool, using the data captured, generates informative dashboard, report, graph and other GUI visuals. These GUI facilitates monitoring of applications and alerts abnormalities to the users. IT department and End users make use of the results generated by Splunk.

This tool handles regulatory compliance as well as security aspects. The information presented by this tool helps user to do root cause analysis using AI techniques and resolve the issues at the earliest. It handles all the data types and helps users to build operational intelligence from the data collected.

It has 3 product ranges in its stable viz.,

• Splunk Enterprise: Used in big enterprises
• Splunk Light: Has Limited features and it is a free
• Splunk Cloud: Offered as Hosted services

Key Differences Between ELK Stack vs Splunk

Let us discuss some of the major key differences between ELK Stack and Splunk:

• Approach: Splunk extracts a pile of data dynamically and enable users to analyze the data at ease in the way they want. There is no pre planning required for extraction of data and analyzing it. Splunk offers a great flexibility to users. ELK requires a through pre planning of the information needs and data model to be designed well before the activity starts. Once the design is complete the extraction and viewing are much simpler.
• Technology: Splunk is one single product with three components a. Data forwarder, b. Indexer, c. Search (Front end). ELK has 3 different open sourced products Elastic search, Logstash and Kibana. Logstash extracts, processes and stores the data in NoSQL database in Elastic Search. Kibana provides a solid front end. Both Splunk and ELK makes use of a single agent to gather log data from source servers. They store the extracted data in indexes. Splunk uses propriety indexes and ELK uses Apache Lucene. Splunk uses search head for search operation and ELK uses Kibana.
• GUI- Visualization: Splunk offers flexibility to add new components to the dashboard on the fly. It provides good control to administrators to authorize permission to users to access data. Hence users can customize their dashboard easily by themselves. Kibana provides visualization effects in ELK stack and it offers impressive charts, graphs and dashboards. But it does not offer user management functions like Splunk.
• Search Functionalities: Splunk uses Search programming language (SPL) for search operation. Kibana uses Lucene query syntax. Splunk offers dynamic search facility in which user can query data using any data field. Such facility is not available in ELK and only the pre-designed fields can be used for query purposes.
• Cost: Splunk license is priced as per the volume of log data handled on a daily basis. The license is either perpetual or renewed yearly. ELK licenses are open sourced and it is free of cost.
• Usage: Features in dashboard of Splunk are easy to handle than ELK and configurations of Splunk are better refined than that of ELK.
• Training; Availability of trained manpower in ELK is plenty and Users of Splunk will have to spend a substantial amount in training

Comparison Table of ELK Stack vs Splunk

Let’s look at the top comparisons between ELK Stack vs Splunk.

Sr.No	ELK Stack	Splunk
1	Combination of 3 different products for extracting, storing and analyzing log data	It is a single product having three different components to manage monitoring operations
2	Data Model and Index design need to be finalized upfront. Any data extraction should be configured in Logstash well before. Each field should be defined in the configuration	Any data can be extracted from the source dynamically and it can be analyzed.
3	Data Fields in the visualization are pre fixed and cannot be changed on the fly.	New data fields can be added flexibly on the fly during run time.
4	Search operations criterion is pre fixed	Search can be done using any data field.
5	Has large community base and plenty of support is available.	Has good documentation and knowledgeable form for support
6	User management functions are not that strong	Admin has good control in providing field level authorization to users. Users can configure their own customized dashboard.
7	Fully open sourced and Free of cost	Licenses are priced as per the daily usage. Charged on Yearly or Perpetual basis
8	Certain features in ELK are challenging to configure	Configurations are easy to handle
9	Has fewer features and benefits	Has more features and beneifts.
10	No major investment on training as the trained manpower are available in plenty.	Users need to invest good amount on training.

 Conclusion

Selection of the platform depends on the user needs and the ability to manage budgets. Splunk offers flexible, feature rich and user friendly platform but it comes at a heavy cost.

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