Splunk vs ELK

Before going to Splunk vs. ELK, we will have a look at the central area in enterprise firms that is infrastructure.

Infrastructure has various kinds of challenges in different aspects like security, monitoring, and maintenance of a wide range of applications hosted in multiple servers or cloud environments. the thing is to manage them in a secured way means every action that hit to the server from the client or vice versa…here the core concept involved that is Log Management.

Log management is a concept for managing every action in detail that will capture every step in a comprehensive way.

Log management mostly handled by the operations team to check every action taken by the users and machines.sometimes, this will play a vital role in avoiding data breaches and verifying anonymous logins and examing the usual behavior of applications.

I work in Ops teams, and I used to manage several applications and its behavior hosted in the cloud and on-premises.

For Log management and analysis, we have different tools in the market in that the most widely used tools are Splunk and ELK.

Splunk is an enterprise solution for log management, and it analyses in a well-organized way. ELK is also an enterprise solution from the background of opensource from Elastic platform.

Generally, Splunk has its unique terminology, and people call as “Google for Logs,” and ELK is derived from three main components of elastic those are Elastic search, logstash, kibana

Here the question arises why it is called google for logs, Yes it uses a well organized to fetch and manage the records.

In-depth, Splunk is having two types of setup in infrastructure one is on-premises means that will be managed by the organization within its infrastructure and managed by its operations (Ops) team

another cloud setup means that it can be hosted in cloud platforms like AWS to manage the logs.

Splunk has three major working units, or we can call components to perform log analysis.those are forwarder, indexer, and search head.

The working nature of Splunk is unique in the background; it takes an enormous or small amount of data as input from the user. It will push to indexers who are in remote using its first component forwarder than the indexer will process the data, and it will send it to search head, here the search head will act as a graphical interface for fetched results.

As slunk, ELK is also having three components in its working architecture those are elastic search, log stash, kibana.

The concept behind ELK is, it takes the data as input from the user, and it will pass it to its components to perform the required fetch operations in that data processing will be taken care of by logstash. The results will be displayed using the kibana dashboard.

In-depth, Splunk uses its language to perform all these operations in a well-organized way i.e., Splunk Search Processing Language  (SPL).SPL will help users to explore data in a dynamic way that is not supported by ELK.

ELK also uses its query syntax to fetch logs and generate a organizes report to the user.

In both, the common factor is search operation to generate a well-organized report.

Splunk uses a search pipeline to write queries naturally like grep in Linux and ELK uses  Lucene here we need to provide exact syntax for what you are expecting from the ELK

The main feature of Splunk is, it will accept data in various formats, and ELK has few limitations here.

Both the tools are having official documentation to go through, and they provided integrations support to add to more its working nature.

Splunk is having a wide-open source community when compare with ELK. The primary factor between these two is Pricing, yes, Splunk is a paid version, and it costs based on the indexing volume.

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