Updated June 19, 2023
Differences Between Splunk vs Elasticsearch
When discussing Log Management solutions, the first question that comes to mind is, Is Splunk better than Elasticsearch or vice-versa?
Log Management Solutions
Log management tools play a vital role in an enterprise’s layered security —without them, companies have very little visibility into the actions and events occurring inside their technology infrastructures that could either lead to data breaches or signify a security compromise in progress. Splunk and Elasticsearch are two of the leading enterprise competitors in this category. Which tool you use depends on what you are trying to capture and alert your people and whether you will fork some open-source applications on GitHub or buy some commercial software from vendors; let’s see how they stack up in this comparison.
Head to Head Comparison Between Splunk vs Elasticsearch (Infographics)
Below are the top 5 Differences Between Splunk and Elasticsearch:
Key Differences Between Splunk and Elasticsearch
Below are the lists of points that describe the key differences between Splunk and Elasticsearch:
The Basics
Splunk inherited the long-term solution of the BIG-DATA problem way before it existed in the market in 2003 when Michael Baum, Rob Das, and Erik Swan developed the resolution of capturing data logging and machine data logs to confirm the integrity of the systems at that time. On the other hand, Elasticsearch was founded by Shay Banon in 2010 when the BIG-DATA problem existed. People wanted some other logging and monitoring tools to test and compare Splunk’s performance, and then a company was founded around it called Elastic.
The biggest problem with Splunk is that it is an expensive and paid tool. Elasticsearch is a free and open-source solution on the other side of the river.
The Target Problem and Solution
Splunk has traditionally served big enterprise clients in the past, which are opting out for an on-premises solution because of the data integrity issues, and now they are trying to capture the smaller companies as their clients by making their services affordable. Elasticsearch, on the other hand, provides end-to-end open source and some premium services for all kinds of companies out there in this domain. With their on-cloud consumed and data Retention components, they are very famous among newbies and start-ups. They also tend to serve their clients with generic use case solutions.
If you are looking for dynamic greps and good visualizations, then Elasticsearch + Kibana is the one for you. If your use cases frequently change with every release and deployment, try out Splunk.
The Set-Ups
Splunk has distributed its services around two modules. Splunk Enterprise targets on-prem specializations, and Splunk Cloud tries to serve the cloud-based disciplines through the web app as both have comparatively similar features. The daily limits of usage are the things that are on the table for bargains with both services.On the other hand, Elasticsearch has only one SaaS platform, which has a paid module for deployment rest of the different modules are free and open-source, which is very helpful for small companies to monitor their enterprise logs without spending any money on their data.
The UI and Dashboards Visualizations
Nowadays, people are very much into the user interface and other visualization stuff, and eventually, people are expecting very much out of the black log screens. Eventually, Splunk improved its user interface with new dashboard controls, good and interactive graphical interfaces, and abilities to assign tasks and workflows to the team members concerning their departments. They have got the remarkable feature of exporting their dashboards to PDF.
On the other hand, Elasticsearch comes with no UI of its own, but the tasks can be inherited from Kibana, and it has very cool background themes that aren’t there in Splunk. So the dashboard personalization is better in Kibana.
Splunk vs Elasticsearch — The Data Migration and User Management
It depends on the type of data formats supported by both of these tools. Splunk and Elasticsearch come with a data shipper object that retains the path of the files and uploads. The mapping of the object about its other counterparts’ fields is done very differently for both of them as Splunk comes with inbuilt and pre-configured features, which allow it to map the data into entities with respective values. On the other hand, we need to define the mapping of every filed type to its importance in Elasticsearch.
Another difference with data migration comes when the data is parsed, as both of them have a way different approaches to the same. Elasticsearch has taken parsing for granted and is very much manual, but on the other hand, Splunk comes with pre-loaded wizards and features which are very easy and reliable to use.
Another handy feature of user Management comes pre-installed with Splunk as it helps the managers to manage their resources who are working with the particular project logs and their segmentation. On the other hand, Elasticsearch comes with no such features as they have pre-defined assignment logs.
Splunk vs Elasticsearch Comparison Table
Below are the differences between Splunk and Elasticsearch:
| Basis of Comparision | Splunk | Elasticsearch |
| Basics | The biggest problem with Splunk was and still is that it is an expensive and paid tool. | Elasticsearch is a free and open-source solution. |
| Target Problem and Solution | Splunk has traditionally served the big enterprise clients in past which are opting out for an on-premises solution because of the data integrity issues and now they are trying to capture the smaller companies as their client by making their services affordable. | Elasticsearch, on the other hand, tends to provide end to end open source and some premium services for all kinds of companies out there in this domain. |
| Set-Ups | Splunk has distributed their services around two modules Splunk Enterprise targets the on-prem specializations and Splunk Cloud tries to serve the cloud-based specializations through the web app as both has comparatively similar features. The daily limits of usage are the things which are on the table for bargains with both the services. | On the other hand, Elasticsearch has only one SaaS platform which has a paid module for deployment rest other modules are a free and open source which are really very helpful for small companies to monitor their enterprise logs without paying any money for their own data. |
| The UI and Dashboards Visualizations | Eventually, Splunk has improved their user interface with new dashboard controls, good and interacting graphical interfaces and abilities to assign the tasks and workflows to the team members with respect to their departments. They have got a really cool feature of exporting their dashboards to PDF. | On the other hand, Elasticsearch comes with no UI of its own but the tasks can be inherited to Kibana and it has really very cool background themes which aren’t there in Splunk. So the dashboard personalization is kind of better in Kibana. |
| Data Migration and User Management | As Splunk come with inbuilt and pre-configured features which allows it to map the data into entities with respective values.
Splunk comes with pre-loaded wizards and features which are very easy and reliable for use. |
Elasticsearch comes with no such pre-loaded wizards and features which are very easy and reliable for use. |
Conclusion
In short, both Splunk and Elasticsearch are competent, enterprise-grade log management and analysis platforms trusted by the world’s leading organizations.
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