Splunk vs Tableau

Differences Between Splunk vs Tableau

With the ever-increasing data flow in a market, it becomes essential for most corporations today to use the right business intelligence software. The definition of “right” is not related to choosing the one with a high number of features but the one which is capable of addressing all your crucial priorities. Business intelligence software is the type of application software that is designed in order to extract, analyze, transform the data as per need and report for business intelligence purposes. The data is generally previously-stored, though applications like Apache Spark are providing real-time data streaming capabilities, specifically using meta keywords in a data warehouse.

Let us understand the role of business intelligence with the help of an example.

Suppose you are required to predict the team that will win the ICC Cricket world cup this year, then you would need the performance statistics of all the players for the past 10 years and other stakeholder’s details. That data can be stored in a NoSQL database or an RDBMS. Then this data can be extracted, transformed and provided to Business intelligence tools using Hadoop software. Now, these tools can be used to predict the winning team by looking at the winning trend and right set of parameters such as the coach under whom the teams most likely won, etc.

Two such tools are Splunk and Tableau which can be used to give your business an additional edge over your competitors. Let’s find out in this Tableau vs Splunk post, by means of comparison, the right suited tool for your needs.

Head to Head Comparison Between Splunk and Tableau (Infographics)

Below is the top 12 comparison between Splunk and Tableau:

Key Differences Between Splunk and Tableau

The differences between Splunk and Tableau are explained in the points presented below:

1. Splunk is used to monitor all machine activities including logins and actions taken on those machines under each user whereas Tableau provides pattern-based visualizations under a huge pile of data, on a real-time basis.
2. Splunk is mostly compared with QRadar from IBM, Micro Focus ArcSight, LogRhythm whereas Tableau can be compared with Microsoft BI, Oracle OBIEE, SAS Visual Analytics.
3. Splunk helps organizations by reducing MTTR as all the developers and stakeholders have easy access to log events. It reduces costs by eliminating the needs for engaging developers of multiple teams for issues relating to multiple platforms. It also provides an improved security mechanism as many people do not have access to servers. Tableau, on the other hand, is intuitive, simple in creating insights and its drag and drop feature makes it extremely handy to use. It is also capable of handling quick calculations by providing various shortcuts. It provides a huge variety of data connector integration with different databases.
4. As far as initial setup and implementation are concerned, Splunk provides a relatively straightforward setup. An experienced implementation partner could be considered for this purpose. Implementing Tableau is much simpler and is just a matter of hours.
5. When we talk about pricing, cost and licensing, Splunk enterprise becomes extremely costly after 20GB/month license. So, if you manage and watch your logging (by not logging excessive event triggers), this number is a long way to go. Tableau is very useful for small-scale organizations as it is relatively cheaper but it becomes costly with the increase in server integrations.
6. There is definitely room for improvement for both Splunk and Tableau. Splunk certifications such as CBT are comparatively expensive, so not many people are able to certify themselves and therefore they are unable to optimally utilize the tool. Managing and filtering logs becomes a major task as easy data ingestion can result in exceeded bandwidth. A suggested easier way is to provide the functionality to flag the non-critical files so that they can be discarded on its own by the tool. When it comes to Tableau, it lacks machine learning and other cognitive data science technologies due to which implementation of new analytics languages such as R, SAS, Python, etc. is not possible. Also, techniques like decision trees, CHAID analysis, K-means cannot be implemented due to the lack of cognitive technologies. Tableau has a connector to R, so that can be used with minimal features.
7. Our advice for using Splunk is to let an experienced Splunk architect design the infrastructure configuration by collaborating completely with the senior technical team to understand product viability. Splunk configurations should be managed in GIT and team members should be educated as quickly as possible to use the tool efficiently. For tableau, not more than 2M data points should be visualized at an instant and extracted data should be used for high performance. Using just 3-4 sections in the report and doing calculations during the ETL phase will reap the maximum benefit from this tool.

Comparison Table Between Splunk and Tableau

Below are the lists of points, describe the comparisons between Splunk and tableau.

Conclusion

In this Splunk vs Tableau post, we did a detailed comparison between Splunk and Tableau. There are other tools in a market which can suit your need better. Go ahead, implement these tools to your business and write back to us about your thrilling experience with these tools.

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