Elastic Observability vs. Splunk Application Performance Monitoring (APM)

We can use this Elastic Observability in our business problems such as Creating internal/operational efficiencies issues, customer relations/service, and business process outcomes issues. This product has a lot of features for the above problems. But this product may be having some issues when charting purposes. But it can adjust for that purpose.

Incentivized

Good for below cases 1. There is a front end and need to correlate data with front end data 2. multiple microservices and need to check the health of each system 3. correlate data from various sources 4. Application performance is a key to be captured 5. application performance is a key metric.

Incentivized

• Open source code base
• Community support
• Is fast in processing

Incentivized

• Detect and Troubleshoot, which allows team to eliminate errors.
• Dashboard creates a one stop location for environment health
• Provides visibility, access to logs and user data real time.

Incentivized

• Difficult to setup/maintain
• Search pattern bar could be more user-friendly
• Premium subscription features are very expensive

Incentivized

• I wish Splunk Application Performance Monitoring could integrate with packet capture and analysis tools and provide the integrated analysis results on each tier of the application

Incentivized

Splunk is a great tool for log mining. It is less time-consuming and easy to use. It provides high security, it has customisable dashboards. Furthermore, it acts as a search head, and it gives real-time status. It has the ability to collect data from any source and multiple sources, and it can correlate them.

Incentivized

Splunk is a very good product but the licensing costs are high; we utilise the best of both worlds by using both products for slightly different purposes. We put the voluminous data with simple use cases in Elastic where it doesn't cost too much and can be searched quickly while putting the less voluminous data with more complex use cases in Splunk so we can take advantage of Splunk's very comprehensive but often much slower SPL search query language

Incentivized

It is good that the correlation analysis and the cause of the failure can be analyzed more extensively through the collection of many data. It is good because it provides a user interface (usability) well and can be used conveniently.
It is good because it can utilize anomaly detection through various AI-based ML models.

Incentivized

• Cost management.
• Good customer increment.
• Time management.

Incentivized

• It gave us more quicker alerts in a organize way to distribute to specific audience.
• Helped us to improve our uptime by getting pro-active alerts via emails
• More collaboration in a DevOps culture

Incentivized