BMC AMI Strobe vs. Splunk Application Performance Monitoring (APM)

Compuware Strobe is very essential if you are working in a multi-system environment to constantly measure the activity of your applications. My setup involves deploying bots to perform repetitive tasks. With Compuware Strobe, I'm able to conduct early tests on codes that reduce by great margins performance issues on the development cycle; It's why I'm making a good recommendation for it.

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

• Runs detailed analysis on captured data to aid in tuning applications.
• It's possible to run early tests on the bots' codes to detect early performance issues.

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

• Does not collect all performance indicators(toolchain measurements) in early CI pipelines.

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

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

• It has helped us identify issues affecting our and our clients' applications' performance.
• Smoothened robots deployment.

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