Accelerate your ability to sniff out application exceptions and performance outliers using Splunk

Are you frustrated with how long a Splunk time-series query of your data can take when you need it now? Are you looking to use machine learning to quickly gain insights about your app’s performance? Finding application exceptions or detecting outliers in your performance KPIs too late can lead your business to suffer without the information it needs to make the right decisions in a timely manner. We will show you how we used the metrics index and machine learning capabilities in Splunk to make better alerts, build scheduled performance reports, and ultimately gain deeper insights and make better decisions based on our data. Sharing these insights as a weekly scheduled report helped our team find hidden issues, increase performance awareness, and maintain SLAs around performance KPIs. Additionally, better alerts operationally helped us to detect outliers in performance metrics within minutes after they occur. Join this session to see queries, demos and several examples for you to take back with you and implement this solution at your company.

From .conf19, session IT1171.

Special thanks and credit to:
- Eurus Kim, Staff ML Architect, Splunk
- PJ Pokhrel, Performance Engineer, StubHub
- Steve Veio, Performance OPS Manager, StubHub

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