Machine learning can be used for a variety of roles such as troubleshooting, capacity planning, and performance analysis.
Businesses can use machine learning to understand seasonality for trending and forecasting capabilities or view dashboards on data already collected.
In a recent DBTA webinar with Courtney Llamas, architect, strategic customer program systems management & security, and Timothy Mooney, director, Oracle Product Marketing, discussed taking a proactive, prioritized approach to SQL tuning by leveraging Oracle's advanced analytics to see which databases, and specifically, which SQL IDs, are degrading in performance over time.
Common Tasks in OEM for a DBA include alerting, tracking historical metrics, performance tuning a particular database or SQL, database management, and more, Llamas and Mooney said.
The types of data collected consist of metadata, metrics, events/availability, and configuration.
Which resource will become the bottle in the system based on current load growth from onboarded databases?
Which systems have available capacity headroom to support consolidation of additional databases?
Which SQLs are performing better or worse than expected?
Which of the poor SQLs regressed due to bad plans vs. excessive waits?
Is the application performance suffering as result of sub-optimal SQL?
Which databases are reporting the most problems and what type?
How do I correlate from application tier to the database to perform RCA?
Oracle Management Cloud gives users the ability to solve some of these problems and answer these questions, Llamas and Mooney said.