Sandesh Rao is a VP running the AIOps Automation for the Autonomous Database Group at Oracle Corporation specializing using AI/ML for different usecases from predicting faults before they happen to Anomaly Detection within log data , metrics data. His previous positions have focused on performance tuning , high availability , disaster recovery and architecting cloud based solutions using the Oracle Stack. With more than 18 years of experience working in the HA space and having worked on several versions of Oracle with different application stacks he is a recognized expert in RAC , Database Internals , PaaS , SaaS and IaaS solutions and solving Big Data related problems . Most of his work involves working with customers in the implementation of public and hybrid cloud projects in the financial , retailing , scientific , insurance , biotech and the tech space. He is also responsible for developing assessments for best practices for the Oracle Grid Infrastructure 19c including products like RAC (Real Application Clusters) , Storage (ASM , ACFS) More details at http://bit.ly/1UCL46K twitter @sandeshr
A full day workshop going over the following topics:
Autonomous Database Cloud Diagnosability using Machine Learning
Autonomous Database is one of the hottest Oracle products where we have attempted to use Machine Learning for several aspects of the service. This presentation takes a view on our current state of Diagnostic methodology in the Autonomous Database Cloud services and how do we process this data to find anomalies in them to troubleshoot them at a scale of several petabytes a year and conduct AIOps. Some of the use cases we will cover are a Log Anomaly timeline which we reduce significant amounts of logs using semi-supervised machine learning techniques to reduce logs and match them in near real time. We will cover techniques to analyze database issues using Machine learning techniques like Kmeans , TFIDF, Random Forests, and z-scores to predict if a spike in the CPU is a normal or abnormal spike. We will also talk about RNN’s with LSTM/GRU as some of the applications of how to predict faults before they happen. Some of the other use cases are to use convolution filters to determine maintenance windows within the database workloads, determine best times to do database backups, security anomaly timelines and many others. This is a production service and this can be used if you have a customer SR/defect today. The service is much more extensive inside the Oracle Autonomous Database Cloud. This presentation will accompany several examples with how to apply these techniques, machine learning knowledge is preferred but not a prerequisite
AI/ML – 19 troubleshooting tips and tricks for Database 19c
This part of the workshop will focus on 19 troubleshooting tips and tricks for DBA’s covering tools from the Oracle Autonomous Health Framework (AHF) like Trace File Analyzer (TFA) to collect , organize and analyze log data , Exachk and orachk to perform mass best practices analysis and automation , Cluster Health Advisor to debug node evictions and calibrate the framework , OSWatcher and its analysis engine , oratop for pinpointing performance issues and many others to make one feels like a rockstar DBA
Troubleshooting and Diagnosing Oracle RAC 19c
Learn about new features in the 19c RAC database. In this session get a good understanding of the architecture of RAC , ASM and the Grid Infrastructure which involves processes, their communication mechanisms, startup sequences and then we move to scenarios and common troubleshooting scenarios with how to proceed to diagnose the same. We will learn to automatically troubleshoot hangs, collect and debug trace, perform best practices on your stack automatically and how to act on the recommendations.