Sandesh Rao

Sandesh Rao – Oracle


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).

Sandesh Rao is the keynote speaker at ConTech2022.

Besides the keynote talk, Sandesh is doing a second presentation about analyzing database issues with AHF and ML.

Under the hood of the Autonomous Database

In this session, we will cover details of the database components such as Exadata, Oracle RAC, and Data Guard that power the Oracle Autonomous Database. Database Administrators will learn about the architectural choices such as Oracle Linux configurations and Oracle RAC cache fusion internals that helps improve overall database performance.  So join us to learn and see how all of this works.
Analysis of Database Issues using AHF and Machine Learning

Oracle Autonomous Health Framework (AHF) is Oracle’s Artificial Intelligence Operations platform for autonomous database health management. This session will focus on enhancements to current functionality and new features in 21c until present time. We will discuss how to use the data which is derived from the Bayesian Net framework of AHF to conduct root cause analysis, telemetry and remediations for issues. You will learn to utilize these features to determine workload footprint, ongoing monitoring, early detection of anomalies and performance issues, their root causes and corrective actions, prevention of node or database failures, and targeted postmortem analysis enabling quick resolution.