The document discusses troubleshooting Oracle RAC in the private cloud. It provides an overview of Oracle Grid Infrastructure including the architectural components and processes. It then discusses common troubleshooting scenarios for cluster startup problems and provides a diagnostic flowchart. It also describes some of the key Grid Infrastructure processes like the cssd agent and monitor.
Introducing New AI Ops Innovations in Oracle 19c Autonomous Health Framework ...Sandesh Rao
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 18c and coming in 19c. First successfully introduced in Cluster Health Advisor, and extended to Trace File Analyzer and Hang Manager, Oracle AHF’s applied machine learning technology now enhances additional framework components. You will learn how to utilize these features for determining 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.
AUSOUG - Applied Machine Learning for Database Autonomous HealthSandesh Rao
This session will focus on how Oracle is applying various Machine Learning technologies to the area of diagnostics to prevent performance issues and maintain availability. Details about the architectures and algorithms used for this Autonomous Health initiative will be covered for both Cloud and On-premise database deployments. Components and functionality covered will be Cluster Heath Advisor, Trace File Analyzer (Collector, Receiver and Web), Hang Manager, and Adaptive Bug Search
NZOUG - GroundBreakers-2018 -Using Oracle Autonomous Health Framework to Pres...Sandesh Rao
This session will focus on the best practice use of the Oracle Autonomous Health Framework (AHF) with an emphasis on consolidated or private cloud database deployments. It will utilize a workload test driver and schemas that can be used to validate the prognostic and performance management functionality in Oracle AHF. Additionally, use cases focusing on best practices for runtime performance management, targeted diagnosis and rapid recovery to preserve availability will be covered.
NZOUG-GroundBreakers-2018 - Troubleshooting and Diagnosing 18c RACSandesh Rao
Learn about new diagnostic features in the 18c database product, tools on how to read trace and log files, automatically troubleshoot hangs, perform best practices on your stack automatically and how to act on the recommendations. We will cover RAC, ASM basics, the newest features of the diagnostic tools like Trace File Analyzer Collector, orachk, exachk, OSWatcher, Procwatcher, Hang Manager, Cluster Health Monitor and Cluster Health Analyzer. You can use Trace File Analyzer Collector to do all your first failure diagnostic collections and reduce the amount of back and forth with Oracle Support due to 90% of all the files Support needs being included by default. We will also cover analyzing logfiles using Machine Learning
AUSOUG - NZOUG-GroundBreakers-Jun 2019 - AI and Machine LearningSandesh Rao
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
The Machine Learning behind the Autonomous Database- EMEA Tour Oct 2019 Sandesh Rao
Autonomous Database is one of the hottest Oracle products where we have attempted to use Machine Learning for several aspects of the service. We take a view on our current state of ML in the Autonomous Database Cloud and how do we process this data in ADW/ATP with zeppelin notebooks to find anomalies in them to troubleshoot them at a scale of several petabytes a year and conduct AIOps. We will cover some sample notebooks to some 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. Some of the other use cases is 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 presentation will accompany several examples with how to apply these techniques , machine learning knowledge is preferred but not a prerequisite
Introducing New AI Ops Innovations in Oracle 19c Autonomous Health Framework ...Sandesh Rao
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 18c and coming in 19c. First successfully introduced in Cluster Health Advisor, and extended to Trace File Analyzer and Hang Manager, Oracle AHF’s applied machine learning technology now enhances additional framework components. You will learn how to utilize these features for determining 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.
AUSOUG - Applied Machine Learning for Database Autonomous HealthSandesh Rao
This session will focus on how Oracle is applying various Machine Learning technologies to the area of diagnostics to prevent performance issues and maintain availability. Details about the architectures and algorithms used for this Autonomous Health initiative will be covered for both Cloud and On-premise database deployments. Components and functionality covered will be Cluster Heath Advisor, Trace File Analyzer (Collector, Receiver and Web), Hang Manager, and Adaptive Bug Search
NZOUG - GroundBreakers-2018 -Using Oracle Autonomous Health Framework to Pres...Sandesh Rao
This session will focus on the best practice use of the Oracle Autonomous Health Framework (AHF) with an emphasis on consolidated or private cloud database deployments. It will utilize a workload test driver and schemas that can be used to validate the prognostic and performance management functionality in Oracle AHF. Additionally, use cases focusing on best practices for runtime performance management, targeted diagnosis and rapid recovery to preserve availability will be covered.
NZOUG-GroundBreakers-2018 - Troubleshooting and Diagnosing 18c RACSandesh Rao
Learn about new diagnostic features in the 18c database product, tools on how to read trace and log files, automatically troubleshoot hangs, perform best practices on your stack automatically and how to act on the recommendations. We will cover RAC, ASM basics, the newest features of the diagnostic tools like Trace File Analyzer Collector, orachk, exachk, OSWatcher, Procwatcher, Hang Manager, Cluster Health Monitor and Cluster Health Analyzer. You can use Trace File Analyzer Collector to do all your first failure diagnostic collections and reduce the amount of back and forth with Oracle Support due to 90% of all the files Support needs being included by default. We will also cover analyzing logfiles using Machine Learning
AUSOUG - NZOUG-GroundBreakers-Jun 2019 - AI and Machine LearningSandesh Rao
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
The Machine Learning behind the Autonomous Database- EMEA Tour Oct 2019 Sandesh Rao
Autonomous Database is one of the hottest Oracle products where we have attempted to use Machine Learning for several aspects of the service. We take a view on our current state of ML in the Autonomous Database Cloud and how do we process this data in ADW/ATP with zeppelin notebooks to find anomalies in them to troubleshoot them at a scale of several petabytes a year and conduct AIOps. We will cover some sample notebooks to some 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. Some of the other use cases is 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 presentation will accompany several examples with how to apply these techniques , machine learning knowledge is preferred but not a prerequisite
Introduction to Machine Learning and Data Science using Autonomous Database ...Sandesh Rao
This session will focus on basics of what Machine Learning is , different types of Machine Learning and Neural Networks , supervised and unsupervised machine learning , autoML for training models and this ends with an example of how to predict workloads using Average Active sessions and different algorithms as an example and also how to predict maintenance windows for your databases. We will also use different open source frameworks as well as some of the tools in the Autonomous Database cloud to do this. If you are a DBA and want to learn something about machine learning and use the tools to perform your tasks more efficiently and automaticall
AUSOUG - Introducing New AI Ops Innovations in Oracle 19c Autonomous Health F...Sandesh Rao
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 18c and coming in 19c. First successfully introduced in Cluster Health Advisor, and extended to Trace File Analyzer and Hang Manager, Oracle AHF’s applied machine learning technology now enhances additional framework components. You will learn how to utilize these features for determining 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.
AIOUG-GroundBreakers-2018 -Using Oracle Autonomous Health Framework to Preser...Sandesh Rao
This session will focus on the best practice use of the Oracle Autonomous Health Framework (AHF) with an emphasis on consolidated or private cloud database deployments. It will utilize a workload test driver and schemas that can be used to validate the prognostic and performance management functionality in Oracle AHF. Additionally, use cases focusing on best practices for runtime performance management, targeted diagnosis and rapid recovery to preserve availability will be covered.
Introduction to AutoML and Data Science using the Oracle Autonomous Database ...Sandesh Rao
We are entering a new era in the database with the introduction of the Oracle Autonomous Database. AI and Machine Learning are center stage to most projects and assist in making complex decisions which was not possible before. Most data science projects don’t get beyond the data scientist and rarely operationalize their predictive models. there are new toolsets and methods available everyday which make this an extremely dynamic space. There are different categories of users who want to use the algorithms , the toolsets but don't know where to start. Whether you are a data scientist who wants to play with data and build your own models or make use of the database features with the built in models or use the specific AI services within a specific vertical such as Insurance or Healthcare . We will take a glimpse at Oracle's Machine Learning Zeppelin-based notebooks for Oracle Autonomous Data Warehouse Cloud to how Oracle uses AIOps and Applied Machine learning for its own operations and the Oracle AI Platform Cloud Service to provided an all rounded view of what Oracle is upto in this space
LAD -GroundBreakers-Jul 2019 - The Machine Learning behind the Autonomous Dat...Sandesh Rao
We are entering a new era in the database with the introduction of the Oracle Autonomous Database. AI and Machine Learning are center stage to most projects and assist in making complex decisions which was not possible before. Most data science projects don’t get beyond the data scientist and rarely operationalize their predictive models. there are new toolsets and methods available everyday which make this an extremely dynamic space. There are different categories of users who want to use the algorithms , the toolsets but don't know where to start. Whether you are a data scientist who wants to play with data and build your own models or make use of the database features with the built in models or use the specific AI services within a specific vertical such as Insurance or Healthcare . We will take a glimpse at Oracle's Machine Learning Zeppelin-based notebooks for Oracle Autonomous Data Warehouse Cloud to how Oracle uses AIOps and Applied Machine learning for its own operations and the Oracle AI Platform Cloud Service to provided an all rounded view of what Oracle is upto in this space
Introduction to Machine Learning - From DBA's to Data Scientists - OGBEMEASandesh Rao
This session will focus on basics of what Machine Learning is , different types of Machine Learning and Neural Networks , supervised and unsupervised machine learning with examples, AutoML for training models and this ends with an example of how to predict fraud , to determining shopping patterns to Wine picking and different algorithms as an example and also how to predict workload for your databases. We will also use OML in the Autonomous Database cloud to do this. If you are a DBA and want to learn something about machine learning and use the tools to perform your tasks more efficiently and automatically
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
Under the Hood of the Smartest Availability Features in Oracle's Autonomous D...Markus Michalewicz
This presentation discusses details of the smartest High Availability (HA) features in Oracle's Autonomous Databases. It also explains how those features are integrated in the various stages of the journey to the Autonomous Database. This presentation was first presented during Collaborate18 / #C18LV together with Maria Colgan (@SQLmaria).
Perth APAC Groundbreakers tour - 18c featuresConnor McDonald
A tour of the features that are now available in versions 12.2 and 18c of the Oracle Database, with a focus on the new release model and its implications for DBAs
Machine Learning in Autonomous Data WarehouseSandesh Rao
Machine Learning in Autonomous Data Warehouse: One can use Oracle Autonomous Data Warehouse for machine learning. There are several ways to do this. This presentation explores these different but related options for performing machine learning. Each of these options enables people with different backgrounds to engage with building machine learning solutions on their data. At the end of the session, you will know which option will work best for you
This is from the Bay area Cloud Computing event https://www.meetup.com/All-Things-Cloud-Computing-Bay-Area/events/271017950/
This is a comprehensive presentation for the Oracle Exachk tool which covers automation and how to cover best practices and what options are features are available with the same
Presentation from OIS@ASCRS 2016
Moderator:
Jim Mazzo, Executive Chairman & CEO – AcuFocus
Participants:
Mike Ball, CEO – Alcon
Tom Frinzi, President – AMO, SVP – Abbott Laboratories
William J. Link, PhD, Managing Director – Versant Ventures
Ashley McEvoy, Company Group Chairman – Johnson & Johnson Vision Care
William Meury, EVP, President Branded Pharma – Allergan
Ludwin Monz, PhD, President & CEO – Carl Zeiss Meditec AG
Calvin Roberts, MD, SVP & Chief Medical Officer – Bausch + Lomb
Video Presentation:
https://www.youtube.com/watch?v=_ZdUbVHATBQ&list=PL1dmdBNnPTZJBhQxPOp0vdNg3s3wtN2yw&index=3
Introduction to Machine Learning and Data Science using Autonomous Database ...Sandesh Rao
This session will focus on basics of what Machine Learning is , different types of Machine Learning and Neural Networks , supervised and unsupervised machine learning , autoML for training models and this ends with an example of how to predict workloads using Average Active sessions and different algorithms as an example and also how to predict maintenance windows for your databases. We will also use different open source frameworks as well as some of the tools in the Autonomous Database cloud to do this. If you are a DBA and want to learn something about machine learning and use the tools to perform your tasks more efficiently and automaticall
AUSOUG - Introducing New AI Ops Innovations in Oracle 19c Autonomous Health F...Sandesh Rao
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 18c and coming in 19c. First successfully introduced in Cluster Health Advisor, and extended to Trace File Analyzer and Hang Manager, Oracle AHF’s applied machine learning technology now enhances additional framework components. You will learn how to utilize these features for determining 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.
AIOUG-GroundBreakers-2018 -Using Oracle Autonomous Health Framework to Preser...Sandesh Rao
This session will focus on the best practice use of the Oracle Autonomous Health Framework (AHF) with an emphasis on consolidated or private cloud database deployments. It will utilize a workload test driver and schemas that can be used to validate the prognostic and performance management functionality in Oracle AHF. Additionally, use cases focusing on best practices for runtime performance management, targeted diagnosis and rapid recovery to preserve availability will be covered.
Introduction to AutoML and Data Science using the Oracle Autonomous Database ...Sandesh Rao
We are entering a new era in the database with the introduction of the Oracle Autonomous Database. AI and Machine Learning are center stage to most projects and assist in making complex decisions which was not possible before. Most data science projects don’t get beyond the data scientist and rarely operationalize their predictive models. there are new toolsets and methods available everyday which make this an extremely dynamic space. There are different categories of users who want to use the algorithms , the toolsets but don't know where to start. Whether you are a data scientist who wants to play with data and build your own models or make use of the database features with the built in models or use the specific AI services within a specific vertical such as Insurance or Healthcare . We will take a glimpse at Oracle's Machine Learning Zeppelin-based notebooks for Oracle Autonomous Data Warehouse Cloud to how Oracle uses AIOps and Applied Machine learning for its own operations and the Oracle AI Platform Cloud Service to provided an all rounded view of what Oracle is upto in this space
LAD -GroundBreakers-Jul 2019 - The Machine Learning behind the Autonomous Dat...Sandesh Rao
We are entering a new era in the database with the introduction of the Oracle Autonomous Database. AI and Machine Learning are center stage to most projects and assist in making complex decisions which was not possible before. Most data science projects don’t get beyond the data scientist and rarely operationalize their predictive models. there are new toolsets and methods available everyday which make this an extremely dynamic space. There are different categories of users who want to use the algorithms , the toolsets but don't know where to start. Whether you are a data scientist who wants to play with data and build your own models or make use of the database features with the built in models or use the specific AI services within a specific vertical such as Insurance or Healthcare . We will take a glimpse at Oracle's Machine Learning Zeppelin-based notebooks for Oracle Autonomous Data Warehouse Cloud to how Oracle uses AIOps and Applied Machine learning for its own operations and the Oracle AI Platform Cloud Service to provided an all rounded view of what Oracle is upto in this space
Introduction to Machine Learning - From DBA's to Data Scientists - OGBEMEASandesh Rao
This session will focus on basics of what Machine Learning is , different types of Machine Learning and Neural Networks , supervised and unsupervised machine learning with examples, AutoML for training models and this ends with an example of how to predict fraud , to determining shopping patterns to Wine picking and different algorithms as an example and also how to predict workload for your databases. We will also use OML in the Autonomous Database cloud to do this. If you are a DBA and want to learn something about machine learning and use the tools to perform your tasks more efficiently and automatically
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
Under the Hood of the Smartest Availability Features in Oracle's Autonomous D...Markus Michalewicz
This presentation discusses details of the smartest High Availability (HA) features in Oracle's Autonomous Databases. It also explains how those features are integrated in the various stages of the journey to the Autonomous Database. This presentation was first presented during Collaborate18 / #C18LV together with Maria Colgan (@SQLmaria).
Perth APAC Groundbreakers tour - 18c featuresConnor McDonald
A tour of the features that are now available in versions 12.2 and 18c of the Oracle Database, with a focus on the new release model and its implications for DBAs
Machine Learning in Autonomous Data WarehouseSandesh Rao
Machine Learning in Autonomous Data Warehouse: One can use Oracle Autonomous Data Warehouse for machine learning. There are several ways to do this. This presentation explores these different but related options for performing machine learning. Each of these options enables people with different backgrounds to engage with building machine learning solutions on their data. At the end of the session, you will know which option will work best for you
This is from the Bay area Cloud Computing event https://www.meetup.com/All-Things-Cloud-Computing-Bay-Area/events/271017950/
This is a comprehensive presentation for the Oracle Exachk tool which covers automation and how to cover best practices and what options are features are available with the same
Presentation from OIS@ASCRS 2016
Moderator:
Jim Mazzo, Executive Chairman & CEO – AcuFocus
Participants:
Mike Ball, CEO – Alcon
Tom Frinzi, President – AMO, SVP – Abbott Laboratories
William J. Link, PhD, Managing Director – Versant Ventures
Ashley McEvoy, Company Group Chairman – Johnson & Johnson Vision Care
William Meury, EVP, President Branded Pharma – Allergan
Ludwin Monz, PhD, President & CEO – Carl Zeiss Meditec AG
Calvin Roberts, MD, SVP & Chief Medical Officer – Bausch + Lomb
Video Presentation:
https://www.youtube.com/watch?v=_ZdUbVHATBQ&list=PL1dmdBNnPTZJBhQxPOp0vdNg3s3wtN2yw&index=3
ICS Cybersecurity: How to Protect the Proprietary Cyber Assets That Hackers C...EnergySec
Presenter: David Zahn, PAS
Industrial control systems represent the brass ring for hackers who want to disrupt plant operations and negatively impact safety and productivity. The problem for cybersecurity professionals is that plants have highly vulnerable proprietary control systems where configuration data is not visible via standard WMI or SNMP calls. Yet, it is this same configuration data, such as I/O cards, firmware, installed software, and more, that hackers work hard to attain as it aids them in gaining control over industrial systems within plants.
As the saying goes, “you can’t manage what you can’t measure.” Taking inventory of this hidden configuration data and doing so for all control assets is difficult. Plants as a result fall short of achieving centralized, automated inventory – a cybersecurity best practice and a necessary precursor to effective change management. So how do you address change management when important security data is kept locked within each vendor’s distributed control systems, programmable logic controllers, and remote terminal units?
In this session, we’ll explore the types of inventory data that comprise a best practices cyber security plan. Next, we will dive into cost effective, accurate automation opportunities for inventory discovery and maintenance of heterogeneous proprietary and non-proprietary control assets. Finally, we’ll present a case study for implementing best practices for hardening ICS cyber security and automating management of change.
Agenda:
Building and Maintaining an Accurate ICS Inventory
Best Practices in Inventory Automation
Case Study
Tripwire IP360 Vulnerability Management Scanning Best PracticesTripwire
This presentation covers the various factors that influence scan accuracy and how tools within Tripwire IP360 can be leveraged to ensure optimal accuracy is achieved, providing a highly detailed scan report for all hosts within your environment.
Lessons Learned For NERC CIPv5 Compliance & Configuration Change ManagementEnergySec
The NERC CIPv5 deadline is fast approaching, and it’s not too late to be prepared. Join Mark Prince, Manager Operational Technology Fossil, from Entergy, Karl Perman, VP Member Services from EnergySec and Tim Erlin, Director from Tripwire to discuss achieving and maintaining NERC CIPv5 compliance in a fossil generation plant. We’ll cover some of the challenges that Entergy has experienced in their NERC CIPv5 compliance journey. Specifically, we will discuss configuration change management and how to leverage technologies for these requirements and consider what life would be without them.
Industrial Control System Cyber Security and the Employment of Industrial Fir...Schneider Electric
This presentation provides an overview of industrial control systems and typical system topologies, identifies typical threats and vulnerabilities to these systems, and provides recommended security countermeasures to mitigate the risks.
Many organizations are exerting top-down pressure to examine cloud and as-a-service models in general. To the IT managers and administrators in the data center, losing control of your data and/or applications can be a scary thing. There is also a complex web of fiscal and technical items that must be considered.
In this presentation, we will help you build a base understanding of the three core as-a-service models. We will then go on to discuss what we see working with our customers in the real world; these are opportunities that can offload some of the drudgery in your data center, while at the same time demonstrating to your organization that you are embracing the cloud. This presentation provides an in depth discussion surround the pros and cons of moving applications, and or infrastructure over to cloud and managed services.
In this presentation we present EAGLE's ideas on designing a modern disaster recovery environment. Key concepts include balancing cost, risk and complexity in DR strategies. Most notably we'll cover recovery objectives, common DR technologies (that allow you to backup and pre-position data), and the importance of viewing DR as an insurance policy.
Trivadis TechEvent 2017 Leveraging the Oracle Cloud by Kris Bhanushali tech_e...Trivadis
Database and application performance testing often requires complete copies of database and application stack which can be tedious to produce. In this session you will learn how to leverage the Oracle cloud to quickly provision full clones of database and application to create a test environment for performance testing and debugging. I will also touch upon the subject of continuous integration, a hot topic among developers, from a database point of view and discuss ways to implement CI for the database using Oracle cloud services.
Oracle Real Application Clusters (RAC) Roadmap for New Features describes and discusses best practices for new features introduced with Oracle RAC 12c as well as Oracle RAC 18c and provides a short outlook of the road ahead.
Whats new in Autonomous Database in 2022Sandesh Rao
This session covers the new features and happenings in the autonomous database world and will help answer more questions DBAs and Developers will have on the Autonomous Database, from provisioning to backups, troubleshooting, tips and tricks, security and HA. This is a good introduction for on-prem DBAs who want to learn how this works quickly without spending too much time on it. Questions like what does the free tier cover, how do I do backup or if it's automated, how do I manage it, how to scale up and down, how to secure their environment, how to use mtls, how to use tools like SQLDeveloper and SQLModeler, performance tuning all in a quick 45-minute session which might take weeks to pick up reading documentation or spanning several presentations
Oracle Database performance tuning using oratopSandesh Rao
Oratop is a text-based user interface tool for monitoring basic database operations in real-time. This presentation will go into depth on how to use the tool and some example scenarios. It can be used for both RAC and single-instance databases and in combination with top to get a more holistic view of system performance and identify any bottlenecks.
Analysis of Database Issues using AHF and Machine Learning v2 - AOUG2022Sandesh Rao
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. 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.
Session Highlights:
• Insights into AHF enhancements to current functionality and new features in 21c
• Learn early detection of anomalies and performance issues, their root causes and corrective actions
• Targeted postmortem analysis enabling quick resolution
Analysis of Database Issues using AHF and Machine Learning v2 - SOUGSandesh Rao
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. 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.
Session Highlights:
• Insights into AHF enhancements to current functionality and new features in 21c
• Learn early detection of anomalies and performance issues, their root causes and corrective actions
• Targeted postmortem analysis enabling quick resolution
15 Troubleshooting Tips and Tricks for database 21c - OGBEMEA KSAOUGSandesh Rao
This session will focus on 15 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 feel like a rockstar DBA
Introduction to Machine learning - DBA's to data scientists - Oct 2020 - OGBEmeaSandesh Rao
This session will focus on basics of what Machine Learning is , different types of Machine Learning and Neural Networks , supervised and unsupervised machine learning with examples, AutoML for training models and this ends with an example of how to predict fraud , to determining shopping patterns to Wine picking and different algorithms as an example and also how to predict workload for your databases. We will also use OML in the Autonomous Database cloud to do this. If you are a DBA and want to learn something about machine learning and use the tools to perform your tasks more efficiently and automatically
How to use Exachk effectively to manage Exadata environments OGBEmeaSandesh Rao
Exachk is a tool for helping with best practices with an Exadata Box. This presentation will go through setup , usage , options and how to use it more effectively to be more proactive in fixing issues with an Exadata environment. There are features like baselines , scheduler for ongoing automation , Collection Manager an Apex based interface which is used to determine the common problems , how to setup this dashboard all for free and in under 30 minutes to be a rockstar Exadata DBA
Troubleshooting tips and tricks for Oracle Database Oct 2020Sandesh Rao
This talk presents 15 different tips and tricks using tools to better troubleshoot and debug problems with Database , Oracle RAC and Oracle Clusterware , ASM and how to get the right pieces of data with the least of commands which today most people do manually. This session will cover 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 feel like a rockstar DBA.
20 tips and tricks with the Autonomous DatabaseSandesh Rao
This covers the top 20 questions most DBA’s , Developers will have on the Autonomous Database from provisioning to backups , troubleshooting , tips and tricks , security and HA . This is a good introduction for on-prem DBA’s who want to learn how this works quickly without spending too much time on it . Questions like what does the free tier cover , how do I do backup or if its automated how do I manage it , how to scale up and down , how to use tools like SQLDeveloper and SQLModeler , endpoints , terraform all in a quick 45 minute session which might take weeks to pickup reading documentation or spanning several presentations
Oracle Autonomous Health Service- For Protecting Your On-Premise Databases- F...Sandesh Rao
Oracle Autonomous Health Service is a new feature in Oracle Database 19c that supports centralized diagnostic monitoring of your on-premise Oracle RAC database clusters. In this session learn how to free up local cluster resources and improve monitoring efficiency with this centralized service-based solution. Practical tips and best practices for setup are covered along with a demonstration of its responsive browser-based dashboard and triage console. Specific Oracle RAC database, Oracle Automatic Storage Management, and cluster issues are rapidly triaged utilizing the built-in support for Trace File Analyzer and ORAchk/EXAchk collections, Cluster Health Advisor problem diagnosis, Hang Manager and Memory Guard events, and QoS management workload performance metrics.
The Machine Learning behind the Autonomous Database ILOUG Feb 2020 Sandesh Rao
Autonomous Database is one of the hottest Oracle products where we have attempted to use Machine Learning for several aspects of the service. We take a view on our current state of ML in the Autonomous Database Cloud and how do we process this data in ADW/ATP with zeppelin notebooks to find anomalies in them to troubleshoot them at a scale of several petabytes a year and conduct AIOps. We will cover some sample notebooks to some 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. Some of the other use cases is to use convolution filters...
Troubleshooting Tips and Tricks for Database 19c ILOUG Feb 2020Sandesh Rao
This session will focus on 19 troubleshooting tips and tricks for DBAs 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 feel like a rockstar DBA
Introduction to Machine Learning and Data Science using the Autonomous databa...Sandesh Rao
This session will focus on basics of what Machine Learning is, different types of Machine Learning and Neural Networks, supervised and unsupervised machine learning, AutoML for training models and this ends with an example of how to predict workloads using Average Active sessions and different algorithms as an example and also how to predict maintenance windows for your databases. We will also use many examples from the ADW Oracle Autonomous Database offering, Oracle Machine Learning library to make this a session with lots of code examples in addition to the theory of Machine Learning and you will walk out having a definitive path to being a data scientist