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Duration 5 Days 30 CPD hours This course is intended for This intermediate-level course requires students have incoming experience working with Oracle Database 18 or higher. Overview Working in a hands-on learning environment led by our expert facilitator, you'll explore: The Oracle Database Architecture Query Optimizer Tuning Container Databases and Pluggable Databases Oracle 19c Tuning features Evaluating Execution Plans Oracle Tuning Tools Using Automatic Workload Repository Join Types AWR Using Baselines Additional AWR performance tools Optimizer Statistics Monitoring a Service Bind Variables and database parameters Oracle's Real Application Testing (RAT) SQL Tuning Advisor Automatic Sql Tuning Sql Plan Management Shared Pool Tuning Tuning the database buffer cache Tuning the PGA (Program Global Area) Automatic Memory Management (AMM) Tuning Segment Space Utilization (ASSM) Automatic Storage Management Oracle 19C Database Tuning is an intermediate level course for Oracle database experienced attendees that explores core tuning skills such as Database parameters, SQL Tuning Advisor, SQL Access Advisor, Adaptive SQL plans and more. Overview Oracle Database Architecture Instance Definition Define SGA Define Background Processes Datafile Definition Query Optimizer SQL Parsing Optimizing Terms Optimizing Methods Query Plan Generation Query Plan Control Tuning Container Databases and Pluggable Databases Pluggable tuning parameters Define Container tuning structure Using PDB$SEED Create a new PDB Plug and unplug a PDB Oracle 12c Tuning features Identifying and Using Oracle's Heat Map 12c Compression Levels and Types Evaluating Execution Plans Defining SQL execution plans Automatic Workload Repository Reading execution plans Oracle Tuning Tools Monitoring tools overview Enterprise Manager Dynamic Performance Views Automatic Workload Repository Automatic Database Diagnostic Monitor Sql Tuning Advisor SQL Access Advisor Sql Access Advisor DB operation Tuning DB operation Active Reporting Using Automatic Workload Repository Defining AWR AWR Settings Creating AWR Baselines Metrics, Alerts, and Thresholds Defining Metrics Setting Alerts Setting Corrective Actions User Defined Metrics Metric Dynamic Views Join Types Nested Loops Join Sort Merge join Hash Join and Cartesian Join Equijoins and Nonequijoins Outer Joins Semijoins AWR Using Baselines Creating AWR baselines Creating AWR Repeating baselines Moving Window Baseline Additional AWR performance tools Automatic Maintenance Tasks Segment Advisor Statistics Gathering Automatic Tuning Optimizer Automatic Database Diagnostic Monitor Active Session History (ASH) Optimizer Statistics Optimizer Statistics Overview Table and Index Statistics Statistic Preferences Statistics Gathering e) Locking Statistics, Export/Import Statistics Pending and published statistics Optimizer Hints Optimizer Paths Cost Base Optimization Monitoring a Service Overview of what is an Oracle Service Creating an Oracle Service for Single instance and RAC Monitoring a Service Resource Management and a Service Enterprise Manager and a Service Bind Variables and database parameters Bind variable definition Cursor_sharing parameter Adaptive Cursor Sharing Oracle's Real Application Testing (RAT) Sql Performance Analyzer overview Sql Performance Analyzer Options Database Parameter changes Database version changes Creating SQL Tuning Sets Database Replay Overview Database Replay Configuration Database Replay Options SQL Tuning Advisor SQL Tuning Advisor: Overview SQL Tuning Advisor Limited Mode Sql Tuning Advisor Comprehensive mode Sql Tuning Profiles SQL Access Advisor SQL Access Advisor: Overview Sql Access Advisor options SQL Access Advisor and Sql Tuning Sets Sql Access Advisor and AWR Results and Implementation Automatic Sql Tuning Automatic Sql Tuning Maintenance Task Automatic Tuning Optimization implementation(ATO) Automatic Tuning Optimization Results Enable/Disable Automatic Tuning Optimization Sql Plan Management Sql plan Management and baseline overview Enable sql plan management Loading Sql Plan baselines into the SGA Adaptive plan management Shared Pool Tuning Shared pool architecture Shared pool parameters Library Cache Dictionary cache Large pool considerations and contents Tuning the database buffer cache Database buffer cache overview Database buffer cache parameters Oracle and Dirty reads and writes Automatic Shared Memory Management (ASMM) Buffer Cache goals and responsibility Buffer Cache pools Tuning the PGA (Program Global Area) PGA Overview PGA Database Parameters Temporary Segments Temporary Tablespace Sizing the PGA Automatic Memory Management (AMM) Oracle's Automatic Memory Management Overview Database Auto-tuned Parameters Database Non Auto-tuned Parameters Automatic Memory Management Hints and Sizing suggestions AMM versus ASMM Tuning Segment Space Utilization (ASSM) Overview of Automatic Segment Space Management Defining the DB_BLOCK_SIZE Defining DB_nk_CACHE_SIZE parameter The DB_BLOCK_SIZE Parameter Overview of table compression, block chaining, and block migration Automatic Storage Management Overview of ASM Definition of Grid Infrastructure ASM Instance ASM Diskgroups ASM Diskgroup parameters and templates ASMCMD

Lean Six Sigma Black Belt Certification Program: In-House Training This course is specifically for people wanting to become Lean Six Sigma Black Belts, who are already Lean Six Sigma practitioners. If advanced statistical analysis is needed to identify root causes and optimal process improvements, (Lean) Six Sigma Green Belts typically ask Black Belts or Master Black Belts to conduct these analyses. This course will change that. Green Belts wanting to advance their statistical abilities will have a considerable amount of hands-on practice in techniques such as Statistical Process Control, MSA, Hypothesis Testing, Correlation and Regression, Design of Experiments, and many others. Participants will also work throughout the course on a real-world improvement project from their own business environment. This provides participants with hands-on learning and provides the organization with an immediate ROI once the project is completed. IIL instructors will provide free project coaching throughout the course. What you Will Learn At the end of this program, you will be able to: Use Minitab for advanced data analysis Develop appropriate sampling strategies Analyze differences between samples using Hypothesis Tests Apply Statistical Process Control to differentiate common cause and special cause variation Explain and apply various process capability metrics Conduct Measurement System Analysis and Gage R&R studies for both discrete and continuous data Conduct and analyze simple and multiple regression analysis Plan, execute, and analyze designed experiments Drive sustainable change efforts through leadership, change management, and stakeholder management Successfully incorporate advanced analysis techniques while moving projects through the DMAIC steps Explain the main concepts of Design for Six Sigma including QFD Introduction: DMAIC Review IIL Black Belt Certification Requirements Review Project Selection Review Define Review Measure Review Analyze Review Improve Review Control Introduction: Minitab Tool Introduction to Minitab Minitab basic statistics and graphs Special features Overview of Minitab menus Introduction: Sampling The Central Limit Theorem Confidence Interval of the mean Sample size for continuous data (mean) Confidence Interval for proportions Sample size for discrete data (proportions) Sampling strategies (review) Appendix: CI and sample size for confidence levels other than 95% Hypothesis Testing: Introduction Why use advanced stat tools? What are hypothesis tests? The seven steps of hypothesis tests P value errors and hypothesis tests Hypothesis Testing: Tests for Averages 1 factor ANOVA and ANOM Main Effect Plots, Interaction Plots, and Multi-Vari Charts 2 factor ANOVA and ANOM Hypothesis Testing: Tests for Standard Deviations Testing for equal variance Testing for normality Choosing the right hypothesis test Hypothesis Testing: Chi Square and Other Hypothesis Test Chi-square test for 1 factor ANOM test for 1 factor Chi-square test for 2 factors Exercise hypothesis tests - shipping Non-parametric tests Analysis: Advanced Control Charts Review of Common Cause and Special Cause Variation Review of the Individuals Control Charts How to calculate Control Limits Four additional tests for Special Causes Control Limits after Process Change Discrete Data Control Charts Control Charts for Discrete Proportion Data Control Charts for Discrete Count Data Control Charts for High Volume Processes with Continuous Data Analysis: Non-Normal Data Test for normal distribution Box-Cox Transformation Box-Cox Transformation for Individuals Control Charts Analysis: Time Series Analysis Introduction to Time Series Analysis Decomposition Smoothing: Moving Average Smoothing: EWMA Analysis: Process Capability Process capability Discrete Data: Defect metrics Discrete Data: Yield metrics Process Capability for Continuous Data: Sigma Value Short- and long-term capabilities Cp, Cpk, Pp, Ppk capability indices Analysis: Measurement System Analysis What is Measurement System Analysis? What defines a good measurement system? Gage R&R Studies Attribute / Discrete Gage R&R Continuous Gage R&R Regression Analysis: Simple Correlation Correlation Coefficient Simple linear regression Checking the fit of the Regression Model Leverage and influence analysis Correlation and regression pitfalls Regression Analysis: Multiple Regression Analysis Introduction to Multiple Regression Multicollinearity Multiple Regression vs. Simple Linear Regression Regression Analysis: Multiple Regression Analysis with Discrete Xs Introduction Creating indicator variables Method 1: Going straight to the intercepts Method 2: Testing for differences in intercepts Logistic Regression: Logistic Regression Introduction to Logistic Regression Logistic Regression - Adding a Discrete X Design of Experiments: Introduction Design of Experiment OFAT experimentation Full factorial design Fractional factorial design DOE road map, hints, and suggestions Design of Experiments: Full Factorial Designs Creating 2k Full Factorial designs in Minitab Randomization Replicates and repetitions Analysis of results: Factorial plots Analysis of results: Factorial design Analysis of results: Fits and Residuals Analysis of results: Response Optimizer Analysis of results: Review Design of Experiments: Pragmatic Approaches Designs with no replication Fractional factorial designs Screening Design of Experiment Case Study Repair Time Blocking Closing: Organizational Change Management Organizational change management Assuring project sponsorship Emphasizing shared need for change Mobilizing stakeholder commitment Closing: Project Management for Lean Six Sigma Introduction to project management Project management for Lean Six Sigma The project baseline plan Work Breakdown Structure (WBS) Resource planning Project budget Project risk Project schedule Project executing Project monitoring and controlling and Closing Closing: Design for Lean Six Sigma Introduction to Design for Lean Six Sigma (DMADV) Introduction to Quality Function Deployment (QFD) Summary and Next Steps IIL's Lean Six Sigma Black Belt Certification Program also prepares you to pass the IASSC Certified Black Belt Exam (optional)

About this Virtual Instructor Led Training (VILT)  A decision to drill an exploration well with the objective to find a new oil or gas field must be based on sound assessment of the prospect risk and of the volumes. What is the chance that a well will find hydrocarbons, and how much could it be? Risk and volume assessments form the basis for decisions to drill a well or not, and as such form the link between subsurface evaluation and the business aspects of the petroleum industry. This Virtual Instructor Led Training (VILT) course explains how risks and volumes can be assessed in a realistic manner, based on a sound understanding of the geological details of the prospect as well as its regional geological setting and current play understanding. Participants of this VILT course will receive a softcopy of Risk and Volume Assessment Handbook which explains the concepts that are associated with probabilistic Risk & Volume (R & V) Assessment and contains many practical recommendations on how to translate geological understanding into meaningful inputs for probabilistic R &V assessments. The book is fully compatible with any probabilistic R & V tool in the industry. Training Objectives By the end of this VILT course, participants will be able to understand: The fundamentals of risk and volumes assessment; translating geological understanding into reasonable numbers and ranges. The difference between risk and uncertainty. Fundamentals of statistics; including explanation of distribution curves, understanding of expectation curves, do's and don'ts for adding risked volumes, and Bayes theorem. Uncertainty of trap, reservoir, seal and charge, illustrated by examples. Guidelines and exercises for estimating risks realistically and consistently. Calculating volume ranges for prospects and for portfolios of prospects; how to add prospect volumes for a correct representation of prospect portfolios. Incorporation of geophysical evidence (DHIs) in a realistic risk assessment. Target Audience This VILT course has been designed in the first place for geoscientists working in exploration, for prospect portfolio analysts and for their direct supervisors. It will also benefit staff from disciplines working closely with exploration staff, such as reservoir engineers, petrophysicists and geophysicists. Course Level Intermediate Training Methods Learning, methods and tools The VILT course will be delivered online in 5 half-day sessions comprising 4 hours per day, with 2 breaks of 10 minutes per day. It is the intention to have at least 2 smaller exercises per day. Time will be reserved for recapitulation, questions and discussions. VILT will be conducted either via Zoom or Microsoft Teams. Presenting materials can easily be done on this platform. When participants need to ask a question, they can raise their hand, write notes or interrupt the Instructor by using their microphone. The presenter can switch to a screen where he/she can see all participants (also when each participant is sitting in another location e.g. at home). There is also a whiteboard functionality that can be used as one would use a flip chart. Exercises will be done on an online platform which provides each participant with a private work area that can be accessed by the Instructor to discuss the exercise in a similar manner as in a classroom course. Each topic is introduced by a lecture, and learning is re-enforced by practical exercises and discussions. Handout material in electronic format will be provided. Trainer Dr. Jan de Jager has a PhD in Geology from the University of Utrecht. He joined Shell in 1979 as an exploration geologist, and worked in several locations around the world such as Netherlands, Gabon, USA, Australia, Argentina, and Malaysia in technical and management positions. During the last 10 years of his career, he was responsible for the quality assurance of Shell's exploration prospects in many parts of the world and for upgrading and replenishing Shell's global exploration portfolio. During this period, he had also developed extensive expertise in Prospect Risk and Volume assessments for which he ran successful internal training programmes. Following his retirement from Shell in 2010, Dr Jan de Jager took on a position as part-time professor at the University of Amsterdam and also serves as a consultant exploration advisor for various E&P companies. POST TRAINING COACHING SUPPORT (OPTIONAL) To further optimise your learning experience from our courses, we also offer individualized 'One to One' coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster. Request for further information about post training coaching support and fees applicable for this. Accreditions And Affliations

Lean Six Sigma Black Belt Certification Program: Virtual In-House Training This course is specifically for people wanting to become Lean Six Sigma Black Belts, who are already Lean Six Sigma practitioners. If advanced statistical analysis is needed to identify root causes and optimal process improvements, (Lean) Six Sigma Green Belts typically ask Black Belts or Master Black Belts to conduct these analyses. This course will change that. Green Belts wanting to advance their statistical abilities will have a considerable amount of hands-on practice in techniques such as Statistical Process Control, MSA, Hypothesis Testing, Correlation and Regression, Design of Experiments, and many others. Participants will also work throughout the course on a real-world improvement project from their own business environment. This provides participants with hands-on learning and provides the organization with an immediate ROI once the project is completed. IIL instructors will provide free project coaching throughout the course. What you Will Learn At the end of this program, you will be able to: Use Minitab for advanced data analysis Develop appropriate sampling strategies Analyze differences between samples using Hypothesis Tests Apply Statistical Process Control to differentiate common cause and special cause variation Explain and apply various process capability metrics Conduct Measurement System Analysis and Gage R&R studies for both discrete and continuous data Conduct and analyze simple and multiple regression analysis Plan, execute, and analyze designed experiments Drive sustainable change efforts through leadership, change management, and stakeholder management Successfully incorporate advanced analysis techniques while moving projects through the DMAIC steps Explain the main concepts of Design for Six Sigma including QFD Introduction: DMAIC Review IIL Black Belt Certification Requirements Review Project Selection Review Define Review Measure Review Analyze Review Improve Review Control Introduction: Minitab Tool Introduction to Minitab Minitab basic statistics and graphs Special features Overview of Minitab menus Introduction: Sampling The Central Limit Theorem Confidence Interval of the mean Sample size for continuous data (mean) Confidence Interval for proportions Sample size for discrete data (proportions) Sampling strategies (review) Appendix: CI and sample size for confidence levels other than 95% Hypothesis Testing: Introduction Why use advanced stat tools? What are hypothesis tests? The seven steps of hypothesis tests P value errors and hypothesis tests Hypothesis Testing: Tests for Averages 1 factor ANOVA and ANOM Main Effect Plots, Interaction Plots, and Multi-Vari Charts 2 factor ANOVA and ANOM Hypothesis Testing: Tests for Standard Deviations Testing for equal variance Testing for normality Choosing the right hypothesis test Hypothesis Testing: Chi Square and Other Hypothesis Test Chi-square test for 1 factor ANOM test for 1 factor Chi-square test for 2 factors Exercise hypothesis tests - shipping Non-parametric tests Analysis: Advanced Control Charts Review of Common Cause and Special Cause Variation Review of the Individuals Control Charts How to calculate Control Limits Four additional tests for Special Causes Control Limits after Process Change Discrete Data Control Charts Control Charts for Discrete Proportion Data Control Charts for Discrete Count Data Control Charts for High Volume Processes with Continuous Data Analysis: Non-Normal Data Test for normal distribution Box-Cox Transformation Box-Cox Transformation for Individuals Control Charts Analysis: Time Series Analysis Introduction to Time Series Analysis Decomposition Smoothing: Moving Average Smoothing: EWMA Analysis: Process Capability Process capability Discrete Data: Defect metrics Discrete Data: Yield metrics Process Capability for Continuous Data: Sigma Value Short- and long-term capabilities Cp, Cpk, Pp, Ppk capability indices Analysis: Measurement System Analysis What is Measurement System Analysis? What defines a good measurement system? Gage R&R Studies Attribute / Discrete Gage R&R Continuous Gage R&R Regression Analysis: Simple Correlation Correlation Coefficient Simple linear regression Checking the fit of the Regression Model Leverage and influence analysis Correlation and regression pitfalls Regression Analysis: Multiple Regression Analysis Introduction to Multiple Regression Multicollinearity Multiple Regression vs. Simple Linear Regression Regression Analysis: Multiple Regression Analysis with Discrete Xs Introduction Creating indicator variables Method 1: Going straight to the intercepts Method 2: Testing for differences in intercepts Logistic Regression: Logistic Regression Introduction to Logistic Regression Logistic Regression - Adding a Discrete X Design of Experiments: Introduction Design of Experiment OFAT experimentation Full factorial design Fractional factorial design DOE road map, hints, and suggestions Design of Experiments: Full Factorial Designs Creating 2k Full Factorial designs in Minitab Randomization Replicates and repetitions Analysis of results: Factorial plots Analysis of results: Factorial design Analysis of results: Fits and Residuals Analysis of results: Response Optimizer Analysis of results: Review Design of Experiments: Pragmatic Approaches Designs with no replication Fractional factorial designs Screening Design of Experiment Case Study Repair Time Blocking Closing: Organizational Change Management Organizational change management Assuring project sponsorship Emphasizing shared need for change Mobilizing stakeholder commitment Closing: Project Management for Lean Six Sigma Introduction to project management Project management for Lean Six Sigma The project baseline plan Work Breakdown Structure (WBS) Resource planning Project budget Project risk Project schedule Project executing Project monitoring and controlling and Closing Closing: Design for Lean Six Sigma Introduction to Design for Lean Six Sigma (DMADV) Introduction to Quality Function Deployment (QFD) Summary and Next Steps IIL's Lean Six Sigma Black Belt Certification Program also prepares you to pass the IASSC Certified Black Belt Exam (optional)

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Python Data Analytics with Python using Numpy, Pandas, Dataframes. Most attendees are in-work Data Professional. Private individuals are very welcome. Our Style: Hands-on, Practical Location: Online, Instructor-led

Duration 3 Days 18 CPD hours This course is intended for This course is intended for system administrators, network administrators and application developers responsible for the customization of traffic flow through a BIG-IP system. This course provides networking professionals a functional understanding of iRules development. The course builds on the foundation of the Administering BIG-IP or Configuring LTM course, demonstrating how to logically plan and write iRules to help monitor and manage common tasks involved with processing traffic on the BIG-IP system. Extensive course labs consist of writing, applying and evaluating the effect of iRules on local traffic. This hands-on course includes lectures, labs, and discussions. Module 1: Setting Up the BIG-IP System Introducing the BIG-IP System Initially Setting Up the BIG-IP System Archiving the BIG-IP System Configuration Leveraging F5 Support Resources and Tools Module 2: Getting Started with iRules Customizing Application Delivery with iRules Triggering an iRule Leveraging the DevCentral Ecosystem Creating and Deploying iRules Module 3: Exploring iRule Elements Introducing iRule Constructs Understanding iRule Events and Event Context Working with iRule Commands Logging from an iRule Using SYSLOG-NG (LOG Command) Working with User-Defined Variables Working with Operators and Data Types Working with Conditional Control Structures (IF and SWITCH) Incorporating Best Practices in iRules Module 4: Developing and Troubleshooting iRules Mastering Whitespace and Special Symbols Grouping Strings Developing and Troubleshooting Tips Using Fiddler to Test and Troubleshoot iRules Module 5: Optimizing iRule Execution Understanding the Need for Efficiency Measure iRule Runtime Efficiency Using Timing Statistics Modularizing iRules for Administrative Efficiency Using Procedures to Modularize Code Optimizing Logging Using High-Speed Logging Commands in an iRule Implementing Other Efficiencies Using Looping Control Structures (WHILE, FOR, FOREACH Commands) Module 6: Securing Web Applications with iRules Integrating iRules into Web Application Defense Mitigating HTTP Version Attacks Mitigating Path Traversal Attacks Using iRules to Defends Against Cross-Site Request Forgery (CSRF) Mitigating HTTP Method Vulnerabilities Securing HTTP Cookies with iRules Adding HTTP Security Headers Removing Undesirable HTTP Headers Module 7: Working with Numbers and Strings Understanding Number Forms and Notation Working with Strings (STRING and SCAN Commands) Combining Strings (Adjacent Variables, CONCAT and APPEND Commands) Using iRule String Parsing Functions (FINDSTR, GETFIELD, and SUBSTR Commands) Module 8: Processing the HTTP Payload Reviewing HTTP Headers and Commands Accessing and Manipulating HTTP Headers (HTTP::header Commands) Other HTTP commands (HTTP::host, HTTP::status, HTTP::is_keepalive, HTTP::method, HTTP::version, HTTP::redirect, HTTP::respond, HTTP::uri) Parsing the HTTP URI (URI::path, URI::basename, URI::query) Parsing Cookies with HTTP::cookie Selectively Compressing HTTP Data (COMPRESS Command) Module 9: Working with iFiles and Data Groups Working with iFiles Introducing Data Groups Working with Old Format Data Groups (MATCHCLASS, FINDCLASS) Working with New Format Data Groups (CLASS MATCH, CLASS SEARCH) Module 10: Using iRules with Universal Persistence, Stream, and Statistics Profiles Implementing Universal Persistence (PERSIST UIE Command) Working with the Stream Profile (STREAM Command) Collecting Statistics Using a Statistics Profile (STATS Command) Collecting Statistics Using iStats (ISTATS Command) Module 11: Incorporating Advanced Variables Reviewing the Local Variable Namespace Working with Arrays (ARRAY Command) Using Static and Global Variables Using the Session Table (TABLE Command) Processing Session Table Subtables Counting ?Things? Using the Session Table

Duration 5 Days 30 CPD hours This course is intended for Data Warehouse AdministratorDatabase Administrators Overview Use the Oracle Database tuning methodology appropriate to the available toolsUtilize database advisors to proactively tune an Oracle Database InstanceUse the tools based on the Automatic Workload Repository to tune the databaseDiagnose and tune common SQL related performance problemsDiagnose and tune common Instance related performance problemsUse Enterprise Manager performance-related pages to monitor an Oracle DatabaseGain an understanding of the Oracle Database Cloud Service In the Oracle Database 12c: Performance Management and Tuning course, learn about the performance analysis and tuning tasks expected of a DBA: proactive management through built-in performance analysis features and tools, diagnosis and tuning of the Oracle Database instance components, and diagnosis and tuning of SQL-related performance issues. In this course, you will be introduced to Oracle Database Cloud Service. Introduction Course Objectives Course Organization Course Agenda Topics Not Included in the Course Who Tunes? What Does the DBA Tune? How to Tune Tuning Methodology Basic Tuning Diagnostics Performance Tuning Diagnostics Performance Tuning Tools Tuning Objectives Top Timed Events DB Time CPU and Wait Time Tuning Dimensions Time Model Dynamic Performance Views Using Automatic Workload Repository Automatic Workload Repository Overview Automatic Workload Repository Data Enterprise Manager Cloud Control and AWR Snapshots Reports Compare Periods Defining the Scope of Performance Issues Defining the Problem Limiting the Scope Setting the Priority Top SQL Reports Common Tuning Problems Tuning During the Life Cycle ADDM Tuning Session Performance Versus Business Requirements Using Metrics and Alerts Metrics and Alerts Overview Limitation of Base Statistics Benefits of Metrics Viewing Metric History Information Viewing Histograms Server-Generated Alerts Setting Thresholds Metrics and Alerts Views Using Baselines Comparative Performance Analysis with AWR Baselines Automatic Workload Repository Baselines Moving Window Baseline Baselines in Performance Page Settings Baseline Templates AWR Baseslines Creating AWR Baselines Managing Baselines with PL/SQL Using AWR-Based Tools Automatic Maintenance Tasks ADDM Performance Monitoring Using Compare Periods ADDM Active Session History New or Enhanced Automatic Workload Repository Views Emergency Monitoring Real-time ADDM Real-Time Database Operation Monitoring Overview Use Cases Defining a Database Operation Scope of a Composite Database Operation Database Operation Concepts Identifying a Database Operation Enabling Monitoring of Database Operations Identifying, Starting, and Completing a Database Operation Monitoring Applications What is a Service? Service Attributes Service Types Creating Services Managing Services in a Single-Instance Environment Where are Services Used? Using Services with Client Applications Services and Pluggable Databases Identifying Problem SQL Statements SQL Statement Processing Phases Role of the Oracle Optimizer Identifying Bad SQL Top SQL Reports SQL Monitoring What is an Execution Plan? Methods for Viewing Execution Plans Uses of Execution Plans Influencing the Optimizer Functions of the Query Optimizer Selectivity Cardinality and Cost Changing Optimizer Behavior Optimizer Statistics Extended Statistics Controlling the Behavior of the Optimizer with Parameters Enabling Query Optimizer Features Reducing the Cost of SQL Operations Reducing the Cost Index Maintenance SQL Access Advisor Table Maintenance for Performance Table Reorganization Methods Space Management Extent Management Data Storage Using SQL Performance Analyzer Real Application Testing: Overview Real Application Testing: Use Cases SQL Performance Analyzer: Process Capturing the SQL Workload Creating a SQL Performance Analyzer Task SQL Performance Analyzer: Tasks Parameter Change SQL Performance Analyzer Task Page SQL Performance Management Maintaining SQL Performance Maintaining Optimizer Statistics Automated Maintenance Tasks Statistic Gathering Options Setting Statistic Preferences Restore Statistics Deferred Statistics Publishing Automatic SQL Tuning Using Database Replay Using Database Replay The Big Picture System Architecture Capture Considerations Replay Considerations: Preparation Replay Considerations Replay Options Replay Analysis Tuning the Shared Pool Shared Pool Architecture Shared Pool Operation The Library Cache Latch and Mutex Diagnostic Tools for Tuning the Shared Pool Avoiding Hard Parses Reducing the Cost of Soft Parses Sizing the Shared Pool Tuning the Buffer Cache Oracle Database Architecture: Buffer Cache Buffer Cache: Highlights Database Buffers Buffer Hash Table for Lookups Working Sets Buffer Cache Tuning Goals and Techniques Buffer Cache Performance Symptoms Buffer Cache Performance Solutions Tuning PGA and Temporary Space SQL Memory Usage Performance Impact Automatic PGA Memory SQL Memory Manager Configuring Automatic PGA Memory Setting PGA_AGGREGATE_TARGET Initially Limiting the size of the Program Global Area (PGA) SQL Memory Usage Automatic Memory Oracle Database Architecture Dynamic SGA Granule Memory Advisories Manually Adding Granules to Components Increasing the Size of an SGA Component Automatic Shared Memory Management: Overview SGA Sizing Parameters: Overview Performance Tuning Summary with Waits Commonly Observed Wait Events Additional Statistics Top 10 Mistakes Found in Customer Systems Symptoms Oracle Database Cloud Service: Overview Database as a Service Architecture, Features and Tooling Software Editions: Included Database Options and Management Packs Accessing the Oracle Database Cloud Service Console Automated Database Provisioning Managing the Compute Node Associated With a Database Deployment Managing Network Access to Database as a Service Scaling a Database Deployment Performance Management in the Database Cloud Environment Performance Monitoring and Tuning What Can be Tuned in a DBCS Environment?