Course Overview

PostgreSQL Administration Course Overview

This PostgreSQL Administration course covers administration,maintenance,security and performance tuning of PostgreSQL version 10,11,12,13,14,15 and 16 databases.

Exercises and examples are used throughout the course to give practical hands-on experience with the techniques covered.

Course Objectives

Course Objectives

This course aims to provide the delegate with the knowledge to be able to install,administer,maintain,backup,recover and tune a PostgreSQL database.

Course Content

PostgreSQL Administration Training Course

Course Contents – DAY 1

Course Introduction

• Administration and Course Materials
• Course Structure and Agenda
• Delegate and Trainer Introductions

Session 1: INSTALLATION AND CONFIGURATION OF POSTGRESQL

• PostgreSQL Version Numbers
• PostgreSQL Configuration Recommendations
• Single Cluster and Database per Server
• File System Layouts
• Install PostgreSQL
• Install PostgreSQL on Linux/UNIX
• Post-Installation Setup
• Create a Database Cluster
• Basic Server Configuration
• Account Management

Session 2: POSTGRESQL ARCHITECTURE

• Overview of PostgreSQL Architecture
• The PostgreSQL Instance
• How Data is Processed by PostgreSQL
• PostgreSQL Processes
• Shared Memory
• PostgreSQL Directory Structure
• Manage a PostgreSQL Instance
• The PostgreSQL Configuration Files
• Multiversion Concurrency Control
• The Vacuum utility
• PostgreSQL WAL

Session 3: POSTGRESQL CLIENT APPLICATIONS

• Overview of PostgreSQL Client Applications
• The PostgreSQL Interactive Client Terminal – psql
• The pgAdmin Tool
• Client and host based access control
• Client Connection Problems
• Authentication Failures
• Server Startup Failures

Session 4: CREATE AND CONFIGURE A DATABASE

• Create a New Database using the CREATE DATABASE Command
• Create a New Database using pgAdmin
• Start and Stop the Database Server
• Drop a Database
• Copy a Database
• List Databases
• Obtain Database Object Sizes
• Initialise a Cluster

PostgreSQL Administration Training Course

Course Contents – DAY 2

Session 5: POSTGRESQL SCHEMAS

• Overview of the Key Concepts of PostgreSQL Schemas
• Create and Drop a Schema
• The Public Schema
• The Schema Search Path
• Schemas and Privileges
• The System Catalog Schema
• Overview of the Information Schema

Session 6: ROLE MANAGEMENT

• Overview of PostgreSQL Roles and Privileges
• Create a User Defined Role
• Role Attributes
• Role Membership
• Assigning Users to Roles
• Group and User Role Inheritance
• Removing Roles
• Troubleshooting and Understanding Role Access

Session 7: FINE GRAINED ACCESS CONTROL USING GRANT

• Control Database Level Permissions
• Control Schema Level Permissions
• Grant Table Level Permissions
• Define Access Privileges with the GRANT Command
• Remove Access Privileges with the REVOKE Command
• Manage Column Rights

Session 8: INDEX CREATION AND MANAGEMENT

• Index Overview
• The CREATE INDEX Command
• Index Types
• Efficient Usage of PostgreSQL Indexes
• Index creation
• B-tree,Hash and BRIN Indexes
• Single Column Indexes
• Unique indexes
• Multicolumn Indexes
• The INCLUDE clause
• Partial Indexes
• Index Based Expressions
• Creating an index concurrently
• The REINDEX command
• List Indexes
• Manage and Maintain Indexes
• When Indexes Should be Avoided

Session 9: TRANSACTIONS AND CONCURRENCY

• Overview of Transaction Processing in PostgreSQL
• Transaction Properties
• Transaction Control
• Multi-version Concurrency Control
• Concurrency Problems
• Isolation Levels
• Implicit Locking
• Explicit Locking
• Possible Causes of Lock Contention
• Deadlocks
• Advisory Locks
• Lock Management Parameters

PostgreSQL Administration Training Course

Course Contents – DAY 3

Session 10: POSTGRESQL DATABASES STRUCTURE

• The PostgreSQL Configuration Files
• Relocate the Configuration Files
• Physical Storage and File Layout
• Overview of Tablespace Usage
• Table and Row Storage
• Column Limitations
• Free Space Map
• The Visibility Map
• Index Storage

Session 11: MANAGE TABLESPACES

• Overview of PostgreSQL Tablespaces
• Default PostgreSQL Tablespaces
• Create a Tablespace using SQL Commands and pgAdmin
• Create a Tablespace in the UNIX Operating System
• Alter a Tablespace
• Drop a Tablespace

Session 12: POSTGRESQL LOGGING

• PostgreSQL Event Log Destinations
• Configuring syslog,eventlog,stderr and csv format output
• Configuring What Should be Logged and When

Session 13: POSTGRESQL EXTENSIONS

• Install the PostgreSQL Contrib Module
• List the Available Extensions
• Add an Extension to the postgresql.conf File
• Create an Extension in a Database
• Drop an Extension in a Database

PostgreSQL Administration Training Course

Course Contents – DAY 4

Session 14: BACKUP AND RECOVERY OF DATABASES

• Overview of Backup Methods
• Export and Import Operations with COPY
• Backup a Database with Operating System Commands
• Backup a Database with pg_dump
• Backup All Databases with pg_dumpall
• Backup User Credentials
• Backup Database Object Definitions
• Overview of Database Restore
• Restore using psql
• Restore using pg_restore
• File system Backup and Recovery

Session 15: POINT-IN-TIME RECOVERY (PITR)

• Write-ahead Logging and Crash Recovery
• Checkpoints
• List the Transaction Logs
• Transaction Log Optimisation
• Overview of PITR
• Setup PITR
• Continuous Archiving
• Test Transaction Log Archiving
• Create a Base Backup using the Low Level API
• Create a Base Backup using pgBaseBackup
• The PITR Recovery Process
• How To Perform a PITR Recovery
• Recovery Configuration Parameters
• Timelines
• Locating the Correct Timestamp
• Restore Points
• Clean up the Archived Transaction Logs

PostgreSQL Administration Training Course

Course Contents – DAY 5

Session 16: THE POSTGRESQL QUERY OPTIMIZER

• Query Optimization
• Optimization Operations
• Optimization Decisions
• Scan Methods
• Join Methods
• Join Order
• Statement Transformation
• Prepared Statements
• Query Performance Analysis
• Detect Slow Queries
• Use EXPLAIN to optimize Queries and Indexes
• Execution Plans
• Query Planner Statistics
• The ANALYZE command
• The CREATE STATISTICS command
• Parameters Affecting Optimization
• Memory Settings That Affect Performance

Session 17: ROUTINE DATABASE MAINTENANCE

• Optimize Storage and Manage Clean up with VACUUM
• Configure VACUUM
• Configure Autovacuum
• Cost Based Vacuum Delay
• Track a VACUUM Process
• Routine Maintenance Tasks

Session 18: SERVER PERFORMANCE MONITORING AND TUNING

• Monitor Database Activities
• System Monitoring & PostgreSQL Monitoring
• Performance Statistics in the Server Log
• Statistics Collection Configuration
• Monitor Database Activity
• Monitor Table,Index and SQL Statement Activity
• Monitor Background Writer,WAL and Archiving Activity
• Progress Reporting
• Locks

Session 19: POPULATE A DATABASE EFFICIENTLY

• DISABLE autocommit
• Configure Variables for Increased Performance
• Use the COPY Command to Bulk Load Data
• Drop Indexes and Foreign Keys Temporarily
• Use the COPY command to Bulk Load Data
• Temporarily drop indexes and Foreign Key Constraints before a Bulk Load
• Configure Variables for Increased Performance
• Temporarily Disable WAL Archival and Streaming Replication
• Use pg_dump Efficiently

Course Overview

This course helps you explore the IBM Cloud Pak for Data monitoring and alerting framework. In this course, you customize alert forwarding, configure the monitoring stack so that it sends Cloud Pak for Data metrics to Prometheus.  You perform routine Cloud Pak for Data monitoring, which leads to investigating and resolving issues in your environment. You also deploy scripts that use platform APIs, and enhance the monitoring feature by introducing custom monitors.

Virtual Learning

This interactive training can be taken from any location, your office or home and is delivered by a trainer. This training does not have any delegates in the class with the instructor, since all delegates are virtually connected. Virtual delegates do not travel to this course, Global Knowledge will send you all the information needed before the start of the course and you can test the logins.

Course Objectives

• Explain the Cloud Pak for Data monitoring and alerting framework
• Set up the SMTP settings and alert forwarding
• Configure the monitoring stack
• Perform routine Cloud Pak for Data monitoring
• Use platform APIs to review events and monitors
• Deploy a custom monitor

Course Content

• Introduction
• Monitor Cloud Pak for Data
• Work with the monitoring stack
• Perform a routine Cloud Pak for Data monitoring
• Use alerting APIs for platform monitoring
• Set up a custom monitor
• Review and evaluation

Course Overview

This course builds upon and extends the DevOps methodology prevalent in software development to build, train, and deploy machine learning (ML) models. The course is based on the four-level MLOPs maturity framework. The course focuses on the first three levels, including the initial, repeatable, and reliable levels. The course stresses the importance of data, model, and code to successful ML deployments. It demonstrates the use of tools, automation, processes, and teamwork in addressing the challenges associated with handoffs between data engineers, data scientists, software developers, and operations. The course also discusses the use of tools and processes to monitor and take action when the model prediction in production drifts from agreed-upon key performance indicators.

Course Objectives

In this course, you will learn to:

• Explain the benefits of MLOps
• Compare and contrast DevOps and MLOps
• Evaluate the security and governance requirements for an ML use case and describe possible solutions and mitigation strategies
• Set up experimentation environments for MLOps with Amazon SageMaker
• Explain best practices for versioning and maintaining the integrity of ML model assets (data, model, and code)
• Describe three options for creating a full CI/CD pipeline in an ML context
• Recall best practices for implementing automated packaging, testing and deployment. (Data/model/code)
• Demonstrate how to monitor ML based solutions
• Demonstrate how to automate an ML solution that tests, packages, and deploys a model in an automated fashion; detects performance degradation; and re-trains the model on top of newly acquired data

Course Content

Day 1

Module 1: Introduction to MLOps

• Processes
• People
• Technology
• Security and governance
• MLOps maturity model

Module 2: Initial MLOps: Experimentation Environments in SageMaker Studio

• Bringing MLOps to experimentation
• Setting up the ML experimentation environment
• Demonstration: Creating and Updating a Lifecycle Configuration for SageMaker Studio
• Hands-On Lab: Provisioning a SageMaker Studio Environment with the AWS Service Catalog
• Workbook: Initial MLOps

Module 3: Repeatable MLOps: Repositories

• Managing data for MLOps
• Version control of ML models
• Code repositories in ML

Module 4: Repeatable MLOps: Orchestration

• ML pipelines
• Demonstration: Using SageMaker Pipelines to Orchestrate Model Building Pipelines

Day 2

Module 4: Repeatable MLOps: Orchestration (continued)

• End-to-end orchestration with AWS Step Functions
• Hands-On Lab: Automating a Workflow with Step Functions
• End-to-end orchestration with SageMaker Projects
• Demonstration: Standardizing an End-to-End ML Pipeline with SageMaker Projects
• Using third-party tools for repeatability
• Demonstration: Exploring Human-in-the-Loop During Inference
• Governance and security
• Demonstration: Exploring Security Best Practices for SageMaker
• Workbook: Repeatable MLOps

Module 5: Reliable MLOps: Scaling and Testing

• Scaling and multi-account strategies
• Testing and traffic-shifting
• Demonstration: Using SageMaker Inference Recommender
• Hands-On Lab: Testing Model Variants

Day 3

Module 5: Reliable MLOps: Scaling and Testing (continued)

• Hands-On Lab: Shifting Traffic
• Workbook: Multi-account strategies

Module 6: Reliable MLOps: Monitoring

• The importance of monitoring in ML
• Hands-On Lab: Monitoring a Model for Data Drift
• Operations considerations for model monitoring
• Remediating problems identified by monitoring ML solutions
• Workbook: Reliable MLOps
• Hands-On Lab: Building and Troubleshooting an ML Pipeline

 

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