PostgreSQL Admin and Development

Duration 5 days – 35 hrs

 

Overview

 

This PostgreSQL Admin and Development Training Course is designed to enhance the technical skills of support teams, enabling them to effectively manage and develop PostgreSQL databases in a data warehouse environment. Participants will gain both administrative and development expertise in PostgreSQL, learning how to optimize database performance, manage large-scale data, ensure data integrity, and support the data warehouse applications. The course will also cover best practices for backup, recovery, and security, empowering the team to handle real-world database challenges in a professional manner. This PostgreSQL Admin and Development Training Course will empower your support team with the necessary skills to properly manage, optimize, and troubleshoot PostgreSQL databases, ensuring the smooth operation of your data warehouse applications.

 

Objectives

 

• Learn PostgreSQL database architecture and setup.
• Understand and apply best practices for PostgreSQL database administration, including backup and recovery.
• Develop skills in writing optimized SQL queries and PL/pgSQL functions.
• Gain knowledge of performance tuning and optimization techniques in PostgreSQL.
• Implement security practices and manage user access in PostgreSQL databases.
• Learn how to handle large-scale data management, ensuring data integrity for data warehouse applications.
• Be equipped to troubleshoot and resolve common PostgreSQL issues in a production environment.

 

Audience

 

• Database Administrators (DBAs) managing PostgreSQL databases.
• Support Teams responsible for maintaining PostgreSQL systems.
• Data Analysts and Developers working with PostgreSQL in data warehouse applications.
• IT professionals who need to learn how to develop and administer PostgreSQL databases effectively.
• Technical teams that need to acquire skills to support data warehouse applications on PostgreSQL.

 

 

Pre- requisites

• Basic understanding of database concepts and SQL.
• Familiarity with data warehouse concepts and requirements.
• Experience in working with relational databases (RDBMS) is beneficial but not mandatory.
• Basic understanding of Linux/Unix command line (for administering PostgreSQL).

 

Course Content

 

Introduction to PostgreSQL Administration

 

• Module 1: PostgreSQL Architecture
  • Overview of PostgreSQL architecture: processes, memory, and storage.
  • Understanding the PostgreSQL data directory, configuration files, and logs.
  • Installing PostgreSQL on various operating systems (Linux, Windows).
  • Setting up PostgreSQL instances and managing multiple clusters.

 

• Module 2: Database and User Management
  • Creating and managing databases and schemas.
  • User roles, permissions, and security best practices.
  • Managing connections and transaction isolation levels.
  • Handling database backups and restores (pg_dump, pg_restore, and PITR).

 

• Module 3: PostgreSQL Configuration and Tuning
  • Configuring PostgreSQL for optimal performance.
  • Key configuration parameters for performance tuning.
  • Using the pg_stat_activity and pg_stat_statements views for monitoring.
  • Adjusting PostgreSQL memory, cache, and I/O settings for performance.

 

 

 

Advanced PostgreSQL Administration

 

• Module 4: Backup and Recovery Strategies
  • Full and incremental backups in PostgreSQL.
  • Point-in-time recovery (PITR).
  • Disaster recovery planning and tools (e.g., streaming replication, pg_basebackup).
  • Automating backups and restoring from backup files.

 

• Module 5: Performance Tuning and Optimization
  • Identifying and resolving performance bottlenecks in PostgreSQL.
  • Query optimization and indexing strategies (e.g., B-tree, GIN, GiST indexes).
  • Using EXPLAIN and ANALYZE to analyze query performance.
  • Database vacuuming and autovacuum tuning.

 

• Module 6: PostgreSQL Replication and High Availability
  • Overview of synchronous and asynchronous replication.
  • Setting up streaming replication for high availability.
  • Configuring failover and automatic failover management with tools like Patroni.
  • Cluster management and monitoring with tools like pgAdmin and PgBouncer.

 

 

PostgreSQL Development and Data Warehouse Integration

 

• Module 7: Writing Optimized SQL Queries
  • Best practices for writing efficient SQL queries.
  • Understanding joins, subqueries, and set operations.
  • Using window functions, common table expressions (CTEs), and recursive queries.
  • Optimizing aggregate and complex queries for data warehouses.
• Module 8: PL/pgSQL Programming
  • Introduction to PL/pgSQL for writing stored procedures and functions.
  • Managing control flow in PL/pgSQL (loops, exceptions, and conditionals).
  • Writing triggers and event-based functions.
  • Performance considerations when using PL/pgSQL in large-scale applications.

 

 

• Module 9: Data Warehouse Management with PostgreSQL
  • Integrating PostgreSQL with data warehouse applications.
  • Techniques for handling large data sets: partitioning, sharding, and indexing.
  • Managing and optimizing ETL processes in PostgreSQL.
  • Advanced data management techniques: materialized views and query optimization for OLAP.

 

 

 

Security, Monitoring, Troubleshooting, and Best Practices

 

• Module 10: PostgreSQL Security
  • Understanding and implementing PostgreSQL authentication and access control.
  • Using SSL for secure client-server communication.
  • Managing data encryption at rest and in transit.
  • Auditing and monitoring user activity for compliance.

 

• Module 11: Monitoring and Troubleshooting PostgreSQL
  • Using PostgreSQL logs and external tools (e.g., pgBadger, Prometheus, Grafana) for monitoring.
  • Troubleshooting common database issues: deadlocks, query performance, and disk space.
  • Diagnosing and resolving hardware-related performance issues.
  • Troubleshooting replication, backup, and recovery problems.

 

• Module 12: Best Practices for PostgreSQL in Data Warehouse Applications
  • Strategies for managing large datasets in a data warehouse.
  • Database partitioning and indexing best practices for data warehouse performance.
  • Query optimization for large-scale data warehouse applications.
  • Implementing robust monitoring and maintenance routines to ensure reliability.