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https://github.com/bursasha/oracle-postgresql-projects

Advanced SQL, covering Oracle PL/SQL and PostgreSQL PL/pgSQL with phases on using procedures, functions, triggers, query optimizations, packages, and Docker server setup 💡
https://github.com/bursasha/oracle-postgresql-projects

common-table-expression docker docker-compose function oracle oracle-plsql plpgsql plsql posgtresql postgresql-plpgsql procedure query-optimization recursive-queries script sql trigger

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Advanced SQL, covering Oracle PL/SQL and PostgreSQL PL/pgSQL with phases on using procedures, functions, triggers, query optimizations, packages, and Docker server setup 💡

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README 

        
# Advanced SQL, Oracle PL/SQL, and PostgreSQL PL/pgSQL University Course Project 📘



## Overview 🏫

This repository contains projects developed during a university course on advanced and in-depth **SQL**, **Oracle PL/SQL**, and **PostgreSQL PL/pgSQL**. 

Each phase of the project builds upon the previous, exploring different aspects of database management, data processing, and query optimization.



## Repository Structure 📁

- `Phase1/`: Data Generation

- `Phase2/`: Implementation of Complex Integrity Constraints

- `Phase3/`: Otter Lineage Tracing in PL/SQL

- `Phase4/`: SQL Query Optimization and Analysis

- `Phase5/`: Data Processing and Audit in PostgreSQL

- `README.md`: Main README file for the repository.



## Technology Stack and Concepts 🔧

- **Languages and Frameworks:**

  - **Oracle PL/SQL**

  - **PostgreSQL PL/pgSQL**

  - **Docker** for PostgreSQL server setup



- **Key Concepts:**

  - Procedures, Functions, and Packages

  - Anonymous Blocks

  - Triggers

  - Recursive Queries and Common Table Expressions (CTEs)

  - Data Preprocessing and Validation

  - Indexing and Query Optimization

  - Data Integrity and Constraint Management

  - Data Loading and Exporting

  - Audit Report Generation



## Phase Descriptions 📜



### 1. Data Generation 📑

**Objective:** Generate realistic data for `tbl_CLIENT`, `tbl_PAYMENT`, and `tbl_SERVICE` tables using Oracle PL/SQL.

**Key Elements:**

- Dynamic table drop and creation scripts.

- Procedures for populating tables with synthetic data.



**Reflections and Learnings 🌟**

- This phase involved understanding the data generation process, ensuring data integrity, and effectively using PL/SQL procedures for bulk data operations.



### 2. Implementation of Complex Integrity Constraints 🛠️

**Objective:** Manage data integrity and implement business logic using PL/SQL packages and triggers.

**Key Elements:**

- Package `pkg_iFix_management` for advanced CRUD operations.

- Trigger `trg_CheckServiceCostLimit` to enforce financial constraints based on client privilege levels.



**Reflections and Learnings 🌟**

- This phase highlighted the power of PL/SQL in managing complex business rules and maintaining data integrity through procedural logic and triggers.



### 3. Otter Lineage Tracing in PL/SQL 🦦

**Objective:** Trace the lineage of otters using recursive queries in PL/SQL.

**Key Elements:**

- Recursive Common Table Expressions (CTEs) to retrieve and display otter lineage.

- Procedures and scripts for table creation, data insertion, and lineage tracing.



**Reflections and Learnings 🌟**

- This phase provided practical experience with recursive queries and CTEs, showcasing their effectiveness in handling hierarchical data.



### 4. SQL Query Optimization and Analysis 🛠️

**Objective:** Optimize and analyze SQL queries within the Oracle database environment.

**Key Elements:**

- Execution plan analysis and query refactoring.

- Implementation of indexing, join optimization, and parallel execution.



**Reflections and Learnings 🌟**

- This phase deepened understanding of SQL performance tuning, emphasizing the importance of efficient query design and execution plan analysis.



### 5. Data Processing and Audit in PostgreSQL 🛠️

**Objective:** Process and audit data within PostgreSQL, generating reports and ensuring data integrity.

**Key Elements:**

- Data loading procedure `load_csv_data` for importing data from CSV files.

- Audit report generation procedure `save_csv_audit` for exporting monthly audit data.



**Reflections and Learnings 🌟**

- This phase demonstrated the complete data processing and auditing workflow within a PostgreSQL environment, highlighting practical applications for real-world data management.



## Conclusion 📝

This repository encapsulates a comprehensive learning journey through advanced database management, focusing on both Oracle PL/SQL and PostgreSQL PL/pgSQL. 

The projects demonstrate practical applications of complex SQL concepts, recursive queries, data integrity management, and performance optimization.