CourseGenix

SQL

7 Units30 Lessons

Unit 1

Basics

Introduction to SQL Syntax and Data Types

Executing Simple SELECT Queries

Inserting, Updating, and Deleting Data Basics

Real-World Project: Building a Simple Inventory Database

Unit 2

Advanced Query Techniques

Using Aggregations and Grouping Functions

Advanced String and Date Manipulation Functions

Working with Common Table Expressions (CTEs)

Implementing Window Functions for Data Analysis

Real-World Project: Analyzing Sales Data with Advanced Queries

Unit 3

Joins and Subqueries

Understanding Inner and Outer Joins

Using Subqueries in SELECT Statements

Nesting Subqueries in WHERE and FROM Clauses

Exploring Self-Joins and Cross Joins

Real-World Project: Merging Data from Multiple Tables

Unit 4

Database Design and Normalization

Fundamentals of Database Design Principles

Applying First, Second, and Third Normal Forms

Real-World Project: Designing and Normalizing a Relational Database

Advanced Normalization Techniques like BCNF

Unit 5

Transactions and Concurrency Control

Basics of SQL Transactions and ACID Properties

Implementing Commit, Rollback, and Savepoints

Concurrency Control with Locking Mechanisms

Real-World Project: Managing Transactions in a Banking System

Unit 6

Indexing and Performance Optimization

Introduction to SQL Indexing and Its Types

Real-World Project: Optimizing a Large-Scale Database

Query Optimization Techniques and Execution Plans

Creating and Managing Indexes for Query Speed

Unit 7

Advanced Topics in SQL Integration

Integrating SQL with External Data Sources

Real-World Project: Building an Integrated SQL Application

Using Triggers and Stored Procedures for Automation

Views and Materialized Views in Complex Systems

Unit 6•Chapter 3

Query Optimization Techniques and Execution Plans

Summary

Provide summaries up to 250 words, focusing only on the main topic, excluding sponsors and unrelated details, without introductory remarks

Concept Check

0/5

What is the primary technique used in cost-based query optimization?

Manual intervention required

Random plan selection

Using fixed rules only

Estimating query execution costs

How does an index improve query execution plans?

Reduces data scanning needed

Increases join operations

Slows down data retrieval

Complicates plan generation

In which stage is the query execution plan finalized?

Execution phase

Parsing phase

Validation phase

Optimization phase

What join method is efficient for large datasets in optimization?

Full table scan

Merge sort join

Nested loop join

Hash join algorithm

Why is rule-based optimization less flexible?

Follows predefined rules only

Generates multiple plans

Adapts to data dynamically

Reduces query costs automatically

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