SQL : Key Features, Commands & Practical Use Cases |Data Analytics | Belayet Hossain

SQL (Structured Query Language) is a standard programming language specifically designed for managing and manipulating relational databases.

Key Features of SQL

1. Data Retrieval: SQL allows users to pull specific data from large datasets, using queries to select, filter, and display the exact data needed.
2. Data Manipulation: It supports operations to add, update, and delete data within databases.
3. Data Definition: SQL lets users define the structure of the database, including creating, altering, and deleting tables and other objects.
4. Data Control: It includes commands to control access to the database, manage permissions, and set up user access.

Common SQL Commands

SQL is broken down into several types of commands, each serving a different purpose:

1. Data Definition Language (DDL):

→ CREATE: Creates a new table, view, or database.

→ ALTER: Modifies the structure of an existing table (e.g., adding or removing columns).

→ DROP: Deletes a table or database permanently.

→ TRUNCATE: Removes all rows from a table without deleting the table itself.

→ RENAME: Changes the name of a table or a column in the database.

2. Data Manipulation Language (DML):

→ INSERT: Adds new data into a table.

→ UPDATE: Modifies existing data in a table.

→ DELETE: Removes data from a table.

3. Data Control Language (DCL):

→ GRANT: Gives permissions to users.

→ REVOKE: Removes permissions from users.

4. Transaction Control Language (TCL):

→ COMMIT: Saves changes to the database permanently.

→ ROLLBACK: Undoes changes to the last saved state.

→SAVEPOINT: Sets a temporary point in a transaction to which you can roll back.

5. Data Query Language (DQL):

→ SELECT: Retrieves specific data from one or more tables.

Practical SQL Use Cases for Data Analysis

1. Filtering Data with SELECT and WHERE

• Example: Find all customers from New York with SELECT and WHERE clauses.
• Purpose: Helps to retrieve only the data relevant to a specific query, saving time and simplifying analysis.

2. Aggregating Data with GROUP BY

• Example: Group sales data by region to see total sales per region.
• Purpose: Summarizes large datasets into meaningful insights, like total sales or average ratings.

3. Joining Data with JOIN

• Example: Link customer data with order data to see who bought what.
• Purpose: Combines data from multiple tables based on a common column, which is crucial for relational analysis.

4. Sorting and Limiting Results with ORDER BY and LIMIT

• Example: Retrieve the top 5 best-selling products.
• Purpose: Sorts data to focus on highest or lowest values, allowing quick insights into top-performing or low-performing items.

5. Updating Data with UPDATE

• Example: Update all inactive user accounts to reflect a status change.
• Purpose: Allows for bulk updates in a structured and efficient way, especially useful for data correction.

Real-World Example

Consider a sales database with tables for Customers, Products, and Orders:

• Goal: Find the top 5 products by revenue in a particular month.
• Process:

→ Use JOIN to link Orders with Products to get order quantity and product price.

→ Multiply quantity by price to calculate revenue per product.

→ Use GROUP BY on the product and sum the revenue.

→ Sort results in descending order and use LIMIT to get the top 5 products.

Summary

SQL is essential for working with relational databases and is foundational for tasks like querying, transforming, and analyzing data. It provides a consistent, structured way to handle data, enabling businesses to make data-driven decisions and gain insights efficiently.

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