The document discusses use cases in SQL, highlighting scenarios such as data retrieval, manipulation, and analysis. It addresses scalability strategies including indexing, partitioning, caching, load balancing, and data sharding to enhance performance. Additionally, it emphasizes the importance of query optimization, appropriate data types, and well-designed database architecture for improving SQL performance.

1. USE CASE
SCALABILITY
&
PERFORMANCE
Presented by: RABIA
Roll no: 04

2.  use case in SQL refers to a scenario where a specific query or
operation is used to solve a particular problem or achieve a
certain goal. Here are some common use cases in SQL
 :1. Data retrieval: Retrieving specific data from a database, such
as fetching all customers from a certain region.
 2. Data manipulation: Inserting, updating, or deleting data in a
database, such as adding a new customer or updating an
existing order.
 3. Data analysis: Performing calculations and aggregations on
data, such as finding the total sales for a quarter or the average
order value.
USE CASE

3. USE CASE EXPRESSION

5. CASE STATEMENT

7.  Scalability in SQL refers to the ability of a database to handle
increasing amounts of data and user traffic without a decrease in
performance. Here are some strategies for achieving scalability in
SQL
 :1. Indexing: Creating indexes on frequently used columns can
significantly improve query performance.
 2. Partitioning: Dividing large tables into smaller, more manageable
pieces can improve performance and reduce storage requirements.
 3. Caching: Storing frequently accessed data in memory can reduce
the number of database queries and improve performance
 .4. Load Balancing: Distributing incoming traffic across multiple
servers can improve responsiveness and reduce the risk of
overload.5. Data Sharding: Splitting large datasets acro
SCALABILITY

8.  Performance in SQL refers to the efficiency and speed of
executing queries and operations on a database. Here are
some key factors that impact performance in SQL
 :1. Query Optimization: Well-optimized queries can
significantly improve performance.
 2. Indexing: Creating appropriate indexes can reduce query
execution time.
 3. Data Types: Using suitable data types can improve
storage efficiency and query performance.
 4. Database Design: A well-designed database schema can
reduce data redundancy and improve query performance.
PERFORMANCE

9. THANK YOU!