OLTP databases store and manage data related to the day-to-day operations of a system or company. However, OLTP focuses on transaction-oriented tasks.

OLTP typically handles query processing (inserting, updating, deleting data in a database) and maintaining data integrity and validity while processing a large number of transactions simultaneously.

OLTP and OLAP
OLTP and oltp database  are two complementary systems. OLTP handles the day-to-day transactions while OLAP helps in analyzing the processed data.

Following are some key differences between OLTP and OLAP:

OLAP
OLTP databases manage the day-to-day affairs of an organization. For business analysis, including planning, budgeting, forecasting, data mining, and more.
The focus is on fast and efficient query processing and ensuring data integrity in a multi-access environment. The focus is on response time for complex queries executing complex queries on large volumes of historical data aggregated from multiple rows.
Handles many standard and simple queries (such as insert, delete, and update statements). Handles a small number of queries, but they are complex and involve large amounts of data (such as aggregation queries). Mainly use the select statement.
End users are clerks, tellers, DBAs, etc. End users are knowledge workers, business analysts, managers, etc.
Data is stored in 3NF (Third Normal Form). Data is denormalized to improve query performance.
Does not take up much space. Usually takes up a lot of space.
Perform data backups on a regular basis. Data backups are rare.
Use the ER model schema. Use snowflake mode.
Use a traditional DBMS. Use a unique data warehouse that integrates disparate sources.
market oriented. Be customer-oriented.
Designed for real-time business operations. Designed for analyzing business metrics by category and attribute.
Support thousands of users. Support hundreds of users.
Note: 3NF - Third Normal Form - is a database schema design for relational databases. 3NF uses normalization principles to reduce data duplication, prevent data anomalies, ensure data integrity, and simplify data management.