Techtalks: Traveltech

Relational Database (RDBMS)

• RDBMS is a DBMS that enables the organization of aggregated data.
• Relational DBMS is the most widely used database like Oracle, SQL Server. It is characterized by handling data in tables with basic keys that make it possible to identify the rows in each table separately. And an external key that allows you to associate the table with other tables.

OLAP (Online Analytical Processing)

• OLAP is a collection of software tools that provide information to business decision-makers.
• OLAP usually works on a standardized database to facilitate OLAP, not OLTP. These databases are often referred to as data warehouses.
• You can check the comparison and key differences between OLAP vs OLTP here

OLAP (Online Analytical Processing) databases and relational databases are both types of database systems, but they serve different purposes and have distinct characteristics.

1. Data Model:

   • Relational Database: Relational databases are based on the relational model, which organizes data into tables with rows and columns. Each table has a unique key, and relationships between tables are established using foreign keys.

   • OLAP Database: OLAP databases use a multidimensional data model. Instead of tables, they organize data into dimensions and measures. Dimensions represent the characteristics or categories of data, and measures represent the data values.

2. Purpose:

   • Relational Database: Relational databases are designed for transactional processing (OLTP - Online Transaction Processing). They are optimized for efficient insertion, updating, and retrieval of individual records.

   • OLAP Database: OLAP databases are designed for analytical processing (OLAP - Online Analytical Processing). They are optimized for complex queries and aggregations on large datasets to support business intelligence and decision-making.

3. Query Performance:

   • Relational Database: Relational databases excel in handling simple queries involving individual records or small sets of records. They may struggle with complex analytical queries or aggregations on large datasets.

   • OLAP Database: OLAP databases are specifically designed for complex queries and aggregations. They can efficiently handle multidimensional queries, making them suitable for analytical tasks.

4. Data Aggregation:

   • Relational Database: Aggregations in relational databases can be computationally expensive, especially when dealing with large datasets, as they involve scanning and summarizing numerous records.

   • OLAP Database: OLAP databases are optimized for data aggregation. They store pre-aggregated data at different levels of granularity, allowing for faster retrieval of summarized information.

5. Schema:

   • Relational Database: Relational databases use a schema with predefined tables and relationships. The schema is typically normalized to eliminate redundancy.

   • OLAP Database: OLAP databases often use a star or snowflake schema. Data is organized into a central fact table (containing measures) surrounded by dimension tables (containing attributes). This allows for efficient querying and aggregation along dimensions.

In summary, while both OLAP and relational databases store and manage data, their design and optimization are geared toward different types of applications. Relational databases are well-suited for transactional processing, while OLAP databases are designed to efficiently handle complex analytical queries and aggregations for business intelligence purposes