T2.D->T1.B

T1.A->T1.B

T2.D->T2.E

T1.A->T2.D

T1.C->T2.D

T2.E->T1.A

A specific dimension is removed from the cube

The hierarchy level moves from summary (e.g., year) to detail (e.g., month or day)

The analyst views data at a more detailed level of granularity

Data is aggregated into higher levels

The schema design resembles a star, with dimensions radiating from a fact table

It is mainly used for online transaction processing (OLTP)

It has a central fact table connected to multiple dimension tables

It is commonly used in data warehousing for analytical queries

It only uses normalized tables to reduce redundancy

Contains quantitative data (measures) such as sales amount or quantity sold

Usually has no numeric columns

Typically stores large volumes of data

Contains foreign keys referencing dimension tables

Contains only descriptive attributes

To manage employee attendance records in real time

To handle daily financial transactions

To combine data from multiple operational and external sources

To support enterprise-wide data analysis and reporting

Data is extracted from various sources and transformed before being loaded into a data warehouse.

ETL ensures that data is cleaned and standardized for analysis.

ETL is used  for interactive querying of data warehouses.

ETL is only used in operational databases, not in data warehouses.

A LEFT JOIN followed by a WHERE condition on the right-hand table’s column can behave like an INNER JOIN.

The logical order of execution is: FROM → WHERE → GROUP BY → HAVING → SELECT

HAVING can be used without GROUP BY, so it is always interchangeable with WHERE

GROUP BY happens before WHERE.