---
title: "RDBMS vs DW database; DW terms"
subject: "Data Mining"
chapter: 1
key: "semester_7/data_mining:rdbms-vs-dw-terms"
source: https://www.bctengineer.com/q/semester_7/data_mining/rdbms-vs-dw-terms
site: BCT Engineering
---

# RDBMS vs DW database; DW terms

Data Mining · Chapter 1: Introduction

- Asked in: year not given, 3+5 marks
- Video: [Database vs Data Warehouse vs Data Lake | What is the Difference?](https://www.youtube.com/watch?v=-bSkREem8dM) — Alex The Analyst
- Answer checked by Claude Opus 5.5 (high effort) on 2026-10-14
- Page: https://www.bctengineer.com/q/semester_7/data_mining/rdbms-vs-dw-terms
- Whole chapter: https://www.bctengineer.com/md/semester_7/data_mining/chapter-1

## Question

Write down the differences between RDBMS database with data warehouse database. Explain with example, the terms data mart, metadata, slicing, dicing and drilling.

## Short answer

RDBMS supports OLTP with current, normalized data; a data warehouse supports OLAP with historical, denormalized data. A data mart is a departmental subset (e.g., sales). Metadata describes data (e.g., source mapping). Slicing selects one dimension value (time=2025). Dicing selects ranges on multiple dimensions. Drilling moves from summary to detail (year→quarter).

## Answer

#### RDBMS database vs data warehouse database
| Point | RDBMS (operational) | Data warehouse |
|---|---|---|
| Purpose | Run daily operations (OLTP) | Support analysis and decisions (OLAP) |
| Data | Current, detailed | Historical, integrated, summarised |
| Design | Normalised, ER model | Denormalised, star/snowflake |
| Operations | Insert, update, delete | Bulk load, read-mostly |
| Orientation | Application | Subject |
| Query | Simple and predefined | Complex and ad hoc |
| Size | Small to medium | Very large |

#### Terms
- **Data mart**: a department-level subset of the warehouse focused on one subject. Example: a sales data mart with only sales facts and customer, item and time dimensions.
- **Metadata**: data about data: schema, source mapping, transformation rules, lineage. Example: "`amt` = bill total in NPR, from POS table, loaded nightly".
- **Slicing**: selecting one value for one dimension of the cube, giving a sub-cube. Example: choose time = 2025 from a (time, item, location) cube to get an (item, location) table.
- **Dicing**: selecting ranges/values on two or more dimensions to get a smaller sub-cube. Example: time = Q1 or Q2, location = Kathmandu or Pokhara, item = mobile.
- **Drilling**: moving from summary to detail (drill-down), e.g. year → quarter → month; the reverse is roll-up (drill-up), e.g. city → country.

## Check yourself

The answer's key points as multiple-choice questions, with the correct option and why.

1. How is the database design of an RDBMS different from a warehouse?
   A. RDBMS is denormalised star schema; warehouse is normalised ER
   B. RDBMS is normalised ER; warehouse is denormalised star/snowflake
   C. Both are identical
   D. RDBMS uses no tables
   Answer: B. Operational DBs are normalised; warehouses use denormalised star or snowflake designs. (Key point: Design difference)

2. What is slicing in a (time, item, location) cube?
   A. Choosing one value for one dimension, e.g. time = 2025, giving an (item, location) sub-cube
   B. Moving from year to month
   C. Choosing ranges on three dimensions
   D. Rotating the cube axes
   Answer: A. Slice fixes a single value on one dimension. (Key point: Slicing)

3. How does dicing differ from slicing?
   A. Dicing selects values on two or more dimensions to get a smaller sub-cube
   B. Dicing removes all dimensions
   C. Dicing moves up the hierarchy
   D. Dicing is identical to slicing
   Answer: A. Dice selects ranges or values on two or more dimensions. (Key point: Dicing)

4. Which is a drill-down example?
   A. City to country
   B. Year to quarter to month
   C. Selecting time = 2025
   D. Naming a data mart
   Answer: B. Drill-down moves from summary to detail; city to country is roll-up. (Key point: Drilling)

## Related in Data Mining

- [Data warehouse vs database](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Adw-vs-database) — Data Mining, chapter 1
- [Data warehouse, data mart and need](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Awarehouse-datamart-need) — Data Mining, chapter 1
- [Virtual warehouse, data mart, enterprise warehouse](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Avirtual-data-mart-enterprise-warehouse) — Data Mining, chapter 1
- [DW characteristics vs operational database](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Adw-characteristics-vs-operational) — Data Mining, chapter 1
- [DMQL; transactional vs warehouse database](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Admql-transactional-vs-dw) — Data Mining, chapter 2
- [Short note: Data marts and metadata](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Adatamart-metadata-note) — Data Mining, chapter 1

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