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

# DW characteristics vs operational database

Data Mining · Chapter 1: Introduction

- Asked in: 2065 Baisakh
- Video: [Lec -8 : OLAP vs OLTP | Difference between OLAP vs OLTP with Real Life Examples](https://www.youtube.com/watch?v=DeL3dgiy6vk) — Gate Smashers
- Answer checked by Claude Opus 5.5 (high effort) on 2026-10-14
- Page: https://www.bctengineer.com/q/semester_7/data_mining/dw-characteristics-vs-operational
- Whole chapter: https://www.bctengineer.com/md/semester_7/data_mining/chapter-1

## Question

Explain the characteristics of a Data Warehouse. How is an operational database different from a Data Warehouse?

## Short answer

A data warehouse is subject-oriented, integrated, time-variant, non-volatile, large-volume, decision-support oriented. Unlike operational databases, it stores historical summarized and detailed data, uses denormalized star schemas, supports complex ad hoc queries, and is refreshed periodically rather than updated transaction by transaction.

## Answer

A **data warehouse** is a subject-oriented, integrated, time-variant and non-volatile collection of data used for decision making.

#### Characteristics
- **Subject-oriented**: organised around major subjects such as customer, product, sales, not around day-to-day operations.
- **Integrated**: data from many sources is made consistent in naming, units, formats and encoding.
- **Time-variant**: data is kept as historical snapshots (5-10 years); every record has a time element.
- **Non-volatile**: data is loaded and read, but not updated or deleted in place; only refreshed periodically.
- **Large volume**: stores years of detailed and summarised data, in GB to TB.
- **Decision-support oriented**: built for queries and analysis, not transactions.

#### Operational database vs data warehouse
| Point | Operational database (OLTP) | Data warehouse |
|---|---|---|
| Purpose | Day-to-day transactions | Analysis and decision support |
| Data | Current, detailed | Historical, summarised and detailed |
| Orientation | Application-oriented | Subject-oriented |
| Update | Frequent insert/update/delete | Periodic bulk load, read-mostly |
| Design | Normalised (ER model) | Denormalised (star/snowflake) |
| Query | Short, simple, predefined | Complex, ad hoc, aggregate |
| Users | Clerks, DBAs (thousands) | Managers, analysts (few) |
| Size | MB to GB | GB to TB |

## Check yourself

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

1. What does non-volatile mean for warehouse data?
   A. Data disappears after a day
   B. Data is updated frequently in place
   C. Data is loaded and read but not updated or deleted in place
   D. Data is stored only in memory
   Answer: C. Warehouse data is not changed in place; it is only refreshed periodically. (Key point: Non-volatile)

2. What does subject-oriented mean?
   A. Organised by user name
   B. Organised around day-to-day operations
   C. Organised around major subjects such as customer, product and sales
   D. Organised by application
   Answer: C. Subject orientation groups data by business subjects, not by operational application. (Key point: Subject-oriented)

3. Which pairing is correct?
   A. OLTP: historical, summarised; DW: current, detailed
   B. OLTP: managers; DW: clerks
   C. OLTP: ad hoc complex queries; DW: short predefined queries
   D. OLTP: normalised with frequent updates; DW: denormalised, periodic bulk load
   Answer: D. OLTP is normalised and updated often; warehouses are denormalised and read-mostly. (Key point: OLTP vs DW)

## 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
- [RDBMS vs DW database; DW terms](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Ardbms-vs-dw-terms) — 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
- [Data warehouse and its characteristics](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Adw-define-characteristics) — Data Mining, chapter 1
- [Data mining vs DW and applications](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Adm-vs-dw-applications) — Data Mining, chapter 1
- [Importance, uses and characteristics of DW](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Adw-importance-uses-characteristics) — Data Mining, chapter 1

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