---
title: "Differentiate OLTP and OLAP"
subject: "Information System"
chapter: 4
key: "semester_8/information_system:oltp-vs-olap"
source: https://www.bctengineer.com/q/semester_8/information_system/oltp-vs-olap
site: BCT Engineering
---

# Differentiate OLTP and OLAP

Information System · Chapter 4: Decision support and Intelligent systems

- Asked in: 2074 Bhadra, 4 marks; 2073 Bhadra, 4 marks; 2072 Magh, 5 marks; 2072 Asoj, 5 marks; 2070 Bhadra, 4 marks (asked 5 times)
- 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_8/information_system/oltp-vs-olap
- Whole chapter: https://www.bctengineer.com/md/semester_8/information_system/chapter-4

## Question

Differentiate between OLTP and OLAP.

## Short answer

OLTP handles day-to-day transactions like banking deposits with fast, normalized operations on current data. OLAP supports decision making by analyzing large volumes of historical, summarized data from a data warehouse using complex queries and multidimensional operations.

## Answer

**OLTP (Online Transaction Processing)** handles day-to-day transactions (insert, update, delete) such as bank deposits and ticket booking. **OLAP (Online Analytical Processing)** analyses large volumes of historical data from a data warehouse for decision making, using multidimensional operations (roll-up, drill-down, slice, dice).

| Basis | OLTP | OLAP |
|---|---|---|
| Purpose | Run daily operations | Analysis and decision support |
| Data | Current, detailed | Historical, summarised, integrated |
| Source | Operational databases | Data warehouse / data marts |
| Operations | Short insert, update, delete, simple reads | Complex queries, aggregation, read mostly |
| Design | Normalised (ER model) | Denormalised (star/snowflake schema) |
| Users | Clerks, operators, customers | Managers, analysts, executives |
| Size | MB to GB | GB to TB or more |
| Response | Milliseconds, very fast | Seconds to minutes |
| Transactions | Many, small | Few, large |
| Availability and concurrency | Very high availability; strict concurrency control and recovery | Mostly read-only, so concurrent updates are less of a concern |
| Example | ATM withdrawal | Sales trend report by region and year |

## Check yourself

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

1. What is OLTP used for?
   A. Analysing years of historical data for decisions
   B. Storing summary data for managers only
   C. Mining patterns
   D. Running day-to-day transactions such as bank deposits and ticket booking
   Answer: D. OLTP handles daily operations; OLAP supports analysis. (Key point: Purpose)

2. Which design is typical of OLAP?
   A. Denormalised star/snowflake schema
   B. Highly normalised ER model
   C. No schema at all
   D. Flat text files only
   Answer: A. OLTP is normalised; OLAP is denormalised for fast aggregation. (Key point: Data design)

3. Which describes OLAP data?
   A. Current, detailed operational data
   B. Historical, summarised and integrated from a data warehouse
   C. Only a few rows
   D. Only real-time sensor streams
   Answer: B. OLAP uses historical, summarised data from warehouses or marts. (Key point: Data)

4. Which pairing is correct?
   A. OLTP: managers with large queries; OLAP: clerks with small ones
   B. Both are used by clerks only
   C. OLTP: clerks with many small transactions; OLAP: managers with complex aggregation queries
   D. OLAP has millisecond inserts
   Answer: C. OLTP has many small transactions by clerks; OLAP has fewer complex read queries by analysts. (Key point: Users and operations)

5. Which is an OLAP example?
   A. ATM withdrawal
   B. Ticket booking
   C. Bank deposit
   D. Sales trend report by region and year
   Answer: D. Trend reports are analytical; withdrawals are transactions. (Key point: Example)

## Related in other subjects

- [OLAP vs OLTP; data cube, operations](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Aolap-oltp-data-cube-fact-dimension) — Data Mining, chapter 2
- [OLAP vs OLTP; schema, fact, dimension](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Aolap-oltp-schema-fact-dimension) — Data Mining, chapter 2
- [OLAP vs OLTP; schema modeling; star DMQL](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Aolap-oltp-schema-star-dmql) — Data Mining, chapter 2
- [OLAP vs OLTP; OLAP operations, fact, dimension](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Aolap-oltp-operations-fact-dimension) — Data Mining, chapter 2