--- title: "Data warehouse, data mart and need" subject: "Data Mining" chapter: 1 key: "semester_7/data_mining:warehouse-datamart-need" source: https://www.bctengineer.com/q/semester_7/data_mining/warehouse-datamart-need site: BCT Engineering --- # Data warehouse, data mart and need Data Mining · Chapter 1: Introduction - Asked in: 2066 Bhadra, 2+2+6 marks; year not given, 2+2+6 marks - Video: [Difference Between Data Warehouse, Data Mart & Data Lake](https://www.youtube.com/watch?v=jnfMA2YVRtw) — 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/warehouse-datamart-need - Whole chapter: https://www.bctengineer.com/md/semester_7/data_mining/chapter-1 ## Question What is a data warehouse and data mart? Why do organizations need a warehouse separately though they have a transactional database system? Explain with its application. ## Short answer A data warehouse is a subject-oriented, integrated, time-variant, non-volatile collection supporting decisions; a data mart is a department-level subset focused on one subject. Organizations need a separate warehouse because OLTP systems are tuned for transactions, keep only current data, lack integration and history, and cannot handle heavy analytical queries. Applications include retail basket analysis, banking risk, and telecom churn analysis. ## Answer #### Data warehouse A **data warehouse** is a subject-oriented, integrated, time-variant and non-volatile collection of data that supports management's decision making (W. H. Inmon). It stores historical data from many sources in one place for analysis. #### Data mart A **data mart** is a small, department-level subset of the warehouse (or a small standalone warehouse) that focuses on one subject, such as sales or finance. It is cheaper and faster to build, and gives its users faster queries. #### Why a separate warehouse when a transactional database exists? 1. **Performance**: OLTP databases are tuned for short transactions. Heavy analytical queries (large scans and aggregates) would slow down daily operations, and their locks would block updates. 2. **Different data**: decision making needs historical data (years), while operational databases keep only current data. 3. **Integration**: analysis needs data from many systems, with consistent names, formats and codes. Operational systems are separate and inconsistent. 4. **Data quality**: the warehouse holds cleaned, validated data. 5. **Different structure**: the warehouse uses a denormalised star schema for fast aggregation; OLTP uses normalised tables. 6. **Different functions**: OLAP needs multidimensional views, which operational DBMSs do not provide. #### Applications - **Retail**: market-basket analysis, stock planning, seasonal sales trends. - **Banking**: customer profitability, credit risk, fraud analysis. - **Telecom**: churn analysis, call-pattern and billing analysis. - **Healthcare**: patient history, disease trends and hospital resource planning. - **Government**: census, tax and e-governance reporting. ## Check yourself The answer's key points as multiple-choice questions, with the correct option and why. 1. How does a data mart differ from a data warehouse? A. It stores only current transactions B. It is a larger enterprise-wide store C. It has no historical data by definition D. It is a small department-level subset focused on one subject Answer: D. A data mart is a department-level subset (e.g. sales), cheaper and faster to build and query. (Key point: Data mart) 2. Why can heavy analytical queries not simply run on the OLTP database? A. Analysis needs less data than OLTP holds B. They would slow daily operations and locking would block updates C. OLTP databases cannot store numbers D. OLTP systems do not have tables Answer: B. OLTP is tuned for short transactions; big scans and aggregates hurt operations and locks block updates. (Key point: Performance reason) 3. Why is a separate warehouse needed regarding the type of data? A. Warehouses store only current data B. Operational data is always denormalised C. Operational databases store too much history D. Operational databases keep only current data, while decisions need years of history Answer: D. Decision making needs historical data, which operational databases do not keep. (Key point: Historical data need) 4. How does warehouse structure differ from OLTP structure? A. Both use the same normalised tables B. Warehouse is normalised, OLTP is denormalised C. Warehouse uses denormalised star schema for fast aggregation; OLTP uses normalised tables D. Warehouse has no schema Answer: C. Star schemas favour fast aggregation; OLTP normalises to avoid update anomalies. (Key point: Warehouse structure) ## Related in Data Mining - [DMQL; transactional vs warehouse database](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Admql-transactional-vs-dw) — Data Mining, chapter 2 - [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 - [Data warehouse vs database](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Adw-vs-database) — Data Mining, chapter 1 - [Define data mart](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Adefine-data-mart) — 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 - [National data warehouse process architecture](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Anepal-national-data-warehouse) — Data Mining, chapter 1 ## Related in other subjects - [Why organizations need IS; IS vs DBMS](https://www.bctengineer.com/question.md?key=semester_8%2Finformation_system%3Awhy-need-is-vs-dbms) — Information System, chapter 1 - [Data warehouse and data mining](https://www.bctengineer.com/question.md?key=semester_8%2Finformation_system%3Adata-warehouse-data-mining) — Information System, chapter 4, asked 2× - [Hybrid data in big data warehouse](https://www.bctengineer.com/question.md?key=semester_8%2Fbig_data_technologies%3Ahybrid-data-warehouse) — Big Data Technologies, chapter 1 - [Need for data compression](https://www.bctengineer.com/question.md?key=semester_8%2Fmultimedia_system%3Aneed-for-data-compression) — Multimedia System, chapter 5