- Year not given · 3+5 marks
RDBMS vs DW database; DW terms
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).
Write down the differences between RDBMS database with data warehouse database. Explain with example, the terms data mart, metadata, slicing, dicing and drilling.
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.
✓ Answer checked by Claude Opus 5.5 (high effort)
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