Components of a Data Warehouse and Their Functions

A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data that supports management's decision-making.

Architecture

Operational DBs, files,    ETL / Staging area            Warehouse layer          Front-end tools
external sources           (extract, clean,              (DW database,            (query, reports,
                            transform, load)              data marts)              OLAP, mining)
   [Source 1] \                                                                  
   [Source 2] ---> [ Extraction -> Cleaning -> Transformation -> Load ] ---> [ Data Warehouse ] ---> [ Users ]
   [Source 3] /                         ^                                   ^
                                        |______ [ Metadata repository ] ____|

Major components

Component	Role
Data sources	Operational databases, flat files, external data feeding the warehouse
ETL / staging area	Extracts, cleans, transforms and loads data
Data warehouse database	Central integrated store, usually on a relational DBMS or multidimensional server
Data marts	Subject-specific subsets (sales, finance)
Metadata repository	Data about the data: schema, mappings, rules, lineage
Query, reporting and OLAP tools	Let users analyse and view the data
Management tools	Scheduling, security, backup, monitoring

Functions in the warehouse environment

1. Data extraction: Pulls the required data from heterogeneous sources, either fully or incrementally (only changes), without disturbing operational systems.
2. Data cleaning: Detects and fixes errors: missing values, duplicates, wrong formats, inconsistent codes. This makes the warehouse data reliable.
3. Data transformation: Converts data into one consistent format: unit and format conversion, key mapping, aggregation, derived fields, and applying business rules.
4. Metadata management: Stores and maintains technical metadata (schema, mappings, ETL logic), business metadata (definitions, ownership) and operational metadata (load times, history). It helps users find and trust data and supports lineage.
5. Reporting: Gives users standard and ad hoc reports, dashboards, OLAP analysis and data mining outputs that turn warehouse data into decisions.

Conclusion

ETL fills the warehouse with clean, consistent data, metadata describes and controls it, and query/reporting tools turn it into information for decision-making.