Data Warehouse Architectures and Roles of Its Components

Comparison of single-tier, two-tier and three-tier architectures

Basis	Single-tier	Two-tier	Three-tier
Structure	One layer: the warehouse and the operational data are kept together; users query it directly	Two layers: a data source/warehouse (server) layer and a client layer, usually with data marts and a staging area	Three layers: bottom (warehouse DB server + ETL), middle (OLAP server), top (front-end tools)
Aim	Minimize the amount of stored data (remove redundancy)	Separate analysis from operational systems	Best separation and scalability
Performance	Poor: analytical queries slow down operational work	Better, but limited as the number of users grows	Best: OLAP server handles heavy analysis
Scalability	Very low	Limited (connectivity and network bottlenecks)	High
Complexity and cost	Lowest	Moderate	Highest
Use today	Rare, not suitable for real warehousing	Small or departmental warehouses	Most common in enterprises

Three-tier architecture in more detail

Top tier:     Front-end tools   (query, reports, dashboards, OLAP clients, data mining)
                    |
Middle tier:  OLAP server       (ROLAP / MOLAP / HOLAP)
                    |
Bottom tier:  Data warehouse DB  <--  ETL  <--  Operational sources + Metadata repository

Roles of components in decision-making

Component	Role
DBMS	Stores and manages warehouse data; provides indexing, partitioning, storage, security and efficient query processing for large volumes
ETL tools	Extract data from sources, clean and transform it, and load it into the warehouse, giving decision-makers accurate, consistent and integrated data
Metadata repository	Holds data about data: schema, mappings, lineage, business definitions, load history. It helps users understand and trust the data and helps administrators maintain it
Query and reporting tools	Let users run queries, produce reports and dashboards, perform OLAP analysis and data mining, so information reaches managers in usable form

How they support decisions

ETL supplies clean and consistent data; the DBMS stores it for fast access; metadata explains it; and query/reporting/OLAP tools let managers see trends, compare periods and drill into causes. Together they turn operational data into decision-ready information.

Conclusion

Single-tier is simple but impractical, two-tier is adequate for small systems, and three-tier is the preferred architecture for scalable, high-performance decision support.