| 1 |
Operational systems are designed to support high-volume transaction processing. |
Data warehousing systems are typically designed to support high-volume analytical processing (i.e., OLAP). |
| 2 |
Operational systems are usually concerned with current data |
Data warehousing systems are usually concerned with historical data. |
| 3 |
Data within operational systems are mainly updated regularly according to need. |
Non-volatile, new data may be added regularly. Once Added rarely changed. |
| 4 |
It is designed for real-time business dealing and processes. |
It is designed for analysis of business measures by subject area, categories, and attributes. |
| 5 |
It is optimized for a simple set of transactions, generally adding or retrieving a single row at a time per table. |
It is optimized for extent loads and high, complex, unpredictable queries that access many rows per table. |
| 6 |
It is optimized for validation of incoming information during transactions, uses validation data tables. |
Loaded with consistent, valid information, requires no real-time validation. |
| 7 |
It supports thousands of concurrent clients. |
It supports a few concurrent clients relative to OLTP. |
| 8 |
Operational systems are widely process-oriented. |
Data warehousing systems are widely subject-oriented |
| 9 |
Operational systems are usually optimized to perform fast inserts and updates of associatively small volumes of data. |
Data warehousing systems are usually optimized to perform fast retrievals of relatively high volumes of data. |
| 10 |
Data In |
Data Out |
| 11 |
Less Number of data accessed. |
Large Number of data accessed. |
| 12 |
Relational databases are created for on-line transactional Processing (OLTP) |
Data Warehouse designed for on-line Analytical Processing (OLAP) |
