Difference between Operational Database(OLTP) and Data Warehouse/(OLAP).

 Difference between Operational Database(OLTP) and Data Warehouse/(OLAP).

• The Operational Database is the source of information for the data warehouse. It includes detailed information used to run the day-to-day operations of the business.

The data frequently changes as updates are made and reflect the current value of the last transactions.

• Operational Database Management Systems also called OLTP (Online Transactions Processing Databases), are used to manage dynamic data in real-time.

• Data Warehouse Systems serve users or knowledge workers in the purpose of data analysis and decision-making. Such systems can organize and present information in specific formats to accommodate the diverse needs of various users. These systems are called as Online-Analytical Processing (OLAP) Systems.

Operational Database(OLTP) 

• Operational systems are designed to support high-volume transaction processing.

• Operational systems are usually concerned with current data.

• Data within operational systems are mainly updated regularly according to need.

• It is designed for real-time business dealing and processes.

• 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 validation of incoming information during transactions, uses validation data tables.

• It supports thousands of concurrent clients.

• Operational systems are widely process-oriented.

• Operational systems are usually optimized to perform fast inserts and updates of associatively small volumes of data.

• Data In

• Less Number of data accessed.

• Relational databases are created for online transactional processing (OLTP).

 Data Warehouse/(OLAP)

• Data warehousing systems are typically designed to support high-volume analytical processing (i.e., OLAP).

• Data warehousing systems are usually concerned with historical data.

• Non-volatile, new data may be added regularly. Once Added rarely changed.

• It is designed for the analysis of business measures by subject area, categories, and attributes.

• It is optimized for extensive loads and high, complex, unpredictable queries that access many rows per table.

• Loaded with consistent, valid information, requires no real-time validation.

• It supports a few concurrent clients relative to OLTP.

• Data warehousing systems are widely subject-oriented.

• Data warehousing systems are usually optimized to perform fast retrievals of relatively high volumes of data.

• Data Out

• A large Number of data accessed.

• Data Warehouse designed for on-line Analytical Processing (OLAP).