A data warehouse is a central data repository that is used for analytics within an organization. These are often referred to as Online Analytical Processing or ‘OLAP’ databases as distinguisehd from ‘OLTP’ or Online Transaction Processing databses.

Instead of running analytical queries directly on an OLTP database many organizations use a process called ETL to ‘Extract’, ‘Transform’, and ‘Load’ data from these other business databases into one common data ‘store’, or ‘warehouse.’ This separation of transactions DBs from analytical DBs is a key distinction to recognize when it comes to designing data-intensive applications.

The key features of a data warehouse include it’s basic architecture, syntax/language, and performance specs. Different databases do different things. The key is to pick the right one(s) for your task, and to organize them effectively into a coherent data strategy.

OLTP (On-line Transaction Processing) is characterized by a large number of short on-line transactions (INSERT, UPDATE, DELETE). The main emphasis for OLTP systems is put on very fast query processing, maintaining data integrity in multi-access environments and an effectiveness measured by number of transactions per second. In OLTP database there is detailed and current data, and schema used to store transactional databases is the entity model (usually 3NF).

OLAP (On-line Analytical Processing) is characterized by relatively low volume of transactions. Queries are often very complex and involve aggregations. For OLAP systems a response time is an effectiveness measure. OLAP applications are widely used by Data Mining techniques. In OLAP database there is aggregated, historical data, stored in multi-dimensional schemas (usually star schema).



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