SAP HANA has use in-memory database
SAP HANA (HochleistungsANalyseAnwendung or High-performance ANalytic Application) is an in-memory, column-oriented, relational database management system developed and marketed by SAP SE. Its primary function as the software running a database server is to store and retrieve data as requested by the applications. In addition, it performs advanced analytics (predictive analytics, spatial data processing, text analytics, text search, streaming analytics, graph data processing) and includes extract, transform, load (ETL) capabilities as well as an application server.Architecture
Overview
The key distinctions between HANA and previous generation SAP systems are that it is a column-oriented, in-memory database, that combines OLAP and OLTP operations into a single system; thus in general SAP HANA is an "online transaction and analytical processing" (OLTAP) system, also known as a hybrid transactional/analytical processing (HTAP). Storing data in main memory rather than on disk provides faster data access and, by extension, faster querying and processing. While storing data in-memory confers performance advantages, it is a more costly form of data storage. Observing data access patterns, up to 85% of data in an enterprise system may be infrequently accessed therefore it can be cost-effective to store frequently accessed, or "hot", data in-memory while the less frequently accessed "warm" data is stored on disk, an approach SAP have termed "Dynamic tiering".Column-oriented systems store all data for a single column in the same location, rather than storing all data for a single row in the same location (row-oriented systems). This can enable performance improvements for OLAP queries on large datasets and allows greater vertical compression of similar types of data in a single column. If the read times for column-stored data is fast enough, consolidated views of the data can be performed on the fly, removing the need for maintaining aggregate views and its associated data redundancy.Although row-oriented systems have traditionally been favored for OLTP, in-memory storage opens techniques to develop hybrid systems suitable for both OLAP and OLTP capabilities, removing the need to maintain separate systems for OLTP and OLAP operations.