Data management with SAPs in-memory computing engine

We present some architectural and technological insights on SAP's HANA database and derive research challenges for future enterprise application development. The HANA database management system [1] was developed to meet changed requirements of modern business applications. Nowadays, these require fast and complex analytical data processing coupled with traditional transactional data management. Additionally, fast and agile decision processes that take operational data as well as structured and unstructured information into account are the current key driver in enterprises for business success. In conventional system landscapes currently found in enterprises, dedicated systems are used for analytical and transactional data processing. In contrast, HANA follows a more holistic data management approach by integrating OLTP and OLAP functionality in a single system and by adding features beyond traditional database management systems, such as graph or text processing for semi- and unstructured data. While in common three-tier architectures, compute-intensive applications run at the application server layer and data is loaded into the main memory of application servers, enterprise applications developed for or moved to HANA are more tightly integrated with the database. The main principle of application development for HANA is to execute data-intensive computations in the database close to the raw data in order to prevent expensive data movement. This shift in application design poses new challenges to the application developer: in order to utilize HANA efficiently, he has to think differently about how to design his application. We'll address these challenges and present some open questions in this area in the second part of the talk.