SAP HANA Dynamic Tiering

The dynamic tiering option in SAP HANA SPS09 gives the ability to keep the data in either memory or on the disk in a columnar format. Data is not duplicated. Dynamic tiering option helps users to choose memory for hot data and disk for warm data, helping to strike the right price/performance balance. To do so, you define the table as “extended table” using an SQL CREATE statement. These tables are like any other SAP HANA tables except for the fact that they are created on the disk and not in memory. From the application developer point of view, these tables can be queried and modified using standard SQL statements, like any other SAP HANA tables. You can join extended tables with in-memory tables and at any point of time, you can convert extended tables into in-memory tables and vice versa, with an SQL ALTER statement.

HOT---->Warm

Dynamic tiering also works with multitenant database containers and you can configure extended storage for each tenant.

Data Virtualization overview

 Data Virtualization:

Data virtualization is synonymous with information agility - it delivers a simplified, unified, and integrated view of trusted business data in real time or near real time as needed by the consuming applications, processes, analytics, or business users. Data virtualization integrates data from disparate sources, locations and formats, without replicating the data,  to create a single "virtual" data layer that delivers unified data services to support multiple applications and users. The result is faster access to all data, less replication and cost, more agility to change.

Data virtualization is modern data integration. It performs many of the same transformation and quality functions as traditional data integration (Extract-Transform-Load (ETL), data replication, data federation, Enterprise Service Bus (ESB), etc.) but leveraging modern technology to deliver real-time data integration at lower cost, with more speed and agility. It can replace traditional data integration and reduce the need for replicated data marts and data warehouses in many cases, but not entirely.

Data virtualization is also an abstraction layer and a data services layer. In this sense it is highly complementary to use between original and derived data sources, ETL, ESB and other middleware, applications, and devices, whether on-premise or cloud-based, to provide flexibility between layers of information and business technology.