Rapid Data Access and Reliable Transactions

Stop using yesterday’s data to make today’s decisions. SAP HANA in-memory database can help execute a futuristic analytics with fast-moving transactions and get accurate, up-to-date responses quickly. The in-memory database removes lag between operational and decision support systems which is instrumental to have the real-time data at our fingertips.

Hana Migration

Migration to Hana database can be done in three different ways as shown below:

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  • You Install new system on SAP HANA
  • Either without changing existing solutions (greenfield)
  • Or for transforming existing solution to SAP HANA by performing (selective) data migration
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  • To bring original system on release supported by SAP HANA, you perform update/upgrade (if required)
  • Then you classically migrate your traditional database to SAP HANA
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  • You use database migration option (DMO) that combines upgrade and database migration in one step
  • One process, one tool, one downtime

Transactional & Analytical Data in a Single Database

Easy Business Logic Management

SAP HANA leverages graphical modeling tools and pre-built business function libraries to create multiplex business logic. We can embed our logic into stored procedures and run it inside the database to avoid unnecessary data movement.
The algorithms run quicker in SAP HANA as models take advantage of highly optimized in-memory calculation engines.

Run Several Tenant Databases as One

With SAP HANA multitenant database containers, we can manage multiple databases as one. We can allocate devoted resources for each tenant to improve database performance and lessen costs by backing-up, patching, and recovering all databases at once.
By running multiple tenant databases as one, we can minimize costs, optimize system resources, and simplify database security and maintenance.

Cost-Effectively Manage Large Data Volumes

With SAP HANA dynamic tiering, the often accessed data remains in memory, while seldom accessed data is moved to disk. This lets us cost-effectively manage large data volumes without being limited by memory size. Our applications can access all data independently of where it is stored and we can modify our database storage preferences at any time to meet user expectations.

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