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HANA Overview and Capabilities. Dr. Bjarne Berg . Why In-Memory Processing?. Focus. Technology. 1990. 2012. Improvement. 0.05 MIPS/$. 304.17 MIPS/$. 6083 x. CPU. 0.02 MB/$. 52.27 MB/$. 2614x. Memory. 2 16. 2 64. 2 48 x. Addressable Memory. 100 Mbps. 100 Gbps. 1000 x.
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HANA Overview and Capabilities Dr. Bjarne Berg
Why In-Memory Processing? Focus Technology 1990 2012 Improvement 0.05 MIPS/$ 304.17 MIPS/$ 6083x CPU 0.02 MB/$ 52.27 MB/$ 2614x Memory 216 264 248x Addressable Memory 100 Mbps 100 Gbps 1000 x Network Speed 5 MBPS 620 MBPS 124x Disk Data Transfer Source: 1990 numbers SAP AG, 2012 numbers, Dr. Berg Source: BI Survey of 534 BI professionals, InformationWeek, 2010 Disk speed is growing slower than all other hardware components, while the need for speed is increasing.
In Memory Processing — General Highlights — BWA BWA = SAP BW Accelerator
SAP HANA — In Memory Options • SAP HANA is sold as an in-memory appliance. This means that both Software and Hardware are included from the vendors • Currently you can buy SAP HANA solutions from Cisco, Dell, Fujitsu, IBM, and Hewlett-Packard • SAP HANA currently indexes the data from a variety of sources, including ERP and BW and store the result on a dedicated server • The future of SAP HANA is to replace the databases of ERP and BW and run these on the in-memory platform Source SAP AG,2011 SAP HANA has the potential to radically change the way databases operate and make systems dramatically faster.
Looking Inside SAP HANA — In-Memory Computing Engine (IMCE) AAAA Persistence Layer Disk Storage Data Volumes Page Mgmt. Session Manager Logger Log Volumes Replication Server Load Controller Relational Engine -Row Store -Column Store Metadata Manager MDX SQL Parser Authorization Manager SQL Script Transaction Manager Calculation Engine BusinessObjects Data Services Inside the Computing Engine of SAP HANA we have many different components that manage the access and storage of the data. This include MDX and SQL access, as well as Load Controller (LC) and the Replication Server.
As we can see, there are only 7 unique states and 3 unique customer classes in the data. This allows SAP HANA to compress this data set significantly By including the Row ID in the column-based index in SAP HANA, the “ownership” of the values in the index can still be mapped back to the record Row- vs. Column-Based Indexing (cont.) Column-based indexes on fields with repeated values often leads to better compression ratios and thereby lower size of the indexes (as we can see, there are few values repeated in the rows).
SAP HANA — Virtual Marts and Applications • Virtual data marts and new applications were built that run on SAP NetWeaver BW, which is again enabled by SAP HANA in-memory processing Applications developed by SAP Planning & consolidation Customer revenue performance mgmt Predictive segmentation & targeting Trade promotion management Merchandise & assortment planning Sales & operations planning (SOP) Demand signal repository Profitability analysis Dynamic cash management Strategic workforce planning Smart meter analytics (power companies) Enterprise Data Warehouse – SAP BW ERP HANA (in-the works) BI Solutions Virtual Data Marts Virtual Data Marts Database Applications Virtual Data Marts Virtual Data Marts Databases Files This provides much tighter integration with the source system (less data latency) and much faster query response time for high-volume analysis
SAP HANA — Loading the Application and Performance You can load the application based on the logs in the source system, ETL-based (Extract Transform and Load) loads, and SAP trigger-based replication ToolPurpose BusinessObjects Data Services 4.0 – ETL-based replication Sybase replication server &Load Controller – Log-based replication SAP Landscape Transformation (LT) – Trigger-based replication Log based replication is possible on IBM DB 2 LUW/UDB, MSFT SQL Server Enterprise Edition, Oracle Enterprise Edition, and Sybase ASE
The table definition inside HANA
Open a HANA table
Browsing data in a HANA table
Creating a New Products HANA Table
Defining a new Sales HANA table
Replicating Product file for HANA data load
Replicated Product file for HANA dataload