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SAP HANA Database

[email protected]

College of Business Administration, Department of Business Information Systems

Central Michigan University

Mount Pleasant, Michigan 48858



“The SAP HANA (High Performance analytical appliance) database is positioned as the core of the SAP HANA Appliance to support complex business analytical processes in combination with transnationally consistent operational workloads” (Färber et al., 2012).  “When the structure of data seems randomly designed (variety), when the speed of the flow of data is continuously increasing (velocity), when the amount of information is growing each second (volume) and when there is additional information hidden in the data (value), only one solution can be assigned to manage this chaos: big data” (Ciobanu, 01 July 2015).  “SAP has been claiming confidently that its Hana product will quickly steal database market share that took Oracle decades to win” (Doug Henschen, 2012d) .  "Big data is simply an evolution of information management" (McKendrick, 2013). “Hana is a threat to Oracle because SAP is promoting it as an in-memory replacement for the database running Business Warehouse”  (Doug Henschen, 2012a). “SAP has more than 176,000 customers, and roughly 60% of its applications and Business Warehouse instances are believed to be running on Oracle database, so even a small percentage of defections would hit Oracle hard “  (Doug Henschen, 2012b). “On the application services side, SAP announced the general availability of SAP Net Weaver Cloud, which is a Java-based development platform-as-a-service for building, deploying, and managing applications in the cloud. It supports Eclipse and industry-standard tools that will enable the use of Ruby, PHP, and other programming models” (Doug Henschen, 2012c). “The SAP HANA database permits the exchange of application semantics with the underlying data management platform that can be exploited to increase query expressiveness and to reduce the number of individual application-to-database round trips” (Sang Kyun Cha, 2013).


There are many feature in SAP HANA to name few of them are In-Memory Computing, Row and Column Storage, Data Compression, and Multicore functionality.

In-Memory Computing:

“SAP-certified and available as a 4- or 8-socket single-node system with up to 6 TBs of in-memory computing”  ("SGI UV for SAP HANA," 2014). “In-memory technology, such as HANA, is only a first but very important step towards Big Data” (Buhl, 2013) . “The in-memory Business Intelligence incorporates a high-performance by providing important benefits as: accelerate analytics with data modelling changing from mandatory to optional activity, significantly reduce planning cycles by processing calculations in-memory”  (Ivan, 2014). “Hana-powered BI on Demand won't provide real-time analysis of all data, but once data reaches the cloud, the in-memory technology will let you do what-if querying and scenario analysis flexibly and with "speed of thought" response times”  (Doug Henschen, 2011b) .  

Row and Column Storage:

“The column-store format emphasizes the database column as the manipulable element, and is typically used for On-Line Analytical Processing (OLAP) of a subset of a total number of transactions (rows) over a fewer number of data types (columns) that may include aggregations of basic data types”  ("Sap Ag; Patent Application Titled "Hybrid Database Table Stored as Both Row and Column Store" Published Online," 2014). "SAP Hana has one columnar store for both transactions and analytics whereas Oracle has two and is, hence, bloated," (Doug Henschen, 2013a)

Data Compression:

“Knowing the exact storage space requirements of all fields in the actual table scheme allows us to refine the calculation of the compression ratio within the compression algorithm”  (Eichinger, Efros, Karnouskos, & Böhm, 2014). “Hana will get its cost advantage from compression and elimination of redundant storage and management layers” (Doug Henschen, 2011a). “The compressed data volume in SAP HANA is the total size that the table occupies in the main memory of SAP HANA” (Anonymous, 2012).

Multicore Parallelism:

“Today, modern system architectures provide server boards with up to 8 separate CPUs where each CPU has up to 12 separate cores. This tremendous amount of processing power should be exploited as much as possible to achieve the highest possible throughput for transactional and analytical applications”  (Buhl, 2013; F et al., 2012; Dough Henschen, 2010; Doug Henschen, 2011b, 2012b, 2012d, 2012e, 2012f, 2013b, 2013c; Ivan, 2014; Nelson, 2012).  “On the hardware certified today with 40 hyper-threaded Xeon cores per server HANA provides 80 units of parallelization” (Klopp, 2013).

 Research Questions and Objectives:

Research questions are:

1. To identify the difference between traditional database and SAP HANA as database?

2. We are speaking lot about performance, to what extent the performance will improve in real-time?

Research Objectives are:

1. To identify the importance of the SAP –HANA in the current market and where it stands as of today?

2. To examine how the features of SAP-HANA will benefit the organizations in gaining more profits?

Research Methods:

Currently I am undergoing training in  SAP S/4HANA in a Nutshell , Software Development on SAP HANA, Implementation of SAP S/4HANA from Open SAP which is an open source where we can get the insight of how SAP HANA works in real time.

Firstly, by going through peer reviewed journals on secondary data , reading articles from different research libraries and blogs from SAP community network(SCN) where SAP professionals share ideas and knowledge and web sources.

Secondly, I will try to get approval from the clients to whom I have worked with, to get the information from the employees of that organization like how SAP HANA transformed their business and in what way these emerging technology helping the organization and we can get the answers like are they really happy with the way the systems transformed.

Thirdly, I will prepare a questioner and post the questions to the employees who worked in implementations of SAP S/4 HANA (I know several organizations who implemented HANA) the advantage of this step will be we can get the data from the different organizations who worked in real time and the employees will fill the questioner when they get the free time.

Fourthly, I will try to meet at least 10 employees who worked in implementation of SAP S/4 HANA in person to have a healthy conversation with them to know what challenges they faced how they overcome those challenges, how business transformed after successful implementation and how long this implementation took and what are the implications of cost in implementation.

Legal considerations:

I will make sure that the data I have collected will be used only for research purpose and I will make sure that the data will not be misused in any manner. I will follow all the legal rules to make this research possible.

Time Scale:

The research will be conducted over 12 weeks.


I have collected most of the data from the peer reviewed journals, open SAP, SAP Community Network, and Web sources besides this I have spoken to the employees who are currently working SAP HANA to know about its pros and cons.


Anonymous. (2012). Data Compression in the Column Store.  Retrieved from

Buhl, H. U. P. D. (2013). Interview with Martin Petry on "Big Data". Business & Information Systems Engineering, 5(2), 101-102. doi:

Ciobanu, R. B. M. (01 July 2015). In-memory databases and innovations in Business Intelligence.  Retrieved from

Eichinger, F., Efros, P., Karnouskos, S., & Böhm, K. (2014). A time-series compression technique and its application to the smart grid. The VLDB Journal, 24(2), 193-218. doi:10.1007/s00778-014-0368-8

F, F., #228, rber, Cha, S. K., #252, Primsch, r., . . . Lehner, W. (2012). SAP HANA database: data management for modern business applications. SIGMOD Rec., 40(4), 45-51. doi:10.1145/2094114.2094126

Färber, F., Cha, S. K., Primsch, J., Bornhövd, C., Sigg, S., & Lehner, W. (2012). SAP HANA database. ACM SIGMOD Record, 40(4), 45. doi:10.1145/2094114.2094126

Henschen, D. (2010). SAP Appliance: First Step In A Grand Plan. InformationWeek(1286), 24.  Retrieved from

Henschen, D. (2011a). SAP Is Delivering In Critical Areas. InformationWeek(1301), 16.  Retrieved from

Henschen, D. (2011b). SAP, Oracle Need To Get Real About In-Memory. InformationWeek(1315), 20.  Retrieved from

Henschen, D. (2012a). Oracle And SAP On Analytics Collision Course. InformationWeek(1322), 15.  Retrieved from

Henschen, D. (2012b). Oracle Strikes At SAP Hana With TimesTen Database. Informationweek - Online.  Retrieved from

Henschen, D. (2012c). SAP's In-Memory Cloud Platform Set To Take On Oracle And Salesforce. InformationWeek(1349), 14.  Retrieved from

Henschen, D. (2012d). SAP HYPE VS. ORACLE FUD. InformationWeek(1334), 21-25.  Retrieved from

Henschen, D. (2012e). SAP Overhypes Its Hana Sentiment Analysis. Informationweek - Online.  Retrieved from

Henschen, D. (2012f). SAP To Expand Database And Cloud Reach. InformationWeek(1323), 9-10.  Retrieved from

Henschen, D. (2013a). Oracle's Ellison Tries To Outmaneuver SAP Hana. Informationweek - Online.  Retrieved from

Henschen, D. (2013b). SAP Moves Core Applications To Hana In-Memory Platform. Informationweek - Online.  Retrieved from

Henschen, D. (2013c). SAP Vows Hana Is Ready To Run ERP. Informationweek - Online.  Retrieved from

Ivan, M.-L. (2014). Characteristics of In-Memory Business Intelligence. Informatica Economica, 18(3), 17-25.  Retrieved from

Klopp, R. (2013). Massively Parallel Processing on HANA.  Retrieved from

McKendrick, J. (2013). NEW PATHS TO DATA INTEGRATION: Taming Big Data Helps Address Lingering Issues. Database Trends and Applications, 27(1), 4-6,8-10.  Retrieved from

Nelson, F. (2012). Memo To Oracle, SAP: Listen To P&G's Language. InformationWeek(1347), 3-4.  Retrieved from

Sang Kyun Cha, J. P., Christof Bornhövd ,Stefan Sigg,Wolfgang Lehner. (2013). SAP HANA Database - Data Management for Modern

Business Applications.

Sap Ag; Patent Application Titled "Hybrid Database Table Stored as Both Row and Column Store" Published Online. (2014). Computer Weekly News, 1276.  Retrieved from

SGI UV for SAP HANA. (2014). Scientific Computing.  Retrieved from

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