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Big Data In-Memory Analytics explained by SAP HANA

Studienarbeit 2015 20 Seiten

Informatik - Wirtschaftsinformatik

Leseprobe

Table of contents

Abstract

List of figures

1 Meaning of Big Data

2 Technologies of Big Data

3 Big Data versus Business Intelligence

4 Business Intelligence, Business Intelligence Framework and Data Warehouse

5 In-memory analytics
5.1 Example of implementation: SAP HANA
5.1.1 Technical concepts
5.1.2 SAP HANA and real-time
5.1.3 Solutions of SAP HANA
5.1.4 Review
5.2 Potential of in-memory analytics

References

Abstract

Nowadays, people produce large amounts of data with talking via smartphones, reading e-mails or using platforms to find the appropriate partner. Conventional technologies no longer cope with the increasing amount of data and come to their limits. Therefore new technologies of Big Data are required for data processing to overcome the data flood.

At the beginning, this paper clarifies what Big Data is, the technologies of Big Data, how Big Data differs from Business Intelligence and a distinction is made between Data Warehouse and Business Intelligence. Furthermore, the theory of the Big Data technology in-memory analytics is explained and an implementation of this technology called “SAP HANA” is consulted and reviewed. In conclusion, the potential of in-memory analytics will be classified.

List of figures

Figure 1-1: Use of Big Data in different application areas (BITKOM e.V., 2014)

Figure 3-1: Classification of Big Data and Business Intelligence

Figure 4-1: Performance comparision of different database types (Acker, Gröne, Blockus, & Bange, 2011, pp. 129-136)

Figure 4-2: Buffer concept of SAP HANA (Knötzele, 2013, pp. 381-412)

Figure 4-3: Hype cycle for emerging technologies (Rivera & Van der Meulen, 2014)

1 Meaning of Big Data

Big Data is becoming increasingly important, not only in the world of information technology. The following graphic shows the application areas of Big Data.

illustration not visible in this excerpt

Figure 1-1: Use of Big Data in different application areas (BITKOM e.V., 2014)

Different interpretations and diverse reporting make it difficult to differ between hype and reality of Big Data. Therefore, the question arises – what Big Data exactly is? “Big Data […] describes large volumes of high velocity, complex and variable data that require advanced techniques and technologies to enable the capture, storage, distribution, management, and analysis of the information” (Agarwal, et al., 2012, pp. 9-15).

Further, characteristics of Big Data will be explained to make it possible to assess this technology. According to an article from the Harvard Business Review, Big Data is defined by the three dimensions volume, velocity and variety (McAfee & Brynjolfsson, 2012). Volume stands for data size. For in instance, it is estimated that Walmart collects more than two and a half petabytes of data every hour from its customers transactions – a petabyte is one quadrillion bytes, or the equivalent of about 20 million filing cabinets’ worth of text (McAfee & Brynjolfsson, 2012). The second dimension is velocity and means the speed of data creation. With real-time or nearly real-time reporting the company can realize a competitive advantage, because the company is able to act more flexible than competition. Variety is the last dimension and covers the different data sources. In this context, data sources are affected, which stores structured, unstructured or semi-structured data. Michael Brands a specialist in analyzing of data says: “[…] it is generally acknowledged in modern economy that knowledge is the biggest of asset of companies and most of this knowledge, since it is developed by people, is recorded in unstructured formats” (Zicari, 2012). Therefore Big Data technologies are mainly focused on unstructured data.

2 Technologies of Big Data

To make it possible to efficiently process large amounts of data, new technologies are needed. The disciplines of Big Data are evolving so quickly that businesses need to wade in or risk being left behind (Mitchell, 2014). In the past, emerging technologies have taken years to mature. Now people iterate and drive solutions in a matter of months or even weeks. The following table faces the trends of Big Data. In chapter five the technology in-memory analytics will be explained in more detail.

illustration not visible in this excerpt

Table 1: Big Data technologies (Mitchell, 2014)

3 Big Data versus Business Intelligence

The market research company Gartner Inc. defines Business Intelligence as “[…] an umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance” (Gartner, Inc., 2015). The concepts of Business Intelligence and Big Data are often used in the same context. Therefore, these two concepts are now clearly distinguished from each other.

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Details

Seiten
20
Jahr
2015
ISBN (eBook)
9783656970804
ISBN (Buch)
9783656970811
Dateigröße
685 KB
Sprache
Englisch
Katalognummer
v300886
Institution / Hochschule
Hochschule Ansbach - Hochschule für angewandte Wissenschaften Fachhochschule Ansbach
Note
1,0
Schlagworte
theory data in-memory analytics technology hana

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Titel: Big Data In-Memory Analytics explained by SAP HANA