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.
Table of Contents
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
Objectives and Core Topics
The primary objective of this thesis is to provide a comprehensive understanding of Big Data and its associated technologies, with a specific focus on the role and implementation of in-memory analytics. The work aims to distinguish Big Data from traditional Business Intelligence frameworks and to evaluate how modern memory-based database architectures, particularly SAP HANA, address current data processing challenges.
- Fundamental definitions and dimensions of Big Data (volume, velocity, variety).
- Comparative analysis between traditional Business Intelligence and Big Data approaches.
- Technical architecture of in-memory analytics and its performance benefits.
- Case study and technical implementation review of SAP HANA.
- Assessment of the future potential of in-memory technologies within the industry.
Excerpts from the Book
Technical concepts
The difference between a column-oriented storage and a row-oriented storage lies in the way as the data stored on the file system. Row-oriented storage means the values of a row are stored sequentially. If the table contents are stored column-oriented, data can be read without interruption and can be aggregated quickly, without jumping from column to column as in a column-oriented database (Knötzele, 2013, pp. 381-412). However, SAP HANA is a relational database whose tables can be stored in a columnar format (Vezzosi, Le Bihan, Mazoué, & Imm, 2015, pp. 3-5).
If the tables are stored in a columnar format, SAP HANA enables a higher compression-rate, because the values of a column have the same data type (Knötzele, 2013, pp. 381-412). The use of compression allows a significant reduction of data volume and makes is possible to store huge databases complete in main memory (Thiele, Lehner, & Habich, 2011, pp. 57-67). The reduction of data volume leads to a more effective use of the valuable RAM. So the companies are able to decrease their costs for main memory.
Summary of Chapters
1 Meaning of Big Data: This chapter defines Big Data and its primary characteristics, noting that current data volumes have reached limits that traditional technologies can no longer effectively process.
2 Technologies of Big Data: This section provides an overview of emerging technologies used to handle large data volumes, highlighting the shift from traditional processing to faster, more agile solutions.
3 Big Data versus Business Intelligence: The chapter clarifies the distinction between Business Intelligence, which answers known questions using structured data, and Big Data, which focuses on exploring unstructured data to uncover unknown insights.
4 Business Intelligence, Business Intelligence Framework and Data Warehouse: This part examines the standard Business Intelligence framework and the role of the Data Warehouse as a central component for decision support.
5 In-memory analytics: This main chapter details the architectural shift toward using massive RAM for data storage to minimize latency and optimize read/write operations.
5.1 Example of implementation: SAP HANA: This section explores the specific implementation of SAP HANA as a hybrid in-memory database, analyzing its technical foundations and real-time capabilities.
5.1.1 Technical concepts: This subsection focuses on the mechanics of column-oriented storage and data compression techniques used to improve efficiency.
5.1.2 SAP HANA and real-time: This text discusses the relationship between in-memory technology and the requirement for real-time analytics in modern business environments.
5.1.3 Solutions of SAP HANA: This section differentiates between the various deployment models of SAP HANA, specifically the Business Warehouse powered by HANA versus the native HANA approach.
5.1.4 Review: A critical evaluation of SAP HANA, considering its competitive position and its dependency on the specific organizational context.
5.2 Potential of in-memory analytics: This final section assesses the future market position of in-memory analytics using industry hype cycles to predict its trajectory toward widespread productivity.
Keywords
Big Data, In-memory analytics, SAP HANA, Business Intelligence, Data Warehouse, Column-oriented storage, Data volume, Velocity, Variety, Unstructured data, Real-time processing, Database technology, RAM, Compression, Performance optimization.
Frequently Asked Questions
What is the core subject of this thesis?
The work fundamentally explores the technological landscape of Big Data, with a concentrated analysis on in-memory analytics as a modern solution for processing massive amounts of high-velocity and unstructured data.
What are the central thematic areas covered?
The key themes include the definition and characteristics of Big Data, the structural differences between Big Data and traditional Business Intelligence, and the technical implementation of in-memory databases.
What is the primary research goal?
The goal is to explain how in-memory analytics function, why they outperform traditional disk-based systems, and to evaluate the practical implementation of these concepts using SAP HANA.
Which scientific methods are applied?
The paper utilizes a literature-based research approach, analyzing academic and industry-relevant sources to define technical concepts and assess current technology trends, supplemented by a practical review of SAP HANA features.
What topics are discussed in the main section?
The main section covers the architecture of in-memory systems, specific technical features like column-oriented storage and compression, and an evaluation of SAP HANA deployment solutions.
Which keywords define this work?
The primary keywords are Big Data, In-memory analytics, SAP HANA, Business Intelligence, Data Warehouse, and Performance optimization.
How does SAP HANA handle data intensive calculations?
SAP HANA optimizes operations by moving computational logic directly into the database layer, thereby reducing the need for unnecessary data transport to the application layer.
Is SAP HANA considered to have a unique selling point?
According to the author, SAP HANA does not have an absolute unique selling point, as competitors such as Oracle offer similar in-memory solutions; its attractiveness lies in its integration within the SAP landscape.
What is the significance of the "hype cycle" mentioned?
The Gartner Hype Cycle is used to illustrate that in-memory analytics has matured beyond initial high expectations and is currently delivering tangible business value, nearing its plateau of productivity.
- Arbeit zitieren
- Sven Weinzierl (Autor:in), 2015, Big Data In-Memory Analytics explained by SAP HANA, München, GRIN Verlag, https://www.grin.com/document/300886
