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
- Abstract
- Table of contents
- 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
Objectives and Key Themes
This thesis explores the concept of Big Data and its role in modern data processing. It aims to provide a comprehensive understanding of Big Data technologies, specifically focusing on in-memory analytics.
- Defining Big Data and its characteristics
- Exploring the differences between Big Data and Business Intelligence
- Examining the theory and application of in-memory analytics
- Analyzing the implementation of in-memory analytics through the example of SAP HANA
- Assessing the potential and benefits of in-memory analytics
Chapter Summaries
The first chapter introduces the concept of Big Data and its growing importance across various industries. It highlights the challenges posed by the massive amount of data generated and explores the definition and characteristics of Big Data.
Chapter two delves into the technologies employed in Big Data processing, providing an overview of the various methods and tools used to manage and analyze large datasets.
Chapter three draws a distinction between Big Data and Business Intelligence, emphasizing the unique aspects and applications of each concept. It explores the limitations of traditional Business Intelligence approaches in handling Big Data and highlights the need for new solutions.
Chapter four examines the relationship between Business Intelligence, Data Warehouses, and in-memory analytics. It discusses the limitations of traditional data warehousing approaches and explores the advantages of in-memory analytics in addressing these limitations.
Chapter five focuses on in-memory analytics, providing a detailed explanation of its principles and implementation. It uses SAP HANA as a specific example to illustrate the technical concepts, real-time capabilities, and solutions offered by in-memory analytics platforms.
Keywords
Big Data, in-memory analytics, SAP HANA, data processing, data management, Business Intelligence, data warehousing, real-time analytics, data velocity, data volume, data variety.
- Citation du texte
- Sven Weinzierl (Auteur), 2015, Big Data In-Memory Analytics explained by SAP HANA, Munich, GRIN Verlag, https://www.grin.com/document/300886
