In this research paper, the author would like to take a look at the current Big Data vendors, and present the status quo of the leading Big Data solutions.
The Big Data market has grown significantly in the last years. The offered solutions are very sophisticated and cover a broad range of user requirements, and have become more user friendly. In the recent years, several well-known IT companies released new products that specialize in Big Data analysis. The desire to analyze more and more data to gain a better understanding of e.g. customer needs, manufacturing efficiencies or e.g. to create predictive analysis based on past consumer behavior drove the need to enhance the functionality of existing business intelligence solutions towards a more open Big Data architecture, that allows the analysis of massive amounts of structured and unstructured data.
Table of Contents
1 Introduction
2 The basics of Big Data
2.1 Definition
2.2 Historic Development
2.3 Usage and Application
3 Market overview Big Data solutions
3.1 Apache Hadoop - Enabling Big Data
3.2 Market analysis of Big Data solutions
3.3 Cost estimates
3.4 Big Data-Solutions of selected vendors
3.4.1 IBM
3.4.3 Oracle
3.4.4 SAP
3.4.5 Microsoft
4 Summary
5 Bibliography
Objectives and Research Themes
This research paper aims to provide a comprehensive market overview of current Big Data solutions. It examines the fundamental characteristics of Big Data, analyzes the prevailing market landscape for related technologies, and evaluates the solutions offered by leading vendors such as IBM, Oracle, SAP, and Microsoft to understand their status quo and strategic implementation.
- Fundamentals, characteristics, and historical development of Big Data.
- Market analysis and cost estimations of Big Data software solutions.
- Technical deep dive into the Apache Hadoop framework.
- Evaluation of vendor-specific Big Data portfolios and their practical application.
- Comparison of cloud-based vs. on-premise Big Data strategies.
Excerpt from the Book
2.1 Definition
The term Big Data refers to a large amount of data that exceeds the processing capacity of conventional database systems.1 In practice, the term Big Data is used not only for large amounts of data, but also for the method used to analyze these data. Thus, the term "Big Data" can either mean the data volume, the method or the system with which the data are analyzed and evaluated.
The Big Data technology is not based on a singular technology, it is the result of the interaction of a whole series of innovations and technologies.2 Thus, there is not only one isolated solution that enables the analysis of large amounts of data. It is the combination of different technological developments that allows companies with the willingness to invest in these technologies to start to transform huge amount of data into information.
Big Data has four important characteristics:3
1. Volume: An increasing number of organizations and enterprises have gigantic data volumes ranging from a few terabytes to petabytes.
2. Variety: More and more sources of data are of different types, which can be grouped roughly into unstructured, semi structured and structured data. Companies are increasingly collecting external data, for example from social networks.
3. Velocity: Huge amounts of data have to be quickly evaluated in real-time. The processing speed has to keep up with the pace of data growth.
4. Veracity: The collected data has to be clean, which results in the need to prevent “dirty” data from accumulating in the databases. Valid data are the basis for all further analyses.4
Summary of Chapters
1 Introduction: This chapter provides the motivation for the research, highlighting the growing significance of data warehousing and business intelligence in a digitalized environment.
2 The basics of Big Data: This section defines Big Data by its key characteristics (Volume, Variety, Velocity, Veracity) and outlines its historic evolution and usage in modern enterprises.
3 Market overview Big Data solutions: This core chapter evaluates the role of Apache Hadoop, provides a market analysis, estimates project costs, and details the specific software portfolios of IBM, Oracle, SAP, and Microsoft.
4 Summary: The concluding chapter synthesizes the findings, noting the trend towards cloud-based solutions and emphasizing that Big Data represents a strategic investment for competitive advantage.
5 Bibliography: A comprehensive list of sources, academic papers, and vendor documentation used to support the research.
Keywords
Big Data, Apache Hadoop, Business Intelligence, Data Warehouse, Cloud Computing, SAP HANA, IBM BigInsights, Microsoft Azure, Oracle Big Data SQL, Data Analytics, Predictive Analysis, Real-time Processing, Data Management, Information Supremacy, Market Analysis
Frequently Asked Questions
What is the primary focus of this research paper?
The paper provides a market overview of current Big Data solutions, examining how leading IT vendors address the challenges of massive, unstructured data sets in modern business environments.
What are the central themes covered in the text?
The core themes include the definition and characteristics of Big Data, the importance of the Apache Hadoop framework, the strategic value of analytical tools, and a comparative look at enterprise vendor portfolios.
What is the main objective of the research?
The objective is to present the status quo of leading Big Data vendors and to help organizations understand the strategic necessity and cost implications of implementing these technologies.
Which scientific or analytical method is applied?
The paper utilizes a descriptive analysis of market trends and a comparative vendor benchmark, relying on existing industry reports, whitepapers, and technical documentation from the vendors themselves.
What does the main body of the work address?
The main body details the technological foundations of Big Data, performs a market analysis, discusses cost structures, and explores the specific products and services offered by IBM, Oracle, SAP, and Microsoft.
Which keywords best characterize this work?
Key terms include Big Data, Apache Hadoop, Cloud Computing, Data Analytics, Business Intelligence, and the specific vendor technologies such as SAP HANA and Azure HDInsight.
How does SAP HANA differ from other solutions mentioned?
SAP HANA is noted for its in-memory technology, which allows for real-time processing and the development of custom applications, often integrating with existing SAP ERP environments.
Why is the shift to cloud-based Big Data solutions significant?
The shift is considered crucial because it allows companies to outsource large, complex data management infrastructure, reducing the total cost of ownership while maintaining accessibility from anywhere.
- Citation du texte
- Wolfgang Steinhart (Auteur), 2017, Big Data Software Solutions by IBM, Oracle, SAP and Microsoft. A Market Overview, Munich, GRIN Verlag, https://www.grin.com/document/369045
