 Название: Handbook of Big Data Analytics : Methodologies, Volume 1
Автор: Vadlamani Ravi, Aswani Kumar Cherukuri
Издательство: The Institution of Engineering and Technology
Год: 2021
Страниц: 390
Язык: английский
Формат: pdf (true)
Размер: 11.8 MB
Big Data analytics is the complex process of examining Big Data to uncover information such as correlations, hidden patterns, trends and user and customer preferences, to allow organizations and businesses to make more informed decisions. These methods and technologies have become ubiquitous in all fields of science, engineering, business and management due to the rise of data-driven models as well as data engineering developments using parallel and distributed computational analytics frameworks, data and algorithm parallelization, and GPGPU programming. However, there remain potential issues that need to be addressed to enable Big Data processing and analytics in real time.
In the first volume of this comprehensive two-volume handbook, the authors present several methodologies to support Big Data analytics including database management, processing frameworks and architectures, data lakes, query optimization strategies, towards real-time data processing, data stream analytics, Fog and Edge computing, and Artificial Intelligence and Big Data.
Contents:
Preface
Introduction
1 The impact of Big Data on databases
1.1 The Big Data phenomenon
1.2 Scalability in relational databases
1.3 NoSQL databases
1.3.1 Disadvantages of NoSQL databases
1.3.2 Aggregate-oriented NoSQL databases
1.3.3 MongoDB: an example of documentary database
1.3.4 Cassandra: an example of columnar-oriented database
1.4 Data distribution models
1.5 Design examples using NoSQL databases
1.6 Design examples using NoSQL databases
1.7 Conclusions
References
2 Big data processing frameworks and architectures: a survey
2.1 Introduction
2.2 Apache Hadoop framework and Hadoop Ecosystem
2.3 HaLoop framework
2.4 Twister framework
2.5 Apache Pig
2.6 Apache Mahout
2.7 Apache Sqoop
2.8 Apache Flume
2.9 Apache Oozie
2.10 Hadoop 2
2.11 Apache Spark
2.12 Big data storage systems
2.13 Distributed stream processing engines
2.14 Apache Zookeeper
2.15 Open issues and challenges
3 The role of data lake in Big Data analytics: recent developments and challenges
4 Query optimization strategies for Big Data
5 Toward real-time data processing: an advanced approach in Big Data analytics
5.5 Lambda architecture
5.6 Stream processing approach for big data
5.6.1 Apache Spark
5.6.2 Apache Flink
5.6.3 Apache Samza
5.6.4 Apache Storm
5.6.5 Apache Flume
5.6.6 Apache Kafka
6 A survey on data stream analytics
7 Architectures of big data analytics: scaling out data mining algorithms using Hadoop-MapReduce and Spark
8 A review of fog and edge computing with Big Data analytics
9 Fog computing framework for Big Data processing using cluster management in a resource-constraint environment
10 Role of artificial intelligence and Big Data in accelerating accessibility for persons with disabilities
Overall conclusions
Index 347
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