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