1Chapter 1. What Is Big Data?
586.7 Spark Streaming - introduction and
21.1 Introduction
59architecture
31.2 Big data
606.8 Spark – use cases
41.3 The Big Data dimensional paradigm
616.9 Summary
51.4 Main Components Of Big Data
626.10 Questions
61.5 Real-time processing
63Chapter 7. Oracle NoSQL Database
71.6 Applications of big data
647.1 Introduction
81.7 Current Trends in Big-Data
657.2 History of NoSQL
91.8 Summary
667.3 Features of NoSQL
101.9 Questions
677.4 Big Data and NoSQL
11Chapter 2. Real-Time Big Data Analytics
687.5 Oracle’s Approach to Big Data
122.1 Introduction
697.6 Oracle NoSQL Database
132.2 Breaking Down Real-Time Big Data Analytics
707.7 What is the CAP Theorem?
142.3 Why is it?
717.8 Database systems Architecture
152.4 Real-time analytics architecture
727.9 Oracle NoSQL Database Architecture
162.5 Applications
737.10 ACID Transactions and Distributed
172.6 Summary
74Transactions
182.6 Questions
757.11 Advantages and disadvantages of NoSQL
19Chapter 3. The RTBDA Stack And Phases
767.12 Summary
203.1 RTBDA Stack
777.13 Questions
213.2 Five Phases of Real-Time
78Chapter 8. Introducing Lambda Architecture
223.3 Summary
798.1 Introduction
233.4 Questions
808.2 Need for Lambda Architecture
24Chapter 4. Introducing Hadoop
818.3 Features
254.1 Introduction
828.4 Layers/components of Lambda Architecture
264.2 Hadoop features
838.5 Technology matrix for Lambda Architecture
274.3 MapReduce
848.6 Realization of Lambda Architecture
284.4 Understanding HDFS
858.7 High-level architecture
294.5 Hadoop subprojects
868.8 Configuring Apache Cassandra and Spark
304.6 Hadoop components
878.8 Coding the custom producer
314.7 Basics of Hadoop streaming
888.9 Coding the real-time layer
324.8 MapReduce dataflow
898.10 Coding the batch layer
334.9 Hadoop MapReduce terminologies
908.11 Coding the serving layer
344.9 Writing a Hadoop MapReduce example
918.12 Executing all the layers
354.10 Understanding several possible MapReduce
928.13 Summary
36definitions to solve business problems
938.14 Questions
374.11 Features of MapReduce
94Chapter 9. Emerging Technologies In Data Analytics
384.12 Other Components of Hadoop
959.1 Introduction
394.13 Summary
969.2 Least-squares-solver
404.14 Questions
979.3 Neuromorphic Hardware Acceleration
41Chapter 5. Introducing Storm
98Enabled by Emerging Technologies
425.1 Introduction
999.4 Energy Efficient Spiking Neural Network
435.2 Traditional Approaches and its Disadvantages
100Design with RRAM Devices
445.3 Apache Storm vs. Hadoop
1019.5 Summary
455.4 Abstractions of storm
1029.6 Questions
465.5 Storm architecture and its components
103Chapter 10. Challenges Of Real-Time Analytics
475.6 Setting up and configuring Storm
10410.1 Introduction
485.7 Real-time processing job on Storm
10510.2 Challenges
495.8 Summary
10610.3 A normative perspective of Big Data:
505.9 Questions
107challenges and analytical methods
51Chapter 6. Introducing Spark
10810.4 Big Data Challenges – related to Q1
526.1 Introduction
10910.5 Big Data analytical methods – related to Q2
536.2 Installing and Configuring Apache Spark
11010.6 Types of Big Data Challenges
546.3 Spark framework and schedulers
11110.7 Types of Big Data analytical methods
556.4 The architecture of Spark
11210.8 Summary
566.5 Spark execution model – master-worker view
11310.9 Questions
576.6 Working with Spark Operations
114Glossary
