Length10h 5m
About this audiobook
Real-Time Big Data Analytics: Emerging Trends explores how advanced technologies have significantly reduced data processing cycle time, enabling unprecedented data exploration and experimentation. This book delves into the real promise of advanced data analytics beyond mere technology, highlighting how real-time big data analytics processes data as it arrives to provide timely, actionable insights.
We discuss scalable hardware solutions based on emerging technologies like nonvolatile memory devices and in-memory computing, paired with optimized data analytics algorithms such as machine learning. The book covers various frameworks for data analytics, including Hadoop, Spark, Storm, and NoSQL, and provides a comparative performance analysis of each.
Designed for students, scholars, and professionals, Real-Time Big Data Analytics: Emerging Trends is an invaluable resource for those looking to master big data and real-time analytics.
Audiobook details
GenreTechnology, Science and Nature
Length10 hrs 5 mins
Narrated byListen with 1,000+ voices
FormateBook with Audio
Publish dateJan 3, 2025
LanguageEnglish
Table of contents
1Chapter 1. What Is Big Data?
21.1 Introduction
31.2 Big data
41.3 The Big Data dimensional paradigm
51.4 Main Components Of Big Data
Show all chaptersShow less
61.5 Real-time processing
71.6 Applications of big data
81.7 Current Trends in Big-Data
91.8 Summary
101.9 Questions
11Chapter 2. Real-Time Big Data Analytics
122.1 Introduction
132.2 Breaking Down Real-Time Big Data Analytics
142.3 Why is it?
152.4 Real-time analytics architecture
162.5 Applications
172.6 Summary
182.6 Questions
19Chapter 3. The RTBDA Stack And Phases
203.1 RTBDA Stack
213.2 Five Phases of Real-Time
223.3 Summary
233.4 Questions
24Chapter 4. Introducing Hadoop
254.1 Introduction
264.2 Hadoop features
274.3 MapReduce
284.4 Understanding HDFS
294.5 Hadoop subprojects
304.6 Hadoop components
314.7 Basics of Hadoop streaming
324.8 MapReduce dataflow
334.9 Hadoop MapReduce terminologies
344.9 Writing a Hadoop MapReduce example
354.10 Understanding several possible MapReduce
36definitions to solve business problems
374.11 Features of MapReduce
384.12 Other Components of Hadoop
394.13 Summary
404.14 Questions
41Chapter 5. Introducing Storm
425.1 Introduction
435.2 Traditional Approaches and its Disadvantages
445.3 Apache Storm vs. Hadoop
455.4 Abstractions of storm
465.5 Storm architecture and its components
475.6 Setting up and configuring Storm
485.7 Real-time processing job on Storm
495.8 Summary
505.9 Questions
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