Analytics for the Internet of Things
Application of Analytics to IoT devices
(Sprache: Englisch)
There will be a trillion connected devices by 2020. Everything from light bulbs in our houses, to our shoes, watches, and even our disposable razors will have sensors embedded in them that collect data that can be wirelessly transmitted. All of these...
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Klappentext zu „Analytics for the Internet of Things “
There will be a trillion connected devices by 2020. Everything from light bulbs in our houses, to our shoes, watches, and even our disposable razors will have sensors embedded in them that collect data that can be wirelessly transmitted. All of these connected devices will generate a veritable tsunami of data.Collecting, storing, processing, and analyzing this massive volume of machine-generated data will be the critical engineering challenge of the next decade. It is imperative that IT professionals and business executives learn about the issues and challenges involved in dealing with machine-generated IoT data and how to solve these issues using a big data technology platform. Mining for invaluable insight and building analytics solutions to predict events and patterns in this huge volume of IoT data is what IoT analytics - and this book - are all about.
Analytics for the Internet of Things describes scalable architectures for IoT analytics and presents practical use cases of IoT analytics in several business verticalsdomains. You will acquire mastery over the architecture, tools, and technologies needed to solve IoT analytics problems. You will also gain a deep understanding of the nature of analytics needed for IoT data and how to build analytics solutions using data science and machine learning.
The kinds of analytics covered include:
Descriptive analytics
Diagnostic analytics
Exploratory analytics
Predictive analytics
prescriptive analytics. Data scientist and big data architect Vivekanand Ganesan also presents advanced topics and recent advances in the analytics domain such as deep learning and graph theory, and he shows how this is applicable to IoT analytics. Deploying an IoT analytics solution to the public cloud and private cloud is also covered. While this is a deeply technical book, software programming knowledge is not a requirement to understand the concepts involved.
Analytics for the Internet of Things will empower the reader to
... mehr
build their own IoT analytics solutions, using the latest big data and cloud technologies, and reap the benefits of the IoT revolution. This book prepares the reader with the knowledge of best practices, big data architectures, data science and machine learning approaches for IoT analytics and enables you to turn your next IoT data analytics project into a roaring success.
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Inhaltsverzeichnis zu „Analytics for the Internet of Things “
Part I: Business view of IoT AnalyticsChapter 1: Internet of Things: An Introduction
Chapter Goal:
- Introduce IoT, IoT analytics, and the convergence of the two
- IoT defined
- IoT current state and the future
- IoT analytics: current state
- IoT, Big Data, and Cloud: the convergence of three key technologies
- The future: IoT analytics using big data that is deployed on the cloud
Chapter 2: IoT Analytics
Chapter Goal:
- Define the different kinds of analytics possible with IoT data
- Define the classification of analytics types
- Descriptive analytics and examples
- Diagnostic analytics and examples
- Exploratory analytics and examples
- Predictive analytics and examples
- Prescriptive analytics and examples
Chapter 3: IoT Analytics and Big Data
Chapter Goal:
- Define Big Data and explain why IoT analytics is a good fit for big data
- Nature of IoT data: Semi-structured, bursty, and time-series data
- Big Data: the three Vs and how IoT data will have all three Vs
4. How Storm, Kafka, etc. can also solve for streaming analytics
Chapter 4: Advanced IoT analytics
Chapter Goal:
- Describe machine learning and graph theory for IoT analytics
- Data science and machine learning defined
- How machine learning can be applied to IoT analytics
- Graph theory and how can it be applied to IoT analytics
- Deep learning and how can it be applied to IoT analytics
- Sensor Fusion: the holy grail of IoT analytics
Chapter 5: Applications of IoT analytics
Chapter Goal:
- IoT analytics applications in different verticals
- Wide variety of IoT devices and applications
- IoT analytics for Transportation
- IoT analytics for Healthcare
- IoT analytics for Security
- IoT analytics for Consumer/Lifestyle/Retail/Fashion
Part II: Technical view of IoT analytics
Chapter 6:
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Architecting IoT analytics: Challenges and Solutions
Chapter Goal:
- Explain why architecting for IoT analytics requires new thinking
- IoT analytics architecture considerations and challenges
- Designing for data
- Architecting for analytics
- Networking challenges and solutions
- Security challenges and solutions
- Scalability challenges and solutions
- Push vs Pull: How to strike the right balance
Chapter 7: Hadoop and IoT analytics: A match made in heaven
Chapter Goal:
- Explain why Hadoop 2.0 is a great platform for IoT analytics
- Introduction to Hadoop and Hadoop 2.0 for analytics
- Scalable data storage for IoT analytics
- Batch processing using Map/Reduce
- Built-in NoSQL database solution to handle time-series data
- Hadoop analytics ecosystem: a survey of tools
Chapter 8: IoT analytics: Search requirements
Chapter Goal:
- Explain why diagnostic analytics needs search
- Diagnostic analytics and the critical need for search solutions
- Keyword search
- Time range search
- Faceted search
- Advanced search
- Explain how Hadoop + Solr solves all of the above
Chapter 9: Get real with real-time analytics
Chapter Goal:
- Explain why real-time analytics is important and how to implement it
- Real-time analytics example for IoT analytics
- Simple counting, aggregation and dashboards using HBase
- Rules, Alerts, and Notifications using Storm
- Advanced Real-time analytics using Spark and Spark streaming
- Pub/Sub model using Kafka
- Lambda architecture as applied to IoT analytics
- Monitoring and Scaling real-time analytics infrastructure
Chapter 10: Graphs for IoT analytics
Chapter Goal:
- Explain why graphs matter for IoT analytics
- Graphs: an Introduction
- Graphs for IoT analytics: examples and use cases
- Graph theory models applied to IoT analytics
- Graphs in Hadoop: Titan, GraphX, and Giraph
- Integrating search, graphs, and real-time analytics in Hadoop
Chapter 11: Cloud Deployment of IoT analytics
Chapter Goal:
- Explain why deploying to the cloud makes a lot of sense from IoT analytics
- Characteristics of cloud deployment: scalability, distribution, and elasticity
- Volume and velocity demands a distributed architecture
- Sudden bursts demands an elastic architecture
- Real-time analytics demands a scalable architecture
- Public cloud vs Private cloud: making the right choice
- Cloud provider rundown: services and solutions offered for IoT analytics
Chapter 12: Putting it all together
Chapter Goal:
- A summary and a roadmap for project planning and success
- A blueprint for success: thinking about project planning for IoT analytics
- Design for data
- Architect for analytics
- Use big data technology
- Deploy on the cloud
- Be agile and flexible
- Have fun and change the world for the better
Chapter Goal:
- Explain why architecting for IoT analytics requires new thinking
- IoT analytics architecture considerations and challenges
- Designing for data
- Architecting for analytics
- Networking challenges and solutions
- Security challenges and solutions
- Scalability challenges and solutions
- Push vs Pull: How to strike the right balance
Chapter 7: Hadoop and IoT analytics: A match made in heaven
Chapter Goal:
- Explain why Hadoop 2.0 is a great platform for IoT analytics
- Introduction to Hadoop and Hadoop 2.0 for analytics
- Scalable data storage for IoT analytics
- Batch processing using Map/Reduce
- Built-in NoSQL database solution to handle time-series data
- Hadoop analytics ecosystem: a survey of tools
Chapter 8: IoT analytics: Search requirements
Chapter Goal:
- Explain why diagnostic analytics needs search
- Diagnostic analytics and the critical need for search solutions
- Keyword search
- Time range search
- Faceted search
- Advanced search
- Explain how Hadoop + Solr solves all of the above
Chapter 9: Get real with real-time analytics
Chapter Goal:
- Explain why real-time analytics is important and how to implement it
- Real-time analytics example for IoT analytics
- Simple counting, aggregation and dashboards using HBase
- Rules, Alerts, and Notifications using Storm
- Advanced Real-time analytics using Spark and Spark streaming
- Pub/Sub model using Kafka
- Lambda architecture as applied to IoT analytics
- Monitoring and Scaling real-time analytics infrastructure
Chapter 10: Graphs for IoT analytics
Chapter Goal:
- Explain why graphs matter for IoT analytics
- Graphs: an Introduction
- Graphs for IoT analytics: examples and use cases
- Graph theory models applied to IoT analytics
- Graphs in Hadoop: Titan, GraphX, and Giraph
- Integrating search, graphs, and real-time analytics in Hadoop
Chapter 11: Cloud Deployment of IoT analytics
Chapter Goal:
- Explain why deploying to the cloud makes a lot of sense from IoT analytics
- Characteristics of cloud deployment: scalability, distribution, and elasticity
- Volume and velocity demands a distributed architecture
- Sudden bursts demands an elastic architecture
- Real-time analytics demands a scalable architecture
- Public cloud vs Private cloud: making the right choice
- Cloud provider rundown: services and solutions offered for IoT analytics
Chapter 12: Putting it all together
Chapter Goal:
- A summary and a roadmap for project planning and success
- A blueprint for success: thinking about project planning for IoT analytics
- Design for data
- Architect for analytics
- Use big data technology
- Deploy on the cloud
- Be agile and flexible
- Have fun and change the world for the better
... weniger
Autoren-Porträt von Vivekanand Ganesan
- Vivek Ganesan is an experienced Big Data Architect and Data Scienctist with 18 years of expertise in building scalable, distributed, analytical systems. He has also designed and implemented recommendation engines, text mining applications, and IT operations/security analytics. He is also an Data Warehousing and BI expert working on high volume and high velocity data processing
Bibliographische Angaben
- Autor: Vivekanand Ganesan
- 2015, 1st ed., 250 Seiten, Maße: 19,1 x 23,5 cm, Kartoniert (TB), Englisch
- Verlag: APress
- ISBN-10: 1484211227
- ISBN-13: 9781484211229
- Erscheinungsdatum: 01.02.2016
Sprache:
Englisch
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