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Practical big data analytics : hands-on techniques to implement enterprise analytics and machine learning using Hadoop, Spark, NoSQL and R /
Big Data analytics relates to the strategies used by enterprises to process and analyze large amounts of data to bring out hidden insights. With the help of open source and enterprise tools, such as R, Python, Hadoop, and Spark, you will learn how to effectively mine your Big Data. By the end of thi...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
2018.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Dasgupta, Nataraj, |e author. | |
| 245 | 1 | 0 | |a Practical big data analytics : |b hands-on techniques to implement enterprise analytics and machine learning using Hadoop, Spark, NoSQL and R / |c Nataraj Dasgupta. |
| 264 | 1 | |a Birmingham, UK : |b Packt Publishing, |c 2018. | |
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| 505 | 0 | |a Cover; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Too Big or Not Too Big; What is big data?; A brief history of data; Dawn of the information age; Dr. Alan Turing and modern computing; The advent of the stored-program computer; From magnetic devices to SSDs; Why we are talking about big data now if data has always existed; Definition of big data; Building blocks of big data analytics; Types of Big Data; Structured; Unstructured; Semi-structured; Sources of big data; The 4Vs of big data. | |
| 505 | 8 | |a When do you know you have a big data problem and where do you start your search for the big data solution?Summary; Chapter 2: Big Data Mining for the Masses; What is big data mining?; Big data mining in the enterprise; Building the case for a Big Data strategy; Implementation life cycle; Stakeholders of the solution; Implementing the solution; Technical elements of the big data platform; Selection of the hardware stack; Selection of the software stack; Summary; Chapter 3: The Analytics Toolkit; Components of the Analytics Toolkit; System recommendations; Installing on a laptop or workstation. | |
| 505 | 8 | |a Installing on the cloudInstalling Hadoop; Installing Oracle VirtualBox; Installing CDH in other environments; Installing Packt Data Science Box; Installing Spark; Installing R; Steps for downloading and installing Microsoft R Open; Installing RStudio; Installing Python; Summary; Chapter 4: Big Data With Hadoop; The fundamentals of Hadoop; The fundamental premise of Hadoop; The core modules of Hadoop; Hadoop Distributed File System -- HDFS; Data storage process in HDFS; Hadoop MapReduce; An intuitive introduction to MapReduce; A technical understanding of MapReduce. | |
| 505 | 8 | |a Block size and number of mappers and reducersHadoop YARN; Job scheduling in YARN; Other topics in Hadoop; Encryption; User authentication; Hadoop data storage formats; New features expected in Hadoop 3; The Hadoop ecosystem; Hands-on with CDH; WordCount using Hadoop MapReduce; Analyzing oil import prices with Hive; Joining tables in Hive; Summary; Chapter 5: Big Data Mining with NoSQL; Why NoSQL?; The ACID, BASE, and CAP properties; ACID and SQL; The BASE property of NoSQL; The CAP theorem; The need for NoSQL technologies; Google Bigtable; Amazon Dynamo; NoSQL databases; In-memory databases. | |
| 505 | 8 | |a Columnar databasesDocument-oriented databases; Key-value databases; Graph databases; Other NoSQL types and summary of other types of databases ; Analyzing Nobel Laureates data with MongoDB; JSON format; Installing and using MongoDB; Tracking physician payments with real-world data; Installing kdb+, R, and RStudio; Installing kdb+; Installing R; Installing RStudio; The CMS Open Payments Portal; Downloading the CMS Open Payments data; Creating the Q application; Loading the data; The backend code; Creating the frontend web portal; R Shiny platform for developers. | |
| 505 | 8 | |a Putting it all together -- The CMS Open Payments application. | |
| 520 | |a Big Data analytics relates to the strategies used by enterprises to process and analyze large amounts of data to bring out hidden insights. With the help of open source and enterprise tools, such as R, Python, Hadoop, and Spark, you will learn how to effectively mine your Big Data. By the end of this book, you will have a clear understanding ... | ||
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