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BIG DATA, BLACK BOOK
The objective of this book is to create a new breed of versatile big data analysts and developers, who are thoroughly conversant with the basic and advanced analytic techniques for manipulating and analysing data, the big data platform, and the business and industry requirements to be able to participate productively in big data projects. contents : - introduction book preview chapter 1: getting an overview of big data what is big data? history of data management – evolution of big data structuring big data types of data elements of big data volume velocity variety veracity big data analytics advantages of big data analytics careers in big data skills required future of big data summary quick revise multiple-choice questions subjective questions chapter 2: exploring the use of big data in business context use of big data in social networking business intelligence marketing product design and development use of big data in preventing fraudulent activities preventing fraud using big data analytics use of big data in detecting fraudulent activities in insurance sector fraud detection methods use of big data in retail industry use of rfid data in retail summary quick revise multiple-choice questions subjective questions chapter 3: introducing technologies for handling big data distributed and parallel computing for big data introducing hadoop how does hadoop function? cloud computing and big data features of cloud computing cloud deployment models cloud delivery models cloud services for big data cloud providers in big data market in-memory computing technology for big data summary quick revise multiple-choice questions subjective questions chapter 4: understanding hadoop ecosystem hadoop ecosystem hadoop distributed file system hdfs architecture features of hdfs mapreduce features of mapreduce hadoop yarn hbase features of hbase hive pig and pig latin sqoop zookeeper flume oozie summary quick revise multiple-choice questions subjective questions chapter 5: understanding mapreduce fundamentals and hbase the mapreduce framework exploring the features of mapreduce working of mapreduce exploring map and reduce functions techniques to optimize mapreduce jobs hardware/network topology synchronization file system uses of mapreduce role of hbase in big data processing characteristics of hbase installation of hbase summary quick revise multiple-choice questions subjective questions chapter 6: understanding big data technology foundations exploring the big data stack data sources layer ingestion layer storage layer physical infrastructure layer platform management layer security layer monitoring layer analytics engine visualization layer big data applications virtualization and big data virtualization approaches server virtualization application virtualization network virtualization processor and memory virtualization data and storage virtualization managing virtualization with hypervisor implementing virtualization to work with big data summary quick revise multiple-choice questions subjective questions chapter 7: storing data in databases and data warehouses rdbms and big data issues with the relational model non-relational database issues with the non-relational model polyglot persistence integrating big data with traditional data warehouses big data analysis and data warehouse changing deployment models in big data era summary quick revise multiple-choice questions subjective questions chapter 8: storing data in hadoop introducing hdfs hdfs architecture using hdfs files hadoop-specific file system types hdfs commands the org.apache.hadoop.io package hdf hdfs high availability introducing hbase hbase architecture storing big data with hbase interacting with the hadoop ecosystem hbase in operation – programming with hbase installation combining hbase and hdfs selecting the suitable hadoop data organization for applications summary quick revise multiple-choice questions subjective questions chapter 9: processing your data with mapreduce recollecting the concept of mapreduce framework developing simple mapreduce application building the application executing the application points to consider while designing mapreduce summary quick revise multiple-choice questions subjective questions chapter 10: customizing mapreduce execution controlling mapreduce execution with inputformat inputsplit recordreader fileinputformat implementing inputformat for compute-intensive applications implementing inputformat to control the number of maps implementing inputformat for multiple hbase tables reading data with custom recordreader organizing output data with outputformats customizing data with recordwriter optimizing mapreduce execution with combiner controlling reducer execution with partitioners implementing a mapreduce program for sorting text data summary quick revise multiple-choice questions subjective questions chapter 11: testing and debugging mapreduce applications performing unit testing for mapreduce applications unit testing the mapper component unit testing the reducer component integration testing of the mapper-reducer combination performing local application testing with eclipse logging for hadoop testing application log processing defensive programming in mapreduce summary quick revise multiple-choice questions subjective questions chapter 12: understanding hadoop yarn architecture background of yarn revisiting mapreduce limitations of mapreduce advantages of yarn yarn architecture resourcemanager applicationmanager integration of resourcemanager and applicationmanager working of yarn yarn schedulers capacityscheduler fairscheduler backward compatibility with yarn script compatibility binary compatibility source compatibility yarn configurations yarn commands administration commands user commands log management in hadoop 1 log management in yarn summary quick revise multiple-choice questions subjective questions chapter 13: exploring hive introducing hive getting started with hive hive variables hive properties hive queries data types in hive built-in functions in hive hive ddl creating databases viewing a database dropping a database altering databases creating tables creating a table using the existing schema dropping tables altering tables using hive ddl statements data manipulation in hive loading files into tables inserting data into tables update in hive delete in hive using hive dml statements data retrieval queries using the select command using the where clause using the group by clause using the having clause using the limit clause executing hiveql queries using joins in hive inner joins outer joins cartesian product joins map-side joins joining tables summary quick revise multiple-choice questions subjective questions chapter 14: analyzing data with pig introducing pig the pig architecture benefits of pig installing pig properties of pig running pig running pig programs getting started with pig latin pig latin structure pig latin application flow working with operators in pig foreach assert filter group order by distinct join limit sample split flatten working with functions in pig summary quick revise multiple-choice questions subjective questions chapter 15: using oozie introducing oozie main functional components of oozie benefits of oozie installing and configuring oozie understanding the oozie workflow execution of asynchronous actions in oozie implementing the oozie workflow oozie recovery capabilities oozie workflow life cycle oozie coordinator types of oozie coordinator oozie coordinator lifecycle operations oozie bundle oozie parameterization with el workflow functions coordinator functions bundle functions el functions oozie job execution model accessing oozie oozie sla event status sla status oozie activity the oozie sla subsystem sla language schema summary quick revise multiple-choice questions subjective questions chapter 16: nosql data management introduction to nosql characteristics of nosql evolution of databases aggregate data models key value data model document databases relationships graph databases schema-less databases materialized views distribution models cap theorem sharding mapreduce partitioning and combining composing mapreduce calculations summary quick revise multiple-choice questions subjective questions chapter 17: understanding analytics and big data comparing reporting and analysis reporting analysis the analytic process types of analytics basic analytics advanced analytics operationalized analytics monetized analytics characteristics of big data analysis points to consider during analysis frame the problem correctly statistical significance or business importance? making inferences versus computing statistics developing an analytic team skills required for an analyst convergence of it and analytics understanding text analytics summary quick revise multiple-choice questions subjective questions chapter 18: analytical approaches and tools to analyze data analytical approaches ensemble methods text data analysis history of analytical tools graphical user interfaces point solutions data visualization tools introducing popular analytical tools the r project for statistical computing ibm spss sas comparing various analytical tools installing r installing r on a windows computer installing r on a macintosh computer installing r on a linux computer installing rstudio on windows installing rstudio on linux summary quick revise multiple-choice questions subjective questions chapter 19: exploring r exploring basic features of r statistical features programming features packages graphical user interfaces exploring rgui r console developing a program quitting r exploring rstudio handling basic expressions in r basic arithmetic in r mathematical operators variables in r calling functions in r working with vectors storing and calculating values in r creating and using objects interacting with users handling data in r workspace the ls() function the rm() function the getwd() function the save() function the load () function executing scripts creating plots accessing help and documentation in r using built-in datasets in r summary quick revise multiple-choice questions subjective questions chapter 20: reading datasets and exporting data from r using the c() command reading and combining numerical data reading and combining text data reading both numeric and text values in r using the scan() command reading the text data using the scan() command using clipboard to create the data reading the data of a file from disk reading multiple data values from large files using the read.csv() command using the read.table() command reading data from r studio exporting data from r using the write.table() command using the write.csv() command summary quick revise multiple-choice questions subjective questions chapter 21: manipulating and processing data in r selecting the most appropriate data structure creating data subsets creating subsets in vectors creating subsets in data frames merging datasets in r using the merge() function using the cbind function using the rbind() function sorting data sorting data ordering data reverse sort putting your data into shape transposing data converting data to wide or long formats melting data to long format casting data to wide format managing data in r using matrices reshaping a vector into a matrix accessing matrix and subsetting the data managing data in r using data frames creating data frames accessing data frames merging data frames performing operations on data frames summary quick revise multiple-choice questions subjective questions chapter 22: working with functions and packages in r using functions instead of scripts transforming an r script into a function returning results in r reducing the number of lines in an r function assigning the function objects writing function without braces using arguments in functions using dot argument in function passing functions as arguments anonymous functions local and global environment of functions built-in functions in r numeric functions character functions statistical probability functions miscellaneous functions introducing packages working with packages summary quick revise multiple-choice questions subjective questions chapter 23: performing graphical analysis in r using plots using plots for a single variable using plots for two variables using plots for multiple variables designing special plots saving graphs to external files summary quick revise multiple-choice questions subjective questions chapter 24: integrating r and hadoop and understanding hive rhadoop?an integration of r and hadoop installing rhadoop using rhadoop text mining in rhadoop data analysis using the mapreduce technique in rhadoop data mining in hive summary quick revise multiple-choice questions subjective questions chapter 25: data visualization-i introducing data visualization techniques used for visual data representation types of data visualization applications of data visualization visualizing big data deriving business solutions turning data into information tools used in data visualization proprietary data visualization tools open-source data visualization tools analytical techniques used in big data visualization tableau products installation of tableau public summary quick revise multiple-choice questions subjective questions chapter 26: data visualization with tableau (data visualization-ii) introduction to tableau software tableau desktop workspace operations on data data analytics in tableau public using visual controls in tableau public summary quick revise multiple-choice questions subjective questions chapter 27: social media analytics and text mining introducing social media introducing key elements of social media introducing text mining understanding text mining process sentiment analysis performing social media analytics and opinion mining on tweets online social media analysis summary quick revise multiple-choice questions subjective questions chapter 28: mobile analytics introducing mobile analytics define mobile analytics mobile analytics and web analytics types of results from mobile analytics types of applications for mobile analytics introducing mobile analytics tools location-based tracking tools real-time analytics tools user behavior tracking tools performing mobile analytics challenges of mobile analytics summary quick revise multiple-choice questions subjective questions chapter 29: finding a job in the big data market importance and scope of big data jobs big data opportunities skill assessment for big data jobs roles and responsibilities in big data jobs business analyst for big data big data scientist software developer for big data gaining a foothold in the big data market take your time preparing the big data skill learning and testing mechanism basic educational requirements for big data jobs basic technological requirements for big data jobs

Author : Big data, black book
Publication : Dreamtech press
Isbn : 9789351197577
Store book number : 107
NRS 1280.00
  
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