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[null,"1.","Record Nr.",null,"UNINA9911006763703321"]
[null,null,"Autore",null,"Owens Jonathan R"]
[null,null,"Titolo",null,"Hadoop real-world solutions cookbook : Realistic, simple code examples to solve problems at scale with Hadoop and related technologies / / Jonathan R. Owens, Jon Lentz, Brian Femiano"]
[null,null,"Pubbl/distr/stampa",null,"Birmingham [England], : Packt Pub., 2013"]
[null,null,"ISBN",null,"1-62198-910-0"]
[{"$nulls":4},"1-84951-913-7"]
[{"$nulls":4},"1-299-18393-X"]
[null,null,"Edizione",null,"[1st edition]"]
[null,null,"Descrizione fisica",null,"1 online resource (316 p.)"]
[null,null,"Disciplina",null,"004.6"]
[{"$nulls":4},"005.74"]
[null,null,"Soggetti",null,"Electronic data processing - Distributed processing"]
[{"$nulls":4},"Open source software"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Includes index."]
[null,null,"Nota di contenuto",null,"Cover; Copyright; Credits; About the Authors; About the Reviewers; www.packtpub.com; Table of Contents; Preface; Chapter 1: Hadoop Distributed File System - Importing and Exporting Data; Introduction; Importing and exporting data into HDFS using Hadoop shell commands; Moving data efficiently between clusters using Distributed Copy; Importing data from MySQL into HDFS using Sqoop; Exporting data from HDFS into MySQL using Sqoop; Configuring Sqoop for Microsoft SQL Server; Exporting data from HDFS into MongoDB; Importing data from MongoDB into HDFS"]
[{"$nulls":4},"Exporting data from HDFS into MongoDB using PigUsing HDFS in a Greenplum external table; Using Flume to load data into HDFS; Chapter 2: HDFS; Introduction; Reading and writing data to HDFS; Compressing data using LZO; Reading and writing data to SequenceFiles; Using Apache Avro to serialize data; Using Apache Thrift to serialize data; Using Protocol Buffers to serialize data; Setting the replication factor for HDFS; Setting the block size for HDFS; Chapter 3: Extracting and Transforming Data; Introduction; Transforming Apache logs into TSV format using MapReduce"]
[{"$nulls":4},"Using Apache Pig to filter bot traffic from web server logsUsing Apache Pig to sort web server log data by timestamp; Using Apache Pig to sessionize web server log data; Using Python to extend Apache Pig "]
[{"$nulls":4},"functionality; Using MapReduce and secondary sort to calculate page views; Using Hive and Python to clean and transform geographical event data; Using Python and Hadoop Streaming to perform a time series analytic; Using Multiple Outputs in MapReduce to name output files; Creating custom Hadoop Writable and InputFormat to read geographical event data"]
[{"$nulls":4},"Chapter 4: Performing Common Tasks Using Hive, Pig, and MapReduce Introduction; Using Hive to map an external table over weblog data in HDFS; Using Hive to dynamically create tables from the results of a weblog query; Using the Hive string UDFs to concatenate fields in weblog data; Using Hive to intersect weblog IPs and determine the country; Generating n-grams over news archives using MapReduce; Using the distributed cache in MapReduce; to find lines that contain matching keywords over news archives; Using Pig to load a table and perform a SELECT operation with GROUP BY"]
[{"$nulls":4},"Chapter 5: Advanced Joins Introduction; Joining data in the Mapper using MapReduce; Joining data using Apache Pig replicated join; Joining sorted data using Apache Pig merge join; Joining skewed data using Apache Pig skewed join; Using a map-side join in Apache Hive to analyze geographical events; Using optimized full outer joins in Apache Hive to analyze geographical events; Joining data using an external key-value store (Redis); Chapter 6: Big Data Analysis; Introduction; Counting distinct IPs in web log data using MapReduce and Combiners"]
[{"$nulls":4},"Using Hive date UDFs to transform and sort event dates from geographic event data"]
[null,null,null,"Sommario/riassunto","Realistic, simple code examples to solve problems at scale with Hadoop and related technologies."]