Processing big data with Azure HDInsight : building real-world big data systems on Azure HDInsight using the Hadoop ecosystem /
Saved in:
| Author / Creator: | Yadav, Vinit. |
|---|---|
| Imprint: | [New York] : Apress, [2017] ©2017 |
| Description: | 1 online resource |
| Language: | English |
| Subject: | |
| Format: | E-Resource Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/11274264 |
MARC
| LEADER | 00000cam a2200000 i 4500 | ||
|---|---|---|---|
| 001 | 11274264 | ||
| 006 | m o d | ||
| 007 | cr ||||||||||| | ||
| 008 | 170602s2017 nyu o 000 0 eng d | ||
| 005 | 20240718143523.7 | ||
| 015 | |a GBB999805 |2 bnb | ||
| 016 | 7 | |a 019205975 |2 Uk | |
| 019 | |a 988396632 |a 989055284 |a 992469381 |a 992892065 |a 1005772038 |a 1011795953 |a 1017738612 |a 1019734016 |a 1048150203 |a 1058402871 |a 1066613587 |a 1066653100 |a 1086462099 |a 1097090476 |a 1192347593 |a 1203989704 |a 1227637721 |a 1240528686 | ||
| 020 | |a 1484228693 |q (electronic bk.) | ||
| 020 | |a 9781484228692 |q (electronic bk.) | ||
| 020 | |a 1484228685 | ||
| 020 | |a 9781484228685 | ||
| 020 | |z 9781484228685 | ||
| 024 | 7 | |a 10.1007/978-1-4842-2869-2 |2 doi | |
| 035 | |a (OCoLC)988753673 |z (OCoLC)988396632 |z (OCoLC)989055284 |z (OCoLC)992469381 |z (OCoLC)992892065 |z (OCoLC)1005772038 |z (OCoLC)1011795953 |z (OCoLC)1017738612 |z (OCoLC)1019734016 |z (OCoLC)1048150203 |z (OCoLC)1058402871 |z (OCoLC)1066613587 |z (OCoLC)1066653100 |z (OCoLC)1086462099 |z (OCoLC)1097090476 |z (OCoLC)1192347593 |z (OCoLC)1203989704 |z (OCoLC)1227637721 |z (OCoLC)1240528686 | ||
| 035 | 9 | |a (OCLCCM-CC)988753673 | |
| 037 | |a CL0500000921 |b Safari Books Online | ||
| 040 | |a YDX |b eng |e pn |c YDX |d N$T |d EBLCP |d N$T |d GW5XE |d UAB |d ESU |d AZU |d UPM |d OCLCF |d IOG |d COO |d MERER |d OCLCQ |d VT2 |d OCLCO |d IDB |d K6U |d U3W |d MERUC |d UMI |d UIU |d TOH |d STF |d LIV |d D6H |d VVB |d OCLCQ |d CEF |d KSU |d EZ9 |d OCLCQ |d OCLCO |d DEBBG |d AU@ |d OCLCQ |d WYU |d OCLCO |d G3B |d OCLCQ |d S9I |d C6I |d AUD |d LEAUB |d OCLCQ |d OCLCO |d ERF |d UKAHL |d OCLCQ |d BRF |d UKMGB |d DCT |d HS0 |d OCLCQ | ||
| 049 | |a MAIN | ||
| 050 | 4 | |a QA76.585 | |
| 072 | 7 | |a COM |x 013000 |2 bisacsh | |
| 072 | 7 | |a COM |x 014000 |2 bisacsh | |
| 072 | 7 | |a COM |x 018000 |2 bisacsh | |
| 072 | 7 | |a COM |x 067000 |2 bisacsh | |
| 072 | 7 | |a COM |x 032000 |2 bisacsh | |
| 072 | 7 | |a COM |x 037000 |2 bisacsh | |
| 072 | 7 | |a COM |x 052000 |2 bisacsh | |
| 072 | 7 | |a UNF |2 bicssc | |
| 072 | 7 | |a UKS |2 bicssc | |
| 100 | 1 | |a Yadav, Vinit. | |
| 245 | 1 | 0 | |a Processing big data with Azure HDInsight : |b building real-world big data systems on Azure HDInsight using the Hadoop ecosystem / |c Vinit Yadav. |
| 260 | |a [New York] : |b Apress, |c [2017] | ||
| 264 | 4 | |c ©2017 | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file | ||
| 347 | |b PDF | ||
| 505 | 0 | |a At a Glance; Contents; About the Author; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: Big Data, Hadoop, and HDInsight; What Is Big Data?; The Scale-Up and Scale-Out Approaches; Apache Hadoop; A Brief History of Hadoop; HDFS; MapReduce; YARN; Hadoop Cluster Components; HDInsight; The Advantages of HDInsight; Summary; Chapter 2: Provisioning an HDInsight Cluster; An Azure Subscription; Creating the First Cluster; Basic Configuration Options; Creating a Cluster Using the Azure Portal; Connecting to a Cluster Using RDP; Connecting to a Cluster Using SSH. | |
| 505 | 8 | |a Creating a Cluster Using PowerShellCreating a Cluster Using an Azure Command-Line Interface; Creating a Cluster Using .NET SDK; The Resource Manager Template; HDInsight in a Sandbox Environment; Hadoop on a Virtual Machine; Hadoop on Windows; Preparing the Host Machine; Installing and Configuring Java JDK; Installing and configuring Python 2.7.x; Download and Install HDP for Windows; Summary; Chapter 3: Working with Data in HDInsight; Azure Blob Storage; The Benefits of Blob Storage; Uploading Data; Using Azure Command-Line Interface; Using Windows PowerShell. | |
| 505 | 8 | |a Using Microsoft Azure Storage ExplorerRunning MapReduce Jobs; Using PowerShell; Using .NET SDK; Hadoop Streaming; Streaming Mapper and Reducer; Serialization with Avro Library; Data Serialization; Binary Encoding; JSON Encoding; Using Microsoft Avro Library; Summary; Chapter 4: Querying Data with Hive; Hive Essentials; Hive Architecture; Submitting a Hive Query; Using Hive View; Using Secure Shell (SSH); Using Visual Studio; Using .NET SDK; Writing HiveQL; Data Types; Create/Drop/Alter/Use Database; The Hive Table; Internal Tables; External Tables; Storage Formats; Row Formats and SerDe. | |
| 505 | 8 | |a Partitioned TablesCreate Table Options; Temporary Tables; Data Retrieval; Hive Metastore; Apache Tez; Connecting to Hive Using ODBC and Power BI; ODBC and Power BI Configuration; Prepare Data for Analysis; Creating Hive Tables; Analyzing Data Using Power BI; Hive UDFs in C#; User Defined Function (UDF); User Defined Aggregate Functions (UDAF); User Defined Tabular Functions (UDTF); Summary; Chapter 5: Using Pig with HDInsight; Understanding Relations, Bags, Tuples, and Fields; Data Types; Connecting to Pig; Operators and Commands; Executing Pig Scripts; Summary; Chapter 6: Working with HBase. | |
| 505 | 8 | |a OverviewWhere to Use HBase?; The Architecture of HBase; HBase HMaster; HRegion and HRegion Server; ZooKeeper; HBase Meta Table; Read and Write to an HBase Cluster; HFile; Major and Minor Compaction; Creating an HBase Cluster; Working with HBase; HBase Shell; Create Tables and Insert Data; HBase Shell Commands; Using .NET SDK to read/write Data; Writing Data; Reading/Querying Data; Summary; Chapter 7: Real-Time Analytics with Storm; Overview; Storm Topology; Stream Groupings; Storm Architecture; Nimbus; Supervisor Node; ZooKeeper; Worker, Executor, and Task; Creating a Storm Cluster. | |
| 520 | |a Get a jump start on using Azure HDInsight and Hadoop Ecosystem components. As most Hadoop and Big Data projects are written in either Java, Scala, or Python, this book minimizes the effort to learn another language and is written from the perspective of a .NET developer. Hadoop components are covered, including Hive, Pig, HBase, Storm, and Spark on Azure HDInsight, and code samples are written in .NET only. Processing Big Data with Azure HDInsight covers the fundamentals of big data, how businesses are using it to their advantage, and how Azure HDInsight fits into the big data world. This book introduces Hadoop and big data concepts and then dives into creating different solutions with HDInsight and the Hadoop Ecosystem. It covers concepts with real-world scenarios and code examples, making sure you get hands-on experience. The best way to utilize this book is to practice while reading. After reading this book you will be familiar with Azure HDInsight and how it can be utilized to build big data solutions, including batch processing, stream analytics, interactive processing, and storing and retrieving data in an efficient manner. What You Will Learn: Understand the fundamentals of HDInsight and Hadoop Work with HDInsight cluster Query with Apache Hive and Apache Pig Store and retrieve data with Apache HBase Stream data processing using Apache Storm Work with Apache Spark. | ||
| 630 | 0 | 0 | |a Windows Azure. |0 http://id.loc.gov/authorities/names/n2010028313 |
| 630 | 0 | 0 | |a Apache Hadoop. |0 http://id.loc.gov/authorities/names/n2013024279 |
| 630 | 0 | 7 | |a Apache Hadoop. |2 fast |0 (OCoLC)fst01911570 |
| 630 | 0 | 7 | |a Windows Azure. |2 fast |0 (OCoLC)fst01796039 |
| 650 | 0 | |a Cloud computing. |0 http://id.loc.gov/authorities/subjects/sh2008004883 | |
| 650 | 0 | |a Big data. |0 http://id.loc.gov/authorities/subjects/sh2012003227 | |
| 650 | 7 | |a COMPUTERS |x Computer Literacy. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Computer Science. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Data Processing. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Hardware |x General. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Information Technology. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Machine Theory. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Reference. |2 bisacsh | |
| 650 | 7 | |a Big data. |2 fast |0 (OCoLC)fst01892965 | |
| 650 | 7 | |a Cloud computing. |2 fast |0 (OCoLC)fst01745899 | |
| 655 | 4 | |a Electronic books. | |
| 776 | 0 | 8 | |i Printed edition: |z 9781484228685 |
| 903 | |a HeVa | ||
| 929 | |a oclccm | ||
| 999 | f | f | |i 89741520-fa6a-51a2-9f6b-f1f539057f03 |s 514493fa-8922-559b-ad6c-37e3aea6ecd8 |
| 928 | |t Library of Congress classification |a QA76.585 |l Online |c UC-FullText |u https://link.springer.com/10.1007/978-1-4842-2869-2 |z Springer Nature |g ebooks |i 12546429 | ||