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Apache Sqoop Cookbook: Unlocking Hadoop for Your Relational Database - Kathleen Ting, Jarek Jarcec Cecho
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Kathleen Ting, Jarek Jarcec Cecho:

Apache Sqoop Cookbook: Unlocking Hadoop for Your Relational Database - Paperback

2013, ISBN: 1449364624

[SR: 1006630], Paperback, [EAN: 9781449364625], O'Reilly Media, O'Reilly Media, Book, [PU: O'Reilly Media], 2013-07-23, O'Reilly Media, Integrating data from multiple sources is essential in the age of big data, but it can be a challenging and time-consuming task. This handy cookbook provides dozens of ready-to-use recipes for using Apache Sqoop, the command-line interface application that optimizes data transfers between relational databases and Hadoop.Sqoop is both powerful and bewildering, but with this cookbook’s problem-solution-discussion format, you’ll quickly learn how to deploy and then apply Sqoop in your environment. The authors provide MySQL, Oracle, and PostgreSQL database examples on GitHub that you can easily adapt for SQL Server, Netezza, Teradata, or other relational systems.Transfer data from a single database table into your Hadoop ecosystemKeep table data and Hadoop in sync by importing data incrementallyImport data from more than one database tableCustomize transferred data by calling various database functionsExport generated, processed, or backed-up data from Hadoop to your databaseRun Sqoop within Oozie, Hadoop’s specialized workflow schedulerLoad data into Hadoop’s data warehouse (Hive) or database (HBase)Handle installation, connection, and syntax issues common to specific database vendors, Q&A with Kathleen Ting and Jarek Jarcec Cecho, author of "Apache Sqoop Cookbook" Q. What makes this book important right now? A. Hadoop has quickly become the standard for processing and analyzing Big Data. In order to integrate a new Hadoop deployment into your existing environment, you will need to transfer data stored in relational databases into Hadoop. Sqoop optimizes data transfers between Hadoop and databases with a command line interface listing 60 parameters. In this book, we'll focus on applying the parameters in common use cases to help you deploy and use Sqoop in your environment. Q. What do you hope that readers of your book will walk away with? A. One recipe at a time, this book guides you from basic commands not requiring prior Sqoop knowledge all the way to very advanced use cases. These recipes are detailed enough not only to enable you to deploy them within your environment but also to understand Sqoop's inner workings. Q. Can you give us a little taste of the contents? A. Imagine a scenario where you are incrementally importing records from MySQL into Hadoop. When you resume importing and noticing that some records have been modified, you also want to include those updated records. How do you drop the older copies of records when records have been updated and then merge in the newer copies? This sounds like a use-case for using the lastmodified incremental mode. Internally, the lastmodified import consists of two standalone MapReduce jobs. The first job will import the delta of changed data similarly to the way normal import does. This import job will save data in a temporary directory on HDFS. The second job will take both the old and new data and will merge them together into the final output, preserving only the last updated value for each row. Here's an example: sqoop import \\ --connect jdbc:mysql://mysql.example.com/sqoop \\ --username sqoop \\ --password sqoop \\ --table visits \\ --incremental lastmodified \\ --check-column last_update_date \\ --last-value "2013-05-22 01:01:01", 3789, Relational Databases, 549646, Databases & Big Data, 5, Computers & Technology, 1000, Subjects, 283155, Books, 10806617011, Storage & Retrieval, 377894011, Network Administration, 3652, Networking & Cloud Computing, 5, Computers & Technology, 1000, Subjects, 283155, Books, 491306, Database Storage & Design, 468204, Computer Science, 465600, New, Used & Rental Textbooks, 2349030011, Specialty Boutique, 283155, Books

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Apache Sqoop Cookbook: Unlocking Hadoop for Your Relational Database - Kathleen Ting, Jarek Jarcec Cecho
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Kathleen Ting, Jarek Jarcec Cecho:

Apache Sqoop Cookbook: Unlocking Hadoop for Your Relational Database - Paperback

2013, ISBN: 1449364624

[SR: 1006630], Paperback, [EAN: 9781449364625], O'Reilly Media, O'Reilly Media, Book, [PU: O'Reilly Media], 2013-07-23, O'Reilly Media, Integrating data from multiple sources is essential in the age of big data, but it can be a challenging and time-consuming task. This handy cookbook provides dozens of ready-to-use recipes for using Apache Sqoop, the command-line interface application that optimizes data transfers between relational databases and Hadoop.Sqoop is both powerful and bewildering, but with this cookbook’s problem-solution-discussion format, you’ll quickly learn how to deploy and then apply Sqoop in your environment. The authors provide MySQL, Oracle, and PostgreSQL database examples on GitHub that you can easily adapt for SQL Server, Netezza, Teradata, or other relational systems.Transfer data from a single database table into your Hadoop ecosystemKeep table data and Hadoop in sync by importing data incrementallyImport data from more than one database tableCustomize transferred data by calling various database functionsExport generated, processed, or backed-up data from Hadoop to your databaseRun Sqoop within Oozie, Hadoop’s specialized workflow schedulerLoad data into Hadoop’s data warehouse (Hive) or database (HBase)Handle installation, connection, and syntax issues common to specific database vendors, Q&A with Kathleen Ting and Jarek Jarcec Cecho, author of "Apache Sqoop Cookbook" Q. What makes this book important right now? A. Hadoop has quickly become the standard for processing and analyzing Big Data. In order to integrate a new Hadoop deployment into your existing environment, you will need to transfer data stored in relational databases into Hadoop. Sqoop optimizes data transfers between Hadoop and databases with a command line interface listing 60 parameters. In this book, we'll focus on applying the parameters in common use cases to help you deploy and use Sqoop in your environment. Q. What do you hope that readers of your book will walk away with? A. One recipe at a time, this book guides you from basic commands not requiring prior Sqoop knowledge all the way to very advanced use cases. These recipes are detailed enough not only to enable you to deploy them within your environment but also to understand Sqoop's inner workings. Q. Can you give us a little taste of the contents? A. Imagine a scenario where you are incrementally importing records from MySQL into Hadoop. When you resume importing and noticing that some records have been modified, you also want to include those updated records. How do you drop the older copies of records when records have been updated and then merge in the newer copies? This sounds like a use-case for using the lastmodified incremental mode. Internally, the lastmodified import consists of two standalone MapReduce jobs. The first job will import the delta of changed data similarly to the way normal import does. This import job will save data in a temporary directory on HDFS. The second job will take both the old and new data and will merge them together into the final output, preserving only the last updated value for each row. Here's an example: sqoop import \\ --connect jdbc:mysql://mysql.example.com/sqoop \\ --username sqoop \\ --password sqoop \\ --table visits \\ --incremental lastmodified \\ --check-column last_update_date \\ --last-value "2013-05-22 01:01:01", 3789, Relational Databases, 549646, Databases & Big Data, 5, Computers & Technology, 1000, Subjects, 283155, Books, 10806617011, Storage & Retrieval, 377894011, Network Administration, 3652, Networking & Cloud Computing, 5, Computers & Technology, 1000, Subjects, 283155, Books, 491306, Database Storage & Design, 468204, Computer Science, 465600, New, Used & Rental Textbooks, 2349030011, Specialty Boutique, 283155, Books

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Integrating data from multiple sources is essential in the age of big data, but it can be a challenging and time-consuming task. This handy cookbook provides dozens of ready-to-use recipes for using Apache Sqoop, the command-line interface application that optimizes data transfers between relational databases and Hadoop. Sqoop is both powerful and bewildering, but with this cookbook's problem-solution-discussion format, you'll quickly learn how to deploy and then apply Sqoop in your environment. The authors provide MySQL, Oracle, and PostgreSQL database examples on GitHub that you can easily adapt for SQL Server, Netezza, Teradata, or other relational systems. Transfer data from a single database table into your Hadoop ecosystem Keep table data and Hadoop in sync by importing data incrementally Import data from more than one database table Customize transferred data by calling various database functions Export generated, processed, or backed-up data from Hadoop to your database Run Sqoop within Oozie, Hadoop's specialized workflow scheduler Load data into Hadoop's data warehouse (Hive) or database (HBase) Handle installation, connection, and syntax issues common to specific database vendors Computers Computers eBook, O'Reilly Media

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2013, ISBN: 9781449364625

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Details of the book
Apache Sqoop Cookbook
Author:

Ting, Kathleen / Cecho, Jarek Jarcec

Title:

Apache Sqoop Cookbook

ISBN:

1449364624

Integrating data from multiple sources is essential in the age of big data, but it can be a challenging and time-consuming task. This handy cookbook provides dozens of ready-to-use recipes for using Apache Sqoop, the command-line interface application that optimizes data transfers between relational databases and Hadoop.

Sqoop is both powerful and bewildering, but with this cookbook’s problem-solution-discussion format, you’ll quickly learn how to deploy and then apply Sqoop in your environment. The authors provide MySQL, Oracle, and PostgreSQL database examples on GitHub that you can easily adapt for SQL Server, Netezza, Teradata, or other relational systems.

  • Transfer data from a single database table into your Hadoop ecosystem
  • Keep table data and Hadoop in sync by importing data incrementally
  • Import data from more than one database table
  • Customize transferred data by calling various database functions
  • Export generated, processed, or backed-up data from Hadoop to your database
  • Run Sqoop within Oozie, Hadoop’s specialized workflow scheduler
  • Load data into Hadoop’s data warehouse (Hive) or database (HBase)
  • Handle installation, connection, and syntax issues common to specific database vendors,

    Q&A with Kathleen Ting and Jarek Jarcec Cecho, author of "Apache Sqoop Cookbook"

    Q. What makes this book important right now?

    A. Hadoop has quickly become the standard for processing and analyzing Big Data. In order to integrate a new Hadoop deployment into your existing environment, you will need to transfer data stored in relational databases into Hadoop. Sqoop optimizes data transfers between Hadoop and databases with a command line interface listing 60 parameters. In this book, we'll focus on applying the parameters in common use cases to help you deploy and use Sqoop in your environment.

    Q. What do you hope that readers of your book will walk away with?

    A. One recipe at a time, this book guides you from basic commands not requiring prior Sqoop knowledge all the way to very advanced use cases. These recipes are detailed enough not only to enable you to deploy them within your environment but also to understand Sqoop's inner workings.

    Q. Can you give us a little taste of the contents?

    A. Imagine a scenario where you are incrementally importing records from MySQL into Hadoop. When you resume importing and noticing that some records have been modified, you also want to include those updated records. How do you drop the older copies of records when records have been updated and then merge in the newer copies?

    This sounds like a use-case for using the lastmodified incremental mode. Internally, the lastmodified import consists of two standalone MapReduce jobs. The first job will import the delta of changed data similarly to the way normal import does. This import job will save data in a temporary directory on HDFS. The second job will take both the old and new data and will merge them together into the final output, preserving only the last updated value for each row.

    Here's an example:

    sqoop import \\

    --connect jdbc:mysql://mysql.example.com/sqoop \\

    --username sqoop \\

    --password sqoop \\

    --table visits \\

    --incremental lastmodified \\

    --check-column last_update_date \\

    --last-value "2013-05-22 01:01:01"

Details of the book - Apache Sqoop Cookbook


EAN (ISBN-13): 9781449364625
ISBN (ISBN-10): 1449364624
Hardcover
Paperback
Publishing year: 2013
Publisher: O'Reilly Vlg. GmbH & Co.
75 Pages
Weight: 0,176 kg
Language: Englisch

Book in our database since 26.02.2008 20:48:15
Book found last time on 07.02.2017 11:30:12
ISBN/EAN: 1449364624

ISBN - alternate spelling:
1-4493-6462-4, 978-1-4493-6462-5

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