Neuware - 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.

Bestandsnummer des Verk�ufers 9781449364625

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Bibliografische Details

Titel: Apache Sqoop Cookbook : Unlocking Hadoop for Your Relational Database
Verlag: O'reilly Media Aug 2013
Erscheinungsjahr: 2013
Sprache: Englisch
ISBN-10: 1449364624
ISBN-13: 9781449364625
Einband: Taschenbuch
Zustand: Neu

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Inhaltsangabe

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

�ber die Autorin bzw. den Autor

Kathleen Ting is currently a Customer Operations Engineering Manager at Cloudera where she helps customers deploy and use the Hadoop ecosystem in production. She has spoken on Hadoop, ZooKeeper, and Sqoop at many Big Data conferences including Hadoop World, ApacheCon, and OSCON. She's contributed to several projects in the open source community and is a Committer and PMC Member on Sqoop. Jarek Jarcec Cecho is currently a Software Engineer at Cloudera where he develops software to help customers better access and integrate with the Hadoop ecosystem. He has led the Sqoop community in the architecture of the next generation of Sqoop, known as Sqoop 2. He's contributed to several projects in the open source community and is a Committer and PMC Member on Sqoop, Flume, and MRUnit.

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