Apache Sqoop Cookbook: Unlocking Hadoop for Your Relational - download pdf or read online

By Kathleen Ting, Jarek Jarcec Cecho

Integrating information from a number of resources is vital within the age of huge information, however it could be a difficult and time-consuming job. this useful cookbook presents dozens of ready-to-use recipes for utilizing Apache Sqoop, the command-line interface program that optimizes information transfers among relational databases and Hadoop. Sqoop is either strong and bewildering, yet with this cookbook's problem-solution-discussion layout, you will speedy how you can install after which observe Sqoop on your surroundings. The authors offer MySQL, Oracle, and PostgreSQL database examples on GitHub so you might simply adapt for SQL Server, Netezza, Teradata, or different relational platforms.

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Extra info for Apache Sqoop Cookbook: Unlocking Hadoop for Your Relational Database

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Info Solution Sqoop offers two ways to run jobs from within the metastore without requiring any user input. The first and more secure method is by using the parameter --password-file to pass in the file containing the password. xml to true: ... password true Discussion Both available methods have their advantages and disadvantages. While using the pass‐ word file is considered safer, in order to secure it, you need to restrict access to the password file.

Info | 29 Discussion Saved jobs can be customized at execution time. This functionality is not limited to adding new parameters like --verbose (used to get more insight into what the job is doing). You can override any arbitrary parameter to check how the job with the new settings will behave without modifying the saved job itself. Another handy use case is to temporarily change the destination in HDFS or in the Hive table if you need an extra import of data to do some unscheduled investigation or analysis.

There is a lot to be aware of when using free-form query imports. By using query im‐ ports, Sqoop can’t use the database catalog to fetch the metadata. This is one of the reasons why using table import might be faster than the equivalent free-form query import. Also, you have to manually specify some additional parameters that would otherwise be populated automatically. info to specify the --split-by parameter with the column that should be used for slicing your data into multiple parallel tasks.

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