July 2013
Intermediate to advanced
94 pages
1h 52m
English
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Preface
Whether moving a small collection of personal vacation photos between applications or moving petabytes of data between corporate warehouse systems, integrating data from multiple sources remains a struggle. Data storage is more accessible thanks to the availability of a number of widely used storage systems and accompanying tools. Core to that are relational databases (e.g., Oracle, MySQL, SQL Server, Teradata, and Netezza) that have been used for decades to serve and store huge amounts of data across all industries.
Relational database systems often store valuable data in a company. If made available, that data can be managed and processed by Apache Hadoop, which is fast becoming the standard for big data processing. Several relational database vendors championed developing integration with Hadoop within one or more of their products.
Transferring data to and from relational databases is challenging and laborious. Because data transfer requires careful handling, Apache Sqoop, short for “SQL to Hadoop,” was created to perform bidirectional data transfer between Hadoop and almost any external structured datastore. Taking advantage of MapReduce, Hadoop’s execution engine, Sqoop performs the transfers in a parallel manner.
If you’re reading this book, you may have some prior exposure to Sqoop—especially from Aaron Kimball’s Sqoop section in Hadoop: The Definitive Guide by Tom White (O’Reilly) or from Hadoop Operations by Eric Sammer (O’Reilly).
From that exposure, you’ve seen how ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.