Hadoop Sqoop efficiently transfers bulk data between Apache Hadoop and structured datastores such as relational databases. Sqoop helps offload certain tasks (such as ETL processing) from the EDW to Hadoop for efficient execution at a much lower cost. Sqoop can also be used to extract data from Hadoop and export it into external structured datastores. Sqoop works with relational databases such as Teradata, Netezza, Oracle, MySQL, Postgres, and HSQLDB. Sqoop does the following to integrate bulk data movement between Hadoop and structured datastores: Import sequential datasets from a mainframe, parallel data transfer, fast data copies, efficient data analysis, load balancing.
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Recommended reading list:
|Hadoop: The Definitive Guide: Storage and Analysis at Internet Scale
Get ready to unlock the power of your data. With the fourth edition of this comprehensive guide, you’ll learn how to build and maintain reliable, scalable, distributed systems with Apache Hadoop. This book is ideal for programmers looking to analyze datasets of any size, and for administrators who want to set up and run Hadoop clusters.
Using Hadoop 2 exclusively, author Tom White presents new chapters on YARN and several Hadoop-related projects such as Parquet, Flume, Crunch, and Spark. You’ll learn about recent changes to Hadoop, and explore new case studies on Hadoop’s role in healthcare systems and genomics data processing.
Learn fundamental components such as MapReduce, HDFS, and YARN
Explore MapReduce in depth, including steps for developing applications with it
Set up and maintain a Hadoop cluster running HDFS and MapReduce on YARN
Learn two data formats: Avro for data serialization and Parquet for nested data
Use data ingestion tools such as Flume (for streaming data) and Sqoop (for bulk data transfer)
Understand how high-level data processing tools like Pig, Hive, Crunch, and Spark work with Hadoop
Learn the HBase distributed database and the ZooKeeper distributed configuration service