Hadoop Hbase is a column-oriented database management system that runs on top of HDFS. It is well suited for sparse data sets, which are common in many big data use cases. An HBase system comprises a set of tables. Each table contains rows and columns, much like a traditional database. Each table must have an element defined as a Primary Key, and all access attempts to HBase tables must use this Primary Key. HBase allows for many attributes to be grouped together into what are known as column families, such that the elements of a column family are all stored together. This is different from a row-oriented relational database, where all the columns of a given row are stored together. HBase is very flexible and therefore able to adapt to changing application requirements. HBase is built on concepts similar to those of MapReduce and HDFS (NameNode and slave nodes). In HBase a master node manages the cluster and region servers store portions of the tables and perform the work on the data. In the same way HDFS has some enterprise concerns due to the availability of the NameNode, HBase is also sensitive to the loss of its master node.
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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