Abstract
Previous chapters explored how to leverage an HDInsight cluster to store and process big data. You learned how MapReduce jobs process data. Also, you looked at Hive and Pig, and learned how they make it easy to work with data. All the technologies and tools that you saw so far work in batch mode. And they are accepted in online analytical processing (OLAP) scenarios where it is supposed to take time. But you cannot always use batch processing. What if you want a low-latency database that provides near real-time read/write access, and quick random access to your big data in Hadoop? This is where Apache HBase comes into the picture.
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© 2017 Vinit Yadav
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Yadav, V. (2017). Working with HBase. In: Processing Big Data with Azure HDInsight. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-2869-2_6
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DOI: https://doi.org/10.1007/978-1-4842-2869-2_6
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Publisher Name: Apress, Berkeley, CA
Print ISBN: 978-1-4842-2868-5
Online ISBN: 978-1-4842-2869-2
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