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Which Apache Hadoop component can potentially replace an RDBMS as a large Hadoop datastore and is particularly good for "sparse data"?

A. MapReduce
B. HBase
C. Spark
D. Ambari

User KostasRim
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.Final answer:

HBase is the Apache Hadoop component that can potentially replace an RDBMS as a large Hadoop datastore and is particularly good for sparse data.

Step-by-step explanation:

The Apache Hadoop component that can potentially replace an RDBMS as a large Hadoop datastore and is particularly good for sparse data is HBase.

HBase is a distributed, column-oriented database management system built on top of Hadoop, which provides random access to vast amounts of structured and semi-structured data. It is designed for high scalability and fault-tolerance and is well-suited for storing sparse data, where many cells in a table may be empty.

For example, in a traditional RDBMS, if a column has no value for a particular row, it still takes up space in the table. In HBase, the sparse nature of the data allows for efficient storage and retrieval, since only non-empty cells are stored

User Richard Neish
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