Apache Hadoop is a collection of open-source software utilities that facilitates using a network of many computers to solve problems involving massive amounts of data and computation.
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14 Facts About Hadoop
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2.
Hadoop was originally designed for computer clusters built from commodity hardware, which is still the common use.
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3.
All the modules in Hadoop are designed with a fundamental assumption that hardware failures are common occurrences and should be automatically handled by the framework.
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4.
Core of Apache Hadoop consists of a storage part, known as Hadoop Distributed File System, and a processing part which is a MapReduce programming model.
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5.
Term Hadoop is often used for both base modules and sub-modules and the ecosystem, or collection of additional software packages that can be installed on top of or alongside Hadoop, such as Apache Pig, Apache Hive, Apache HBase, Apache Phoenix, Apache Spark, Apache ZooKeeper, Cloudera Impala, Apache Flume, Apache Sqoop, Apache Oozie, and Apache Storm.
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6.
The very first design document for the Hadoop Distributed File System was written by Dhruba Borthakur in 2007.
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7.
Small Hadoop cluster includes a single master and multiple worker nodes.
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When Hadoop MapReduce is used with an alternate file system, the NameNode, secondary NameNode, and DataNode architecture of HDFS are replaced by the file-system-specific equivalents.
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Hadoop distributed file system is a distributed, scalable, and portable file system written in Java for the Hadoop framework.
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Hadoop cluster has nominally a single namenode plus a cluster of datanodes, although redundancy options are available for the namenode due to its criticality.
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11.
When Hadoop is used with other file systems, this advantage is not always available.
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12.
Also, Hadoop 3 permits usage of GPU hardware within the cluster, which is a very substantial benefit to execute deep learning algorithms on a Hadoop cluster.
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13.
Theoretically, Hadoop could be used for any workload that is batch-oriented rather than real-time, is very data-intensive, and benefits from parallel processing.
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14.
Hadoop can be deployed in a traditional onsite datacenter as well as in the cloud.
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