There is so much info on Hadoop out there available on internet but still there seems to be some misapprehensions or rather a lack of clearness among some specialists and their counterparts, as to where it fits into the complete Big Data landscape.
Let’s go ahead and show up some of the widespread myths about Hadoop.
Hive be like SQL
People who work on SQL can rapidly catch up with Hive. Hive look like SQL, but is not of SQL standard. Over the time, it is supposed that Hadoop products will provision standard SQL and SQL based vendor tools will support Hadoop.
Hadoop needs MapReduce
Hadoop and MapReduce are connected, but they are not married to each other. Saying this, they are not equally exclusive to each other. There are some differences of MapReduce that work with a diversity of storage technologies that comprises HDFS and some interactive DBMSs. Some users opt to organize HDFS with Hive or HBase, but not MapReduce.
Hadoop is a single solution
This is the major myth of all! Hadoop has a variety of open source products like – HDFS (Hadoop Distributed File System), Ambari, MapReduce, Mahout, PIG, Hive, HBase Flume and HCatalog. This is just the tip of the iceberg and there is further to it. So, principally Hadoop is an ecosystem.
Hadoop requires a bunch of programmers
This completely depends on what the association plans to do. If the strategy is to build a fancy Hadoop based Big Data set, then computer programmer come into picture. If not, then programming should not be an apprehension at all, as most data incorporation tools have GUIs that abstract MapReduce programming difficulty and pre-built templates.
Hadoop is a Database
Hadoop is not a database nor a spare for any database system. Hadoop is chiefly a distributed file system and doesn’t comprise database features like request optimization, indexing and haphazard access to data. Though, Hadoop can be facilitated to build a database system.
Hadoop can only lever web analytics
When it comes to Hadoop, Web Analytics is emphasized as most of the businesses use it for analysing web logs and other web data. But, its application is not incomplete to web analytics without help. Hadoop can handle a broader range of data and analytics interesting to broader range of organizations.
Big Data can do without Hadoop
When we say Big Data, then instantaneous thing that comes to attention is Hadoop, in-spite of other choices available in the marketplace. Consequently, when dealing with Big Data, there has to be Hadoop.
MapReduce only controls analytics.
MapReduce levers parallel programming, liability tolerance of wide variability of coded logics and other applications, then just analytics.
Hadoop is cheap
This is the most communal misapprehension with anything that is open source – either it is free or cheap. One requests to make wise decisions on their economic condition to make use of all the verticals of Hadoop Training. Just for the reason that it’s a free software doesn’t mean it inexpensive or free.