It is essential that you try to help yourself to figure out “Why understanding Hadoop is complex than any other database?”
Let’s make the pathway simple by comparing Hadoop against Oracle. Just, reverse our views on Hadoop and Oracle and find which is simple to explore.What if, we imagine Hadoop in terms of only Hive (a simple data querying tool) or what if think of Oracle in terms of how it stores data or how it performs read/write operation?
Now give a try again, which is now easy to explore? Surprisingly the answer is Hadoop, provided we are going to consider only the querying capabilities of Hive. Oracle can turn into a tough subject to explore, when you try to relate the data stored in Oracle with backend files and its blocksizes!!
How many of us knows that Oracle also stores the data in form of files like Hadoop? We don’t dig deep into the architecture part of Oracle, and keep it simple to use. For Reference: Inquire physical structure,logical storage structure and default file system of Oracle in https://docs.oracle.com/cd/E11882_01/server.112/e40540/logical.htm#CNCPT301, https://docs.oracle.com/cd/E11882_01/server.112/e40540/physical.htm#CNCPT003 for more details.
Now, let’s get back to the track. Understanding Hadoop takes a long way because,
Hadoop revolves around the core concepts like file storage systems,read/write operations,size of a block etc.
We can simplify development tasks by making use of provisions like defining our own user defined functions using a programming language.
Understanding architecture with Hadoop daemons and examining the frameworks like MapReduce,Spark etc are equally important as using Hadoop tools.
These all make the Hadoop journey longer than Oracle!!