The major of the new features in Apache Hadoop 3:
Java support in Hadoop 3.0 is the minimum version of the JDK 8.0:-
All the jar files have been compiled with Java 8 times. The consumer now wants Java 8 to be enabled in Hadoop 3.0. And the JDK 7 user must upgrade to JDK 8.
HDFS Erasure Coding supports:-
Hadoop 3.x uses fault-tolerance erasing code. Hadoop 2.x uses the same fault tolerance by replication methodology.
Service YARN Timeline v.2:-
Hadoop 3 new to the Yarn Timeline program. The Timeline server stores and retrieves the current and historical application data.
Help to opportunistic and distributed containers:-
The execution form definition has been implemented in Hadoop 3. If resources are not available, those packages are waiting in the Node Manager at this time. Chances are smaller than expected containers for containers. If the promised containers are to meet halfway between the containers, they will be expanded later. That will leave the containers assured.
Multi-service default ports:-
A lot of the Hadoop services had their default ports in the ephemeral Linux port set before Hadoop 3.0.
Such providers have switched their default port out of the restricted range. Name Node, Secondary Name Node, Data Node, and Key Management Server are the utilities.
The minimum supported version of Java:-
It is the minimum supported version of java is java 8.
It can be handled by Erasure coding.
Intra-data node balancer is used in data balancing, named by the HDFS disk balancer CLI.
HDFS erasure encoding support.
The overhead storage:-
There will be 9 blocks for comparison if there are 6 blocks, and there will be 6 blocks.
Serviced by YARN Timeline:-
The timeline v2 support is improved and timeline reliability and scalability are enhanced.
The ports were relocated beyond the control of the ephemeral terminal.
Tools are available:-
Hive, Pig, Tez, Hama, and Giraph.
Few individuals had the experience of winning the war and becoming a master quickly. In addition, a strong compensation package can be found for Hadoop and Big Data Courses in India and abroad.