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What is Hadoop?

  1. Open source data storage and processing API.
  2. Hadoop is reliable and fault tolerant with no rely on hardware for these properties.
  3. It is made by apache software foundation in 2011. written in JAVA

Hadoop Project Assignment Help

  1. MapReduce
  2. Task Scheduling
  3. HDFS
  4. Data Rebalancing
  5. Hadoop Scheduler for Hetrogenous Resources
  6. Dynamic Workload Balancing

Features of Hadoop:

  1. Distributed Storage
  2. Fault Tolerance
  3. Horizontal Scalability
  4. Open Source
  5. Commodity Hardware
  6. Parallel Processing

Finding the history of hadoop online service to Hadoop Assignment Help:

Features of Hadoop :

The following are the salient features of Hadoop :

1) Velocity: Velocity signifies speed. So, to use big data as a tool of data analytics, the unprocessed data must be streamed in the fastest possible speed.

2) Volume: Volume definitely points towards the size of data. Organisations that have huge data that is derived from years of storage and unstructured streaming, big data is the only solution.

3)Variety: Big Data analytics files can be stored in a variety of formats. It can be kept and viewed as an audio file, video file, text file and email file etc.

4) Complexity: Big Data Analytics is the big name in the field of data analytics in the current scenario. It is best suited to perform complex analysis of data. It is mandatory for the organisation implementing big data in their system for analysis of unprocessed data to also use the suitable application to deal with big data and its complexity.

5) Variability: Big data flows through variable formats like an audio file, video file, text file and email file etc it may also result in changeability of the data.

Difference between hapdoop mapreduce, pig and hive

Hadoop Mapreduce
Compiled language
Sql like query language
Scripting language
Lower level of abstraction
Highter level of abstruction
Highter level of abstraction
More lines of code
Comparatively less line of code than   mapreduce and apache pig
Comparatively less lines of code than   mapreduce
Code efficiency is high when compared to   pig and hive.
Code efficiency is relatively less
Code efficiency is relatively less