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What is Hadoop?
- Open source data storage and processing API.
- Hadoop is reliable and fault tolerant with no rely on hardware for these properties.
- It is made by apache software foundation in 2011. written in JAVA
Hadoop Project Assignment Help
- Task Scheduling
- Data Rebalancing
- Hadoop Scheduler for Hetrogenous Resources
- Dynamic Workload Balancing
Features of Hadoop:
- Distributed Storage
- Fault Tolerance
- Horizontal Scalability
- Open Source
- Commodity Hardware
- 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
|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|