Hadoop is an open-source framework that allows to store and
process Big Data in a distributed environment across clusters of computer using
simple programming models. Core components of Hadoop are HDFS and MapReduce.
HDFS is basically used to store large data sets and MapReduce is used to
process such large data sets.
Hadoop can be run in one of three modes:
- 1 Standalone (or local) mode
- 2 Pseudo-distributed mode
- 3 Fully distributed mode
A lot of companies are using Big Data Hadoop technology such
as Cloudera, EMC, MapR, Hortonworks, Amazon, Facebook, eBay, Twitter, Google
and so on, to capture, store, process, retrieve and analyze huge data base.
Facebook is generating 500+ terabytes of data per day, NYSE (New York Stock
Exchange) generates about 1 terabyte of new trade data per day, and a jet
airline collects 10 terabytes of censor data for every 30 minutes of flying
time. All these are day to day examples of Big Data Hadoop.
According to IBM, the three characteristics of Big Data are:
Volume: Facebook generating 500+ terabytes of data per day. Velocity: Analyzing
2 million records each day to identify the reason for losses. Variety: images,
audio, video, sensor data, log files, etc.
Advantage
of Big data Hadoop Technology:
- Capable to store enormous data of any kind of data.
- Highly Scalable
- Fault tolerance
- flexible
- Cost Effective
- Better Operational Efficiency
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