Wednesday, 23 October 2013

Why do I Need Hadoop

Too Much Data



Hadoop provides storage for Big Data at reasonable cost

Storing Big Data using traditional storage can be expensive. Hadoop is built around commodity hardware. Hence it can provide fairly large storage for a reasonable cost. Hadoop has been used in the field at Peta byte scale.


Hadoop allows to capture new or more data

Some times organizations don't capture a type of data, because it was too cost prohibitive to store it. Since Hadoop provides storage at reasonable cost, this type of data can be captured and stored.

One example would be web site click logs. Because the volume of these logs can be very high, not many organizations captured these. Now with Hadoop it is possible to capture and store the logs


With Hadoop, you can store data longer

To manage the volume of data stored, companies periodically purge older data. For example only logs for the last 3 months could be stored and older logs were deleted. With Hadoop it is possible to store the historical data longer. This allows new analytics to be done on older historical data.

For example, take click logs from a web site. Few years ago, these logs were stored for a brief period of time to calculate statics like popular pages ..etc. Now with Hadoop it is viable to store these click logs for longer period of time.


Hadoop provides scalable analytics

There is no point in storing all the data, if we can't analyze them. Hadoop not only provides distributed storage, but also distributed processing as well. Meaning we can crunch a large volume of data in parallel. The compute framework of Hadoop is called Map Reduce. Map Reduce has been proven to the scale of peta bytes.

Hadoop provides rich analytics

Native Map Reduce supports Java as primary programming language. Other languages like Ruby, Python and R can be used as well.

Of course writing custom Map Reduce code is not the only way to analyze data in Hadoop. Higher level Map Reduce is available. For example a tool named Pig takes english like data flow language and translates them into Map Reduce. Another tool Hive, takes SQL queries and runs them using Map Reduce.

Business Intelligence (BI) tools can provide even higher level of analysis. Quite a few BI tools can work with Hadoop and analyze data stored in Hadoop.

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