Big Data is synonymous with Hadoop. But what is Hadoop? Hadoop is an open source software managed by the Apache Software Foundation.
Here's the problem of traditional DB's.
Our SQL databases are a nice concept but my database is now getting enormous. Lets buy an even more powerful computer to handle all of this stuff Wait more powerful computers are expensive. But what if I just bought a ton of small computers instead.
That's what Hadoop is best at. Hadoop actually allows you to store a database across multiple servers. This means :
The HDFS is a subsystem of Hadoop. It is a distributed file system, i.e. it can be spread across many servers. It is highly fault-tolerant, i.e. it has a lot of copies of the files. It relies on a centralized Master Slave Architecture, i.e. the master computer controls all the other servers.
MapReduce basically handles all the distribution of tasks across the database. The framework takes care of scheduling tasks, monitoring them and re-executing any failed tasks. According to Apache MapReduce is used to split the input data set into independent chunks that are processed in a completely parallel manner. The Hadoop MapReduce framework sorts the outputs of the maps, which are then input to the reduce tasks.
Hadoop has a whole bunch of subprojects under it. These allow you handle and manipulate data in various ways and include:
Hive : Built for those familiar with traditional SQL databases. HiveQL allows you to select from tables in a similar way.
Pig : High Level platform for creating MapReduce jobs. Basically a programming language that allows you to do your jobs.
Spark : Open source task handler that works on top of HDFS. It is an alternative to the MapReduce paradigm and has proven to be a 100 times faster for certain tasks
and tons of others.
Hadoop's speed and flexibility represents databases of the future. I'm dying to learn more about it.