Why to go with hadoop ...?
We all know today's real time transactions, social media and other internet transaction increasing the data size means Big Data is generated rapidly. As the size of data is increased the main problem is PROCESSING that data not storing. To deal with this problem Hadoop is one of the better solutions, Hadoop gives fast processing by solving query in distributed manner. In Hadoop we don't pass data to the query machine instead of that we pass our query to that machine where appropriate(required) data is stored/located. that's why response time is reduced and we get our work done fast.
Hadoop is an open source.
Main Features:
1.Large Data Sets – MapReduce paired with HDFS is a successful solution for storing large volumes of unstructured and structured data.
2. Scalable: Hadoop scales linearly to handle larger data by adding more nodes to the cluster.
3. Robust: As Hadoop runs on commodity hardware, its architecture is designed with the assumption of frequent hardware failures. It can easily handle most such failures.
4.Simple: In Hadoop it is simple to write parallel running codes.
We all know today's real time transactions, social media and other internet transaction increasing the data size means Big Data is generated rapidly. As the size of data is increased the main problem is PROCESSING that data not storing. To deal with this problem Hadoop is one of the better solutions, Hadoop gives fast processing by solving query in distributed manner. In Hadoop we don't pass data to the query machine instead of that we pass our query to that machine where appropriate(required) data is stored/located. that's why response time is reduced and we get our work done fast.
Hadoop is an open source.
Main Features:
1.Large Data Sets – MapReduce paired with HDFS is a successful solution for storing large volumes of unstructured and structured data.
2. Scalable: Hadoop scales linearly to handle larger data by adding more nodes to the cluster.
3. Robust: As Hadoop runs on commodity hardware, its architecture is designed with the assumption of frequent hardware failures. It can easily handle most such failures.
4.Simple: In Hadoop it is simple to write parallel running codes.
