Hadoop Ecosystem: Hadoop Tools for Crunching Big Data Problems

Hadoop is a framework with many modules which are supported by a greater ecosystem of technologies. Whereas, the Hadoop Ecosystem is a platform that will provide many services for solving greater issues.

Hadoop is so popular in Big Data analytics because it is very cost-effective when compared to traditional database management systems. Also, Hadoop is faster in providing a distributed file system. It will enable flexibility in accessing and processing data compared to traditional systems.

Apache Spark will fit into the Hadoop open-source community. You have to build it on top of the HDFS. But, Spark will not be tied to a two-stage MapReduce and this results in providing excellent performances quicker than the Hadoop MapReduce.

There are six stages in data processing which are as follows:

• Data Collection
• Data Preparation
• Data Input
• Data Processing
• Data Interpretation / Output
• Data Storage

Here are some challenges that you may face while handling Big Data:

• Not fully able to understand massive data
• Unable to find qualified professionals
• Misassumption in choosing the right Big Data tool
• Issues with growing data
• Security of the data
• Integration of data from different sources

Spark is an enhancement of Hadoop’s MapReduce. The major difference is Spark retains the data in memory while following further processes. And on the contrary MapReduce processes data on the disk. This makes data processing of Spark faster than MapReduce.