Features of Hadoop

1. Fault-efficient scalable, flexible and modular design:

• uses simple and modular programming model.
• The system provides servers at high scalability. The system is scalable by adding new nodes to handle larger data.
• Hadoop proves very helpful in storing, managing, processing and analyzing Big Data.
• Modular functions make the system flexible.
• One can add or replace components at ease.
• Modularity allows replacing its components for a different software tool.

2. Robust design of HDFS:

• Execution of Big Data applications continue even when an individual server or cluster fails.
• This is because of Hadoop provisions for backup (due to replications at least three times for each data block) and a data recovery mechanism.
• HDFS thus has high reliability.

3. Store and process Big Data:

• Processes Big Data of 3V characteristics.

4. Distributed clusters computing model with data locality :

• Processes Big Data at high speed as the application tasks and sub-tasks submit to the DataNodes.
• One can achieve more computing power by increasing the number of computing nodes.
• The processing splits across multiple DataNodes (servers), and thus fast processing and aggregated results.

5. Hardware fault-tolerant:

• A fault does not affect data and application processing. If a node goes down, the other nodes take care of the residue.
• This is due to multiple copies of all data blocks which replicate automatically.
• Default is three copies of data blocks.

6. Open-source framework:

• Open source access and cloud services enable large data store. Hadoop uses a cluster of multiple inexpensive servers or the cloud.

7. Java and Linux based:

• Hadoop uses Java interfaces. Hadoop base is Linux but has its own set of shell commands support.