Hadoop

Hadoop is designed for huge data sets, which can save a lot of time with reading and processing data. However, the NameNode, which allocates the data blocks, is a single point of failure. Without a proper backup, or another NameNode ready to kick in, the file system can be become instantly useless. There are typically two ways to ensure the integrity of the NameNode.

One way is to have a Secondary NameNode, which periodically creates a copy of the file system image file. The process is called a "checkpoint". In the event of a failure of the Primary NameNode, the Secondary NameNode can be manually configured as the Primary NameNode. The need for manual intervention can cause delays and potentially other problems.

The second method is with a Standby NameNode. In this scenario, the same checkpoints are performed, however, in the event of a Primary NameNode failure, the Standby NameNode will immediately take the place of the Primary, preventing a disruption in service. This method requires additional services to be installed for it to operate.

For data indexing (unstructured content) and search without any costly server... there is no real alternative option?

Hadoop is not a replacement of a transactional system such as RDBMS. It is suitable for batch processing.

Hadoop is well suited for internal projects in a secure environment without any external exposure. It also excels well in storing and processing large amounts of data. It is also suitable to be implemented as a data repository for data-intensive applications which require high data availability, a significant amount of memory and huge processing power. However, it is not appropriate to implement as a near real-time solution which needs a high response time with a high number of high transactions per seconds.