Existing relational databases and enterprise data warehouses though excel at processing large data sets but suffer from two major limitations: capability to process only structured data and high costs. This means a lot of crucial business data available in unstructured form remains unexplored. And this is where Hadoop can make a big difference by economically processing large amount of data, irrespective of its structure. Hadoop Common, Hadoop YARN, Hadoop Distributed File System (HDFS) and Hadoop MapReduce form the core of the Apache Hadoop project. Other related projects at Apache including Cassandara, PIG, Hive and Mahout etc., complete the Hadoop development ecosystem.
- Flexibility in handling of all types of data- structured, unstructured, audio, images etc., from disparate systems.
- Cost-efficiency by reducing the cost of data storage over legacy systems.
- Scalability in increasing from single server to hundreds of machines.
- Fault-tolerance by continuously processing your data even when a few servers fail.
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