Hadoop Characteristics

Hadoop is capable of handling large volumes of both structured and unstructured data more efficiently than traditional enterprise data warehouses. It offers a highly reliable storage layer known as HDFS, a batch processing engine called MapReduce, and a resource management layer referred to as YARN.

Here are some key features of Hadoop:

Open Source

Hadoop is an open source project and its code can be modified according to business requirements.

Distributed Processing

Data is stored in a distributed manner across the cluster in HDFS, enabling parallel processing on a cluster of nodes.

Faster

Hadoop excels in high-volume batch processing due to its capability for parallel processing. It can perform batch processes significantly faster than a single-threaded server or a mainframe.

Fault Tolerance

Data is sent to individual nodes, and that same data is also replicated across other nodes in the cluster. If one node fails to process the data, other nodes in the cluster can take over and continue processing.

Reliability

The data sent to one individual node and the same data also replicates on other nodes in the same cluster. If the individual node failed to process the data, the other nodes in the same cluster available to process the data.

High Availability

Data is highly available and accessible despite hardware failure due to multiple copies of data. If the machine or hardware crashes, then data will be accessed from another path.

Scalability

Hadoop is a highly scalable storage platform capable of storing and distributing large datasets across hundreds of systems or servers that operate in parallel. It enables businesses to run applications on thousands of nodes while processing terabytes of data.

Additionally, Hadoop supports horizontal scalability, allowing users to add nodes during processing without experiencing system downtime.

Flexibility

Hadoop effectively manages both structured and unstructured data, regardless of how it is encoded or formatted. Businesses can leverage Hadoop to extract valuable insights from various data sources, such as social media and email conversations. It adds significant value by enabling unstructured data to play a crucial role in the decision-making process.

Economic or Cost effective

Hadoop provides a cost-effective storage solution for businesses with rapidly growing data sets. It is relatively inexpensive as it operates on a cluster of commodity hardware.

Easy to use

No need for clients to handle distributed computing; the framework manages everything. Therefore, Hadoop is user-friendly.

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