Today, as many applications are generating a massive amount of big data, Hadoop plays a significant role in providing a makeover to the database world.
What is Hadoop?
Hadoop is an open-source, Java-based framework used for storing and processing big data. Doug Cutting and Michael J. Cafarella developed Hadoop that uses the MapReduce programming model. The reasons for using MapReduce is for faster storage and retrieval of data from its nodes.
Benefits of Hadoop for Big Data
For big data and analytics, Hadoop is a lifesaver. This is because it is a useful tool when it comes to generating meaningful patterns through a vast amount of data:
Firstly, each node will replicate the data stored by other nodes of the cluster. This ensures fault tolerance. If one node goes down, there is always a backup of the data available in the cluster.
Secondly, Hadoop is scalable, unlike traditional systems that have limited data storage. It is scalable as it operates in a distributed environment. Therefore, user can easily expand its set up to include more servers that can store up to multiple petabytes of data when the need arises.
3. Low cost
Hadoop is an open-source framework, no license to be procured thus the costs are lower compared to relational database systems. The use of inexpensive commodity hardware also works in its favour to keep the solution economical.
Besides, Hadoop’s distributed file system, concurrent processing, and the MapReduce model enable running complex queries in a matter of seconds.
5. Data diversity
Hadoop Distributed File System, also known as HDFS can store different data formats. For instance, it can store unstructured, semi-structured, and structured data. While storing data, Hadoop does not need a validate against a predefined schema. Conversely, it can handle data in any format. When retrieved, data is then parsed and fitted into any schema as needed. This provides the flexibility to derive different insights using the same data.
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