suresh Difference Between Hadoop and Traditional RDBMS Systems for Big Data:
1. Data Storage: Hadoop uses a distributed file system (HDFS) to store and process large volumes of data across multiple nodes, while traditional RDBMS systems store data in tables with predefined schemas on a single server.
2. Scalability: Hadoop is highly scalable and can easily handle petabytes of data by adding more nodes to the cluster, whereas traditional RDBMS systems have limited scalability due to their vertical scaling nature.
3. Data Processing: Hadoop processes data in parallel by dividing tasks across nodes, allowing for faster processing of large datasets, whereas traditional RDBMS systems process data sequentially, which can be slower for big data processing.
4. Data Schema: Hadoop allows for schema-on-read, meaning data can be stored without a predefined structure and the schema can be applied at the time of data retrieval, while traditional RDBMS systems require a predefined schema before storing data.
5. Cost: Hadoop is open-source and can be run on commodity hardware, making it cost-effective for managing big data, whereas traditional RDBMS systems often require expensive licenses and hardware.
Overall, Hadoop is better suited for processing and analyzing large volumes of unstructured data, while traditional RDBMS systems are ideal for handling structured data in a relational format.
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