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Key Differences Between Hadoop and Traditional RDBMS Systems

When comparing Hadoop with traditional RDBMS systems in terms of storing and processing large datasets, there are several key differences:

1. Data Storage: Hadoop stores data across a distributed file system, while RDBMS stores data in structured tables with predefined schemas.
2. Data Processing: Hadoop utilizes a parallel processing framework for big data processing, whereas RDBMS follows a traditional query-based processing approach.
3. Scaling: Hadoop is designed for horizontal scalability, allowing it to handle massive amounts of data effortlessly, whereas RDBMS scaling may require vertical scaling by upgrading hardware.
4. Data Structure: Hadoop can process both structured and unstructured data types effectively, whereas RDBMS primarily deals with structured data.
5. Cost: Hadoop is generally more cost-effective for storing and processing large datasets compared to traditional RDBMS systems.

Understanding these key differences can help organizations make informed decisions when choosing the right data storage and processing technology for their specific requirements.

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