Difference Between Hadoop and Traditional RDBMS

In the context of Big Data, Hadoop and traditional Relational Database Management Systems (RDBMS) serve distinct purposes and have key differences:

Hadoop:

• Designed for handling massive amounts of unstructured and semi-structured data.
• Distributed file system (HDFS) for storing data across a cluster of commodity hardware.
• Utilizes the MapReduce programming model for data processing.
• Horizontal scalability, meaning you can add more nodes to scale out.
• Resilient to failures due to data replication.
• Well-suited for batch processing and complex data analytics.

Traditional RDBMS:

• Structured data storage using tables with predefined schemas.
• ACID properties for ensuring data consistency and reliability.
• Vertical scalability by adding more resources to a single server.
• Strictly defined data relationships with foreign keys and joins.
• Primary use case is transactional processing and queries on structured data.
• Highly optimized for read and write operations on structured data.

Overall, Hadoop is ideal for processing large volumes of unstructured data and handling complex analytics tasks, while traditional RDBMS systems are better suited for structured data storage, transaction processing, and maintaining data consistency.