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Difference Between RDBMS and Hadoop
RDBMS stores structured data in tables with ACID compliance using SQL. Hadoop is an open-source framework for distributed storage and processing of large-scale structured and unstructured data using HDFS and MapReduce.
What is RDBMS?
RDBMS (Relational Database Management System) stores data in tables with rows and columns, following ACID properties (Atomicity, Consistency, Isolation, Durability). It is designed for fast storage and retrieval of structured data using SQL. Examples: Oracle, MySQL, PostgreSQL.
What is Hadoop?
Hadoop is an open-source framework for running distributed applications and storing large-scale data. It handles structured, semi-structured, and unstructured data with high processing power. Its core components are
- HDFS Distributed file system for storage
- YARN Resource management
- MapReduce Batch processing engine
- Hadoop Common Shared utilities
Key Differences
| Feature | RDBMS | Hadoop |
|---|---|---|
| Data Type | Structured only | Structured + unstructured |
| Processing | SQL queries | MapReduce / Spark batch processing |
| Schema | Static (predefined) | Dynamic (schema-on-read) |
| Scalability | Vertical (limited) | Horizontal (highly scalable) |
| Data Integrity | High (ACID) | Lower (eventual consistency) |
| Normalization | Required | Not required |
| Cost | Licensed (paid) | Open-source (free) |
| Best For | OLTP, transactions | Big data, analytics, ML |
Which to Choose?
Use RDBMS for transactional applications needing ACID compliance, structured data, and fast SQL queries. Use Hadoop for big data analytics, processing massive unstructured datasets, and machine learning workloads where horizontal scalability and cost-effectiveness matter.
Conclusion
RDBMS and Hadoop serve different purposes RDBMS excels at structured, transactional data with ACID guarantees, while Hadoop handles large-scale distributed processing of any data type. Many organizations use both together, with RDBMS for operational data and Hadoop for analytics.
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