What are differences between RDBMS and MapReduce

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Many of us usually get confuse while specifying difference between RDBMS and MapReduce.
Below table clearly specifies about this difference hope this will be useful for you.
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RDBMS (Relational Database Management System) and MapReduce are two well-known technologies in the field of data management and processing. Both systems have unique benefits and meet various demands for data processing. We will go into the finer points of RDBMS and MapReduce in this post, investigating their features, benefits, and use cases. also 

Exploring the Key Differences Between RDBMS and Hadoop


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RDBMS: A Relational Data Management Foundation

The foundation of data management and storage has long been Relational Database Management Systems (RDBMS). RDBMS organises data into rows and columns and stores it in organised tables. For data management and retrieval, it makes use of SQL (Structured Query Language). For upholding referential integrity constraints, safeguarding data integrity, and ensuring ACID (Atomicity, Consistency, Isolation, Durability) qualities, the relational model offers a solid foundation.another difference between


RDBMS stand for Relational Database Management Systems.
Please find below the differences

Traditional RDBMS MapReduce
Data size Gigabytes Petabytes
Access Interactive and batch Batch
Updates Read and write many times Write once, read many times
Transactions ACID None
Structure Schema-on-write Schema-on-read
Integrity High Low
Scaling Nonlinear Linear



Key Features of RDBMS

Data Integrity: RDBMS ensures the integrity of data by enforcing relationships and constraints defined by the database schema. This prevents inconsistencies and maintains data accuracy.
ACID Compliance: RDBMS guarantees ACID properties, making it suitable for applications that require transactional consistency and reliability.
Schema Definition: RDBMS employs a predefined schema that defines the structure and relationships between tables, enabling efficient querying and data manipulation.
SQL Querying: The SQL language provides a standardized approach to querying and retrieving data from RDBMS, offering a high degree of flexibility and expressiveness. 

MapReduce: Scalable Data Processing Framework

A distributed data processing platform called MapReduce is made to handle huge datasets. For processing and analysing large amounts of data, it provides a scalable and fault-tolerant solution. MapReduce uses a parallel processing methodology that excels in situations with large data volume and computational complexity.

Key Concepts of MapReduce

  1. Map Function: The map function takes an input dataset and transforms it into a set of key-value pairs. It performs initial data processing and filters the data based on specified conditions.
  2. Reduce Function: The reduce function takes the output generated by the map function and combines, summarizes, or aggregates the data based on common keys. It reduces the dataset into a smaller set of results.
  3. Distributed Processing: MapReduce distributes the data and processing across a cluster of machines, enabling parallel execution and efficient utilization of resources.
  4. Fault Tolerance: MapReduce handles failures by automatically reassigning failed tasks to other machines, ensuring uninterrupted data processing.

Reference: Hadoop Definitive guide 4th Edition
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