RDMS (Relational Database Management System): RDBMS is an information management system, which is based on a data model.In RDBMS tables are used for information storage. Each row of the table represents a record and column represents an attribute of data. Organization of data and their manipulation processes are different in RDBMS from other databases. RDBMS ensures ACID (atomicity, consistency, integrity, durability) properties required for designing a database. The purpose of RDBMS is to store, manage, and retrieve data as quickly and reliably as possible.
Hadoop: It is an open-source software framework used for storing data and running applications on a group of commodity hardware. It has large storage capacity and high processing power. It can manage multiple concurrent processes at the same time. It is used in predictive analysis, data mining and machine learning. It can handle both structured and unstructured form of data. It is more flexible in storing, processing, and managing data than traditional RDBMS. Unlike traditional systems, Hadoop enables multiple analytical processes on the same data at the same time. It supports scalability very flexibly.
Below is a table of differences between Data Science and Data Visualization:
|1.||Traditional row-column based databases, basically used for data storage, manipulation and retrieval.||An open-source software used for storing data and running applications or processes concurrently.|
|2.||In this structured data is mostly processed.||In this both structured and unstructured data is processed.|
|3.||It is best suited for OLTP environment.||It is best suited for BIG data.|
|4.||It is less scalable than Hadoop.||It is highly scalable.|
|5.||Data normalization is required in RDBMS.||Data normalization is not required in Hadoop.|
|6.||It stores transformed and aggregated data.||It stores huge volume of data.|
|7.||It has no latency in response.||It has some latency in response.|
|8.||The data schema of RDBMS is static type.||The data schema of Hadoop is dynamic type.|
|9.||High data integrity available.||Low data integrity available than RDBMS.|
|10.||Cost is applicable for licensed software.||Free of cost, as it is an open source software.|
Don’t stop now and take your learning to the next level. Learn all the important concepts of Data Structures and Algorithms with the help of the most trusted course: DSA Self Paced. Become industry ready at a student-friendly price.
- Difference between RDBMS and OODBMS
- Difference between RDBMS and Couchbase
- Difference between RDBMS and DBMS
- Difference between RDBMS and Hive
- Difference between RDBMS and MongoDB
- Difference between RDBMS and HBase
- Difference between ER Model and RDBMS
- Difference Between Hadoop 2.x vs Hadoop 3.x
- Difference between Hadoop 1 and Hadoop 2
- Difference Between Hadoop and SQL
- Difference Between Hadoop and MapReduce
- Difference Between Hadoop and SQL Performance
- Difference Between Hadoop and MongoDB
- Difference Between Hadoop and Elasticsearch
- Difference Between Hadoop and Splunk
- Difference Between Hadoop and HBase
- Difference Between Hadoop and Teradata
- Difference Between Hadoop and Spark
- Difference Between Hadoop and Hive
- Difference Between Hadoop and Cassandra
If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to firstname.lastname@example.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.
Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.