1 of 4

Sri Raghavendra Educational Institutions Society (R)

(Approved by AICTE, Accredited by NAAC, Affiliated to VTU, Karnataka)

Sri Krishna Institute of Technology

www.skit.org.in

Title: Why not RDBMS, RDBMS Vs Hadoop, History of Hadoop

CO addressed: CO2

Course: Big Data Analytics

Presented by: Mr. P. Kiran Kumar

Department: ISE

2 of 4

10/08/2026

/skit.org.in

(Approved by AICTE, Accredited by NAAC, Affiliated to VTU, Karnataka)

Sri Krishna Institute of Technology

Why Not RDBMS?

Why Not RDBMS for Big Data?

RDBMS is mainly designed for structured data.
Big Data contains structured, semi-structured, and unstructured data.
Handling Terabytes and Petabytes of data becomes difficult.
RDBMS mainly relies on vertical scaling.
Scaling a single powerful server can be expensive.
Processing massive datasets on a traditional database can be time-consuming.
RDBMS is not naturally designed for storing images, videos, logs, social media data, etc.
Big Data requires distributed storage and parallel processing.

10/08/2026

/skit.org.in

(Approved by AICTE, Accredited by NAAC, Affiliated to VTU, Karnataka)

Sri Krishna Institute of Technology

RDBMS vs Hadoop

RDBMS	Hadoop
Designed mainly for structured data	Handles structured, semi-structured & unstructured data
Uses tables and relationships	Uses distributed file storage
Suitable for GB to TB-scale applications	Suitable for TB to PB-scale data
Usually requires powerful servers	Works with commodity hardware
Strong support for transactions	Mainly designed for large-scale data processing
Centralized database architecture	Distributed architecture
Uses SQL for querying	Uses Hadoop ecosystem tools
Examples: MySQL, Oracle	Examples: HDFS, MapReduce, YARN

10/08/2026

/skit.org.in

(Approved by AICTE, Accredited by NAAC, Affiliated to VTU, Karnataka)

Sri Krishna Institute of Technology

History of Hadoop

2002: Doug Cutting and Mike Cafarella started developing Nutch, an open-source web search engine.
2003: Google published the Google File System (GFS) paper.
2004: Google published the MapReduce paper.
2005: Hadoop development began as part of the Nutch project.
2006: Hadoop became an independent project under the Apache Software Foundation.
2008: Hadoop gained popularity for large-scale data processing.
2012: Hadoop 2.x introduced YARN.
2017: Hadoop 3.0.0 was released with several improvements.
Today: Hadoop continues to be an important technology in the Big Data ecosystem.

Key Point

Google GFS + MapReduce → Nutch → Hadoop → HDFS + MapReduce → YARN → Hadoop 3.x