1.
Introduction to
Big Data
&
Big Data 1.0
System
Based on presentations during
Brno Data Week 2018
by prof Sherif Sakr
Created by: Tichý, T. & Luhan, J.
(Feb. 2019)
https://www.chedteb.eu/

2.
„Dealing with Big Data can be described as
a finding a way how to analyze and extract
infomations from large data sets“
• Data is the key resource in the modern world;
• According to IBM data, we create 2.5 quintillion bytes
every day (= 2 500 000 terabytes);
• IDC predicts that the world-wide volume of data will
reach 40 zettabytes by 2020;
• Listening to music, using credit cards, and even
reading a book on an eReader generates a lot of data;
• It is hard to imagine any activity that does not
generate data;
• The information density on silicon integrated circuits
doubles every 18 to 24 months.
Introduction to Big Data

3.
Who creates
Big Data?
Generation and consumption of
data is becoming a major part of
people‘s daily lives, especially
with the availability and usage of
Internet technology and
applications.
Introduction to Big Data
Who?

4.
Application
of Big Data
Introduction to BigData
And many more ….
Where?

5.
New types of
data
Introduction to Big Data
What?

6.
The data
structure
evolution
over the
years
Introduction to Big Data
From
what?

7.
5 V‘s of Big
Data
Introduction to Big Data
How to
define?
There are also other “V“ concepts like: 3’V or 7’V, etc.

8.
History:
Two seminal
contributors
The Google File System
• Describes a scalable,
distributed, fault-tolerant file
system tailored for data-
intensive applications,
running on inexpensive
commodity hardware,
delivers a high aggregate
performance
• MapReduce: Simplified
Data Processing on Large
Clusters
• Describes a simple
programming model and
an implementation for
processing large data sets
on computing clusters
Introduction to Big Data
Based on?

9.
Big Data 1.0 System:
The Hadoop Decade
Big Data 1.0 System

10.
Hadoop: A star of BigData 1.0
• Hadoop is an open-source software framework that
supports data-intensive distributed applications and clones
the Google's MapReduce Framework.
• It is designed to process very large amounts of
unstructured and complex data.
• It is designed to run on a large number of machines that
don't share any memory or disks.
• It is designed to run on a cluster of machines which can be
put together at a relatively lower cost and with easier
maintenance.
Big Data 1.0

11.
Hadoop's
Success
Hadoop has became very
popular in almost every
big company dealing with
Big Data.
Big Data 1.0

12.
To be
continued
In the next episode we will look
at how to deal with the data
diversity in Big Data 2.0 Systems
and the available platforms for
Big Data 2.0 Systems Platforms