0:06	in this presentation we will focus on
0:08	the history of dbms
0:10	let's directly step into the topic of
0:12	today the history of dbms when we talk
0:16	about the history i have given a
0:18	timeline like this starting from 1950s
0:21	and it ranges till to thousands and even
0:24	more
0:25	and we are going to see what happened in
0:27	every decade in terms of the development
0:29	of the databases while waiting let's
0:32	start focusing on the history of dbms
0:36	basically information processing and the
0:38	automation in the information or the
0:40	data processing is the backbone in the
0:43	growth of computers starting from
0:45	punched cards to all the latest
0:47	technologies and tools that are
0:49	available till date everything needs to
0:51	store and process the data in order to
0:54	make it as a meaningful information
0:56	in the year 1950s and the early 1960s we
1:00	can see that for storing the data we
1:02	used magnetic tapes these magnetic tapes
1:05	were used in the year 1950s and early
1:09	1960s let's take an example data
1:12	processing task the payroll processing
1:14	and this payroll processing was
1:16	automated with the data stored on
1:18	magnetic tapes and this involved reading
1:20	of data from one or more magnetic tapes
1:24	and these magnetic tapes were sequential
1:27	in terms of data access so we cannot
1:30	access any data directly on the magnetic
1:32	tapes because it is a sequential storage
1:35	where we need to access the data
1:37	sequentially and because these magnetic
1:40	tapes were sequential in terms of data
1:42	access and the data sizes involved were
1:45	more than the primary memory used and we
1:47	know all the data items whichever we
1:49	want to work on should come to the
1:50	primary memory in order to work with
1:52	isn't it
1:53	in that case the data sizes involved
1:55	were more than the primary memory used
1:58	hence for processing the data it
2:00	involved reading the data from one or
2:02	more magnetic tapes and also merging of
2:05	data were involved which added the real
2:07	complexity to the system
2:09	and in the late 1960s and in the early
2:12	1970s hdds the hard disk drives found a
2:16	widespread usage so we had the power of
2:19	direct data access when compared to the
2:22	previous technology the magnetic tips so
2:24	in hard disk we can directly access the
2:26	data so hard disk is not an example for
2:29	sequential data access it is an example
2:31	for direct data access
2:33	it can also be termed as random data
2:36	access because we can access the data
2:38	randomly i mean we can access data at
2:41	any place
2:42	so we were able to navigate to any
2:44	position on the disk and thus the data
2:47	access were freed from the sequential
2:49	axis and during this time the relational
2:51	databases were born and the developer of
2:54	relational model edgar frank cord
2:57	defined the relational model and
2:59	non-procedural ways of querying data in
3:01	the relational model
3:03	and this effort from edgar francord has
3:06	led to hide the implementation details
3:09	from the programmers which we call as
3:11	data abstraction in simple terms data
3:14	abstraction means hiding the complexity
3:17	and for the work that has been carried
3:19	out on the relational model edgar frank
3:21	cord has been awarded the prestigious
3:24	acm turing award and in 1980s this
3:28	relational model were used in many
3:30	commercial products by overcoming
3:32	certain drawbacks that prevented its use
3:34	in the practice in the initial days i
3:37	mean this relational model was not
3:39	widely used in the initial days of
3:41	introduction whereas after overcoming
3:44	certain drawbacks then the relational
3:46	model were widely used in commercial
3:48	products and relational databases were
3:50	so easy to use it replaced the network
3:53	and the hierarchical model which were
3:55	tied closely to the underlying
3:57	implementation what we mean by this
3:59	network and hierarchical model were
4:01	tightly closed to the underlying
4:03	implementation see this model whenever
4:06
any changes is made on the model level
4:09
it involves a series of changes that
4:11
needs to be deployed at the
4:13
implementation level also so they are
4:15
highly dependent on each other and also
4:18
in the year 1980s we can see that there
4:21
were some research going on the parallel
4:23
and the distributed databases even on
4:26
the object oriented databases
4:28
coming to early 90s we can see sql the
4:32
structured query language was primarily
4:34
developed for decision support
4:36
applications and many database vendors
4:38
have introduced parallel database
4:40
products and also object relational
4:42
support for the databases
4:44
and this is in regards to early 1990s
4:48
but during this 90s we can also see an
4:50
explosive growth of the internet and the
4:53
world wide web and databases were in a
4:56
position to support high transaction
4:58
processing rates with 24 cross 7
5:01
availability and very very high
5:03
reliability it means there is no
5:06
downtime needs to be acquired even at
5:08
the hardware level or at the software
5:09
level for the maintenance activities
5:12
downtime means suppose if we want to
5:13
carry out any maintenance activity maybe
5:16
a hardware replacement or software
5:18
upgrade or any other maintenance
5:20
activity we need to acquire a downtime
5:22
on the server as well as from the
5:24
software level from being used only then
5:27
we can proceed with the maintenance
5:28
activities and this 1990s have seen a
5:31
scenario where availability and
5:34
reliability have increased tremendously
5:36
so it means there is no downtime
5:38
required for scheduling a maintenance
5:40
window and coming to 2000s where
5:43
emerging xml and the associated query
5:46
language xquery was evolved as a new
5:49
database technology and introduction to
5:51
auto admin features have added
5:53
advantages to the database technology
5:56	talking about this xml the extensible
5:58	markup language this language is being
6:01	widely used for data exchange and also
6:03	for storing the complex data types and
6:06	several novel distributed data storage
6:08	systems were built to handle data
6:10	management requirements of very large
6:13	websites like cisco amazon facebook
6:15	google microsoft yahoo etc
6:19	and nowadays we have the mobile
6:21	databases also and the growth of this
6:23	databases is really fascinating and we
6:26	cannot deny databases in our day-to-day
6:29	activities
6:30	i hope this lecture would have given you
6:32	a briefing about the history of
6:34	databases i also hope this session is
6:37	informative and thank you for watching
6:40	[Music]
6:40	[Applause]
6:43	[Music]
6:51	you