Introduction - Flat File System, DBMS, When RDBMS Came
Codd design who is started using- tech behind design
Future of RDBMS technology, conclusion
Physical architecture of SQL Server
Features of SQL Server
3. What is Database ?
•Collection of related & organized information
Database is a structured object.
Structured object consists of data and metadata
Data is the actual stored descriptive information.
Metadata is the structured part (table definition)
Sr. No Name Education % of Marks
1 Anil M Tech 98
2 Shailesh B Tech 89
3 Vijaylaxmi MCA 87
4. Database Systems: A Brief Timeline
1980-present Era of
1980-present Era of
relational database and
relational database and
Database Management
Database Management
System (DBMS):
System (DBMS):
1968-1980 Era of non-relational
1968-1980 Era of non-relational
database: IBM’s first hierarchical DBMS
database: IBM’s first hierarchical DBMS
called IMS. CODASYL DBTG model was for
called IMS. CODASYL DBTG model was for
N/w. IDMS most popular network DBMS.
N/w. IDMS most popular network DBMS.
1968 File-Based:
1968 File-Based:
Data maintained in aa
Data maintained in
flat file. Processing characteristics
flat file.
determined by common use of
magnetic tape medium
Ancient Times:
Ancient Times:
RAM was expensive
RAM was expensive
and limited,
and limited,
programmer Programmer Defined both logical & physical
programmer
structure, such as storage structure, access
productivity low.
productivity low. methods, I/O modes etc.
5. Database Systems: A Brief Timeline (contd..)
•1970: Ted Codd at IBM’s San Jose Lab proposed relational models.
•Two major projects start and both were operational in late 1970s
•INGRES at University of California, Berkeley became commercial
and followed up POSTGRES which was incorporated into
Informix.
•System R at IBM san Jose Lab, later evolved into DB2, which
became one of the first DBMS product based on the relational
model. (Oracle produced a similar product just prior to DB2.)
•1976: Peter Chen defined the Entity-relationship(ER) model
•1980s: Maturation of the relational database technology, more
relational based DBMS were developed and SQL standard adopted
by ISO and ANSI.
•1985: Object-oriented DBMS (OODBMS) develops. Little success
commercially because advantages did not justify the cost of
converting billions of bytes of data to new format.
6. Database Systems: A Brief Timeline (contd..)
•1990s: Incorporation of object-orientation in relational DBMSs,
new application areas, such as data warehousing and OLAP, web and
Internet, Interest in text and multimedia, enterprise resource
planning (ERP) and management resource planning (MRP)
•1991: Microsoft ships access, a personal DBMS created as element
of Windows gradually supplanted all other personal DBMS products.
•1995: First Internet database applications
•1997: XML applied to database processing, which solves long-
standing database problems. Major vendors begin to integrate XML
into DBMS products.
7. Types of Databases
1. Flat-File
Ideal for small amounts of data that needs to be human
readable or edited by hand. Can be a plain text or binary file.
8. Types of Databases (contd..)
2. Relational
The "relation" comes from the fact that the tables can be
linked to each other.
Major advantage: If designed efficiently, no duplication of
data; helping to maintain database integrity & reduced file
size
9. What is DBMS & why it is needed?
DBMS is collection of programs that enables one to store, modify,
& extract information from a database.
Purpose of a DBMS is to provide the definition, storage, and
management of data in a centralized area that can be shared by
many users.
Need
Improves decision making.
Improves data sharing & to more better-managed data.
Increases end-user productivity.
Minimized data inconsistency
Maintains the Privacy/Confidentiality.
10. Data Model
A data model is a model that describes in an abstract way how
data is represented in an information system or a database
management system (DBMS).
The evolution of database modeling techniques
11. Relational Database Model
It improves on the restriction of a hierarchical structure, not
completely abandoning the hierarchy of data, as shown in Figure.
Any table can be accessed directly without having to access all
parent objects. Any tables can be linked together, regardless of their
hierarchical position
12. Database Model Design
Ensuring that it all works
without actually building it. TUTOR
Data structure diagram
teaches on
Depict the entities & the
relationships between them
Also known as a data model
or a logical model or an entity- Entities Relationship
COURSE
relationship model.
Attributes
attended by
Tutor Student Course
Stud enrolment number
Tutor number Title
Name Name Examinations available
STUDENT
Address Address
Subjects taught Telephone number
Subjects studied
13. Database Model Design (contd..)
Relationships
An entity does not exist in
isolation, but is associated with other
entities by means of a relationship
Types of relationship
One-to-one relationship
One-to-Many relationship
Many-to-Many relationship
20. Hierarchical Database - IMS
VENDOR
1
ITEM3
VENDOR
ITEM2
ITEM1
ITEM LOC
LOC 3
1
LOCATION LOC
LOC 3
LOC
2
1
Preliminary Information Subject to Change
43. 32 BIT OS vs 64 BIT OS
64 BITS are not bound by the memory limit as in 32 BIT OS.
More memory is available in 64 Bit OS for performing complex
queries and supporting essential database operations.
64 Bit provides enhanced parallelism whereas in 32 BIT doesn't
provides that.
64 Bit enhances performance by moving more data between
cache and processors in shorter periods.
Index creation operations benefits from the existence of larger
addressable memory in 64 Bit systems.
The 64-bit architecture can substantially reduce overall CPU
utilization and latency by eliminating the need to evict procedures
from cache and compile frequently.
Operations such as aggregation and sorting need to work with the
entire datasets. These operations can benefit from the increased
memory support provided by the 64-bit platform.
58. Features of SQL Server
• Enterprise Data Management
– Management Tools
– Security such as..
database encryption
password policy enforcement
– Scalability
• Developer Productivity
– Common language Runtime (CLR) Integration
– SQL Server Compact Edition
– Transact SQL Enhancement
– More flexibility and control in SQL Server query development
• Business Intelligence
– Analysis Services
– Integration Services
– Reporting Services
59. Databases
• System databases : 4 inbuilt Databases
– Master
Contain information about login, configuration setting and
initialization information of sql server
– TempDb
Holds all temporary tables
– Model
The Model database is simply a template for all databases created
on a system
– MSDB
Information about scheduling alerts, jobs, backups
• User databases
60. SQL Server Release History
Version Year Release Name Codename
1.0 (OS/2) 1989 SQL Server 1.0 (16bit) -
1.1 (OS/2) 1991 SQL Server 1.1 (16bit) -
4.21 (WinNT) 1993 SQL Server 4.21 SQLNT
6.0 1995 SQL Server 6.0 SQL95
6.5 1996 SQL Server 6.5 Hydra
7.0 1998 SQL Server 7.0 Sphinx
- 1999 SQL Server 7.0 OLAP Tools Palato mania
8.0 2000 SQL Server 2000 Shiloh
8.0 2003 SQL Server 2000 64-bit Edition Liberty
9.0 2005 SQL Server 2005 Yukon
10.0 2008 SQL Server 2008 Katmai
10.25 2010 SQL Azure DB CloudDatabase
10.5 2010 SQL Server 2008 R2 Kilimanjaro (aka KJ)
11.0 2012 SQL Server 2012 Denali
61. Current SQL Server Versions
SQL Server 2000: no longer supported by Microsoft
SQL Server 2005: Still widely available and in use
SQL Server 2008: the greatest,
Recommended for new DR deployments
SQL Server 2012: the latest and greatest,
Recommended for new DR deployments
62. • SQL Server 2005 (code named Yukon), released in October 2005, is
the successor to SQL Server 2000
• SQL Server 2005 has also been enhanced with new indexing
algorithms and better error recovery systems
• SQL CLR was introduced with SQL Server 2005 to let it integrate with
the .NET framework
• SQL Server 2005 introduced "MARS" (Multiple Active Results Sets)
allowing usage of database connections for multiple purposes
• It is advancements in performance, the client IDE tools, and several
complementary systems that are packaged with SQL Server 2005
• It included support for managing XML data to relational data. For this
it defined an xml data type that could be used either as a data type in
database columns or as literals in queries
63. • IntelliSense for SQL queries
– Refinements to Management Studio
• Enhanced SQL Server Reporting Services (SSRS)
– Improved performance and scalability through a variety of mechanisms such
as with IT control, report design, and programmability
– Improved Report Builder 2.0
• Enhanced SQL Server Analysis Services (SSAS)
– Improvement on productivity, performance, and extensibility
• Enhanced SQL Server Integration Services (SSIS)
– Improvement on performance, scalability, and productivity
• SQL Server 2008 R2 adds more BI enhancements, including:
– PowerPivot for Excel on SharePoint 2010
– SharePoint 2010 Operations Dashboard
64. • Multi-Subnet Failover Clustering
– Improved Protection at the instance level
– Automatic failover in the event of a failure
– Broad array of storage solutions and disaster recovery solutions
• Programming Enhancements including sequences, ad-hoc query
paging and full-text search tweaks
• BI and Web Development Environment Improvements
– Newly Introduced Business Intelligence Semantic Model (BISM)
• Web-based Visualization
• Enhanced Data Quality Services
71. Limitations of OODBMS
• Procedural navigation
• No querying as it breaks encapsulation
• No mathematical foundation
• Not suitable for adhoc reporting system
• Common Data Model
72. OORDBMS
• Marrying Relational and Object Oriented concepts
• Still data is stored in Relational manner
• Object wrapper for application
• Performance is the major concern
• Still under development stage
• Commercial Products
– Informix Universal Server (Illustra) ( Merged with IBM
)
– Oracle Oracle 10g
– IBM DB2 UDB
– UniSQL UniSQL/X
– Unisys OSMOS
73. XML in DB
• Data-centric to Document-centric
• Simpler integration between Database and other tools
like
– Middlewares
– EAI tools
– ERP tools
– Other Databases
• Introduction of Native XML data type
• XML Query Language
74. What is OLAP or DW or BI?
• An organization’s success also depends on its ability to analyze data (through views
and reports) and make intelligent decisions that potentially affect its future. Systems
that facilitate such analyses are called On Line Analytical Processing (OLAP) systems
or Data Warehousing System
• Why not OLTP?
– OLTP databases do not contain historical data
– OLTP databases contain small subsets of organizational data
– OLTP databases are heterogeneous in nature and geographically distributed systems
• OLTP systems are
– Fragmented
– Not integrated.
– Difficult to access.
– Disparate sources.
– Disparate platforms.
– Poor data quality.
– Redundant data.
– Difficult to understand
75. Data warehouse / Business Intelligence
• A Data Warehouse is a copy of the enterprise operational data,
suitably modified to support the needs of analytical processes
and stored outside the operational database.
• According to Bill Inmon, known as the father of Data
Warehousing, a data warehouse is a
– Subject oriented,
– Integrated,
– Time-variant,
– Nonvolatile
– Collection of data in support of management decisions.
76. Data warehouse architecture
Data Warehouse OLAP Servers Clients
Server (Tier 2) (Tier 3)
(Tier 1)
e.g., MOLAP
Semistructured Analysis
Sources
Data
Warehouse serve
extract Query/Reporting
transform
load serve
refresh
etc. e.g., ROLAP
Operational
DB’s Data Mining
serve
Data Marts
77. Components of DW
• Extraction Transformation and Loading (ETL)
– Informatica Power Center
– Data Stage
– AbInitio
– WebFOCUS
• Data Warehouse
– Teradata
– DB2 UDB
– Oracle 10g
OLAP
– Business Object
– COGNOS
– Hyperion
– Power Analyzer
• Data Mining
– Intelligent Miner
– Darwin
– SAS Miner
78. Complementing Technology
• How many Infy shares sold yesterday in NASDAQ? What
was the highest and lowest Price?
– OLTP System
• How Infy shares are doing in NASDAQ with respect to
NSE India in last 5 Years? What’s the volume? P/E Ratio?
Highest and Lowest Price?
– DW System
• What will be the Infy earnings in second quarter of next
year? What will be the share price during that period?
– Data Mining System
79. Conclusion
In spite of many advantages, ORDBMSs also have a
drawback. The architecture of object-relational model is
not appropriate for high-speed web applications.
However, with advantages like large storage capacity,
access speed, and manipulation power of object
databases, ORDBMSs are set to conquer the database
market.
1989: originally a partnership with Ashton-Tate, Sybase and Microsoft 1990: marketing partnership with Ashton-Tate ends; 1.1 designed as platform to sell Microsoft LAN Manager
Rumors of the demise of relational database systems are greatly exaggerated. The NoSQL movement is increasingly capturing the mindshare of the developers, all the while the academia have been talking about the move away from "RDBMS as one size fits all" for several years. However, while the new storage engines are exciting to see, it is also important to recognize that relational databases still have a bright future ahead - RDBMS systems are headed into main memory, which changes the playing field all together . Several major software companies including IBM , Informix , Microsoft , Oracle , and Sybase have all released object-relational versions of their products. These companies are promoting a new, extended version of relational database technology called object-relational database management systems also known as ORDBMSs . XML is an ideal tool for data interchange. XML documents can be saved on either XML native databases or legacy relational databases. XML data is not only exchanged, but also processed and stored. As a result, the issue of storing XML data effectively and efficiently becomes paramount.
Explicit relationships improve the data access performance (especially as the database and complexity of the relationships grow) when compared to value based relationships. Supports a large number of different types of data, relationships, and objects with complex behavior A good fit for Knowledge Management problems, which are inherently complex Found application in telecommunications, high energy physics and subsets of financial services
One disadvantage of OODBMS is that it lacks a common data model. There is also no current standard, since it is still considered to be in the development stages.