Historical Evolution of RDBMS

Historical Evolution of RDBMS

Yogesh Upadhyay

Anil RaiShailesh

PachoriVijaylaxmi SoniYogesh Jedhe

MIM-Sem4 2011-14

17

32

21

3011

Mahesh Bane

Class

Access Level

•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)

What is Database ?

Sr. No Name Education % of Marks

1 Anil M Tech 98

2 Shailesh B Tech 89

3 Vijaylaxmi MCA 87

Database Systems: A Brief Timeline

Ancient Times: RAM was expensive

and limited, programmer

productivity low.

Ancient Times: RAM was expensive

and limited, programmer

productivity low.

1968 File-Based: Data maintained in a

flat file.

1968 File-Based: Data maintained in a

flat file.

Programmer Defined both logical & physical structure, such as storage structure, access methods, I/O modes etc.

1968-1980 Era of non-relational database: IBM’s first hierarchical DBMS called IMS. CODASYL DBTG model was for N/w. IDMS most popular network DBMS.

1968-1980 Era of non-relational database: IBM’s first hierarchical DBMS called IMS. CODASYL DBTG model was for N/w. IDMS most popular network DBMS.

1980-present Era of relational database and Database Management

System (DBMS):

1980-present Era of relational database and Database Management

System (DBMS):

Processing characteristics determined by common use of magnetic tape medium

•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.

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.

Database Systems: A Brief Timeline (contd..)

Types of Databases

1. Flat-FileIdeal for small amounts of data that needs to be human

readable or edited by hand. Can be a plain text or binary file.

Types of Databases (contd..)

2. RelationalThe "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

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.

What is DBMS & why it is needed?

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).

Data Model

The evolution of database modeling techniques

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

Relational Database Model

Ensuring that it all works without actually building it.

Data structure diagram Depict the entities & the

relationships between them Also known as a data model

or a logical model or an entity-relationship model.

Database Model Design

TUTOR

COURSE

STUDENT

teaches on

attended by

Entities Relationship

Tutor Student Course

Tutor number Stud enrolment number

Title Name Name Examinations available

Address Address Subjects taught Telephone number

Subjects studied

Attributes

Relationships

An entity does not exist in isolation, but is associated with other entities by means of a relationship

Types of relationshipOne-to-one relationshipOne-to-Many relationshipMany-to-Many relationship

Database Model Design (contd..)

Anil Rai

Codd design - tech behind

Preliminary Information Subject to Change

File Based Data Management


Disadvantages of File based system


VSAM Based Systems


Disadvantages of VSAM


Hierarchical Database - IMS


Hierarchical Database - IMS

VENDOR

ITEM

LOCATION

VENDOR1

ITEM1

LOC3LOC

2LOC1

ITEM2

LOC3LOC

1

ITEM3


Disadvantages of IMS


Network Database - IDMS


Disadvantages of IDMS


Relational Data Base Management System (RDBMS)


Codd Design and Technology behind RDBMS


Codd Design and Technology behind RDBMS (contd..)











Shailesh Pachori

Architecture of MS SQL (Internal)

How Database help our Business

Physical architecture of SQL Server

Relational Engine

Tasks of Relational Engine

Relational Engine - Query Processing

Relational Engine- Memory Management

32 BIT OS SQL

32 BIT OS SQL

32 BIT OS SQL

62 BIT OS SQL

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.

Tasks of Relational Engine

Storage Engine

Storage Engine Page

Storage Engine - Page

Storage Engine - Pages

Storage Engine - Pages

Storage Engine – Table

Database File

Storage Engine – Log File

SQL - CLR

SQL - CLR

SQL OS

Vijaylaxmi Soni

Architecture of MS SQL (External)

• 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

Features of SQL Server

• 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

Databases

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 SQLNT6.0 1995 SQL Server 6.0 SQL956.5 1996 SQL Server 6.5 Hydra7.0 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 Yukon10.0 2008 SQL Server 2008 Katmai10.25 2010 SQL Azure DB CloudDatabase10.5 2010 SQL Server 2008 R2 Kilimanjaro (aka KJ)11.0 2012 SQL Server 2012 Denali

Current SQL Server Versions

SQL Server 2005: Still widely available and in use

SQL Server 2012: the latest and greatest, Recommended for new DR deployments

SQL Server 2008: the greatest, Recommended for new DR deployments

SQL Server 2000: no longer supported by Microsoft

• 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

• 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– SSRS Report Builder 3.0

• 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

Yogesh Jedhe

Current Trends in Database

Emerging Trends in DB Technology

• OODBMS• OORDBMS• XML Integration

OODBMS

• Any user-defined data structures• Any user-defined operations• Any user-defined relationships• Useful for

– Manufacturing– Telecommunication– CAD/CAM– Multimedia products– Aerospace and Flight simulations

Why Object Oriented?

Relationship in OODBMS

• One - Many• Many - Many• Is A• Extends• Whole-part

Commercial Packages

• Objectivity• Poet• Jasmine• Gemstone• Itasca• ObjectStore

Limitations of OODBMS

• Procedural navigation• No querying as it breaks

encapsulation • No mathematical foundation• Not suitable for adhoc reporting

system• Common Data Model

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

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

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

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.

Data warehouse architecture

Data Warehouse Server(Tier 1)

OLAP Servers(Tier 2)

Clients(Tier 3)

OperationalDB’s

SemistructuredSources

extracttransformloadrefreshetc.

Data Marts

DataWarehouse

e.g., MOLAP

e.g., ROLAP

serve

Analysis

Query/Reporting

Data Mining

serve

serve

Components of DW

• Extraction Transformation and Loading (ETL)– Informatica Power Center– Data Stage– AbInitio– WebFOCUS

• Data Warehouse – Teradata– DB2 UDB– Oracle 10gOLAP– Business Object– COGNOS– Hyperion– Power Analyzer

• Data Mining– Intelligent Miner– Darwin– SAS Miner

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

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.

Thank [email protected]

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Historical Evolution of RDBMS

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