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Introduction to Database

Sep 16, 2014

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Introduction to Database. CHAPTER 1 INTRODUCTION. Database-System Applications Purpose of Database Systems View of Data Database Languages Relational Databases Database Design Data Storage and Querying Transaction Management Database Architecture Database Users and Administrators.

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Introduction to Database CHAPTER 1 INTRODUCTION • Database-System Applications • Purpose of Database Systems • View of Data • Database Languages • Relational Databases • Database Design • Data Storage and Querying • Transaction Management • Database Architecture • Database Users and Administrators

Database System: Introduction • Database Management System (DBMS) • Contains a large bodies of information • Collection of interrelated data (database) • Set of programs to access the data • Goal of a DBMS: • provides a way to store and retrieve database information that is both • convenient and • efficient. • Functions of DBMS: Management of Data (MOD) • Defining structure for storage data • Providing mechanisms for manipulation of data • Ensure safety of data (system crashes, unauthorized access, misused, …) • Concurrent control in multi-user environment • Computer Scientists: developed a lot of concepts and technique for MOD • concepts and technique form the focus of this book, and this course

1.1 Database-System Applications • Database Applications: • Banking: all transactions • Airlines: reservations, schedules • Universities: registration, grades, student profile, .. • Sales: customers, products, purchases • Manufacturing: production, inventory, orders, supply chain • Human resources: employee records, salaries, tax deductions • Databases touch all aspects of our lives

1.2 Purpose of Database Systems • In the early days, database applications were built on top of file systems • Drawbacks of using file systems to store data: • Data redundancy and inconsistency • Multiple file formats, duplication of information in different files • Difficulty in accessing data • Need to write a new program to carry out each new task • Data isolation — multiple files and formats • Integrity problems • Integrity constraints (e.g. account balance > 0) become part of program code • Hard to add new constraints or change existing ones

Drawbacks of using file systems (cont.) • Drawbacks of using file systems to store data: (cont.) • Atomicity of updates • Failures may leave database in an inconsistent state with partial updates carried out • E.g. transfer of funds from one account to another should either complete or not happen at all • Concurrent access by multiple users • Concurrent accessed needed for performance • Uncontrolled concurrent accesses can lead to inconsistencies • E.g. two people reading a balance and updating it at the same time • Security problems Database systems offer solutions to all the above problems 原子性, 單一性 Solution

1.3 View of Data and Data Abstraction • Physical level: describes how a record (e.g., customer information) is stored in disk. • By sequential file, pointer, or hash structure, … • Logical level: describes data stored in database, and the relationships among the data. typecustomer = recordname : string;street : string;city : string; income : integer; end; • View level: application programs hide details of data types. Views can also hide information (e.g., income) for security purposes.

View of Data -1: Three Levels An architecture for a database system

User A1 User A2 User B1 User B2 User B3 Host Language + DSL Host Language + DSL Host Language + DSL Host Language + DSL Host Language + DSL C, C++ DSL (Data Sub. Language) e.g. SQL 3 1 2 External View B External View @ # & External schema A External schema B External/conceptual mapping B External/conceptual mapping A Database management system Dictionary (DBMS) e.g. system catalog Conceptual View Conceptual schema < DBA Conceptual/internal mapping (Build and maintain schemas and mappings) Storage structure definition (Internal schema) ... 1 2 3 100 Stored database (Internal View) # @ & View of Data -2: Three Levels

account customer 1.3.2 Instances and Schemas • Schema – the logical structure of the database • e.g., the database consists of information about a set of customers and accounts and the relationship between them • Analogous to type information of a variable in a program • Physical schema: database design at the physical level • Logical schema: database design at the logical level create tableaccount (account-numberchar(10),balanceinteger) typecustomer = recordname : string;street : string;city : integer; end;

Instances and Schemas (cont.) • Instance – the actual content of the database at a particular point in time • Analogous to the value of a variable • Physical Data Independence– the ability to modify the physical schema without changing the logical schema • Applications depend on the logical schema • In general, the interfaces between the various levels and components should be well defined so that changes in some parts do not seriously influence others. Instance Schema create tableaccount (account-numberchar(10),balanceinteger)

View of Data: Three Levels An architecture for a database system Physical Data Independence

1.3.3 Data Models • A collection of conceptual tools for describing • data (entities, objects) • data relationships • data semantics • data consistency constraints • Data Models Provide: • A way to describe the design of a database at 3 levels • Physical level • Logical level • View level

Category of Data Models • Category of Data Models: • Entity-Relationship model • Relational model • Object-oriented model • Semi-structured data models • Extensible Markup Language (XML) • Older models: • Network model and • Hierarchical model

1.4 Database Languages • Data Definition Language (DDL): • Specification notation for defining the database schema • E.g. create tableaccount (account-numberchar(10),balanceinteger) • Data Manipulation Language (DML) • To express database queries or updates • E.g. Selectaccount-numberfrom accountwhere balance >1000 • SQL (Structured Query Language): a single language for both

1.4.1 Data-Manipulation Language (DML) • Language for accessing and manipulating the data organized by the appropriate data model • DML also known as query language • For retrieval, insertion, deletion, modification (update) • Two classes of languages • Procedural DMLs – user specifies what data is required and how to get those data • E.g. … in C • Declarative DMLs (Nonprocedural DMLs) – user specifies what data is required without specifying how to get those data • E.g. In SQL: Selectaccount-numberfrom accountwhere balance > 700 • SQL is the most widely used query language

1.4.2 Data-Definition Language (DDL) • Specification notation for defining the database schema • E.g. create tableaccount (account-numberchar(10),balanceinteger) • Define: • Attributes name • Data type • Consistency constraints (integrity constraints) • Domain constraints: e.g. assets are integer type • Assertions: e.g. assets >= 0 • Authorization: for different users • …. create tablebranch (branch-namechar(15),branch-citychar(30),assetsinteger,primary key (branch-name),check (assets >= 0))

Data Dictionary and Storage Definition • Data Dictionary: • DDL compiler generates a set of tables stored in a data dictionary • contains metadata (i.e., data about data) • Database schema • System tables • Users • … • Database system consults the Data dictionary before reading or modifying actual dada. • Data storage and definition language • To specify the storage structure and access methods(ch. 11,12) • Usually an extension of the data definition language

DBMS environments Relational DBMS Relational Data Model C, PASCAL ,PL/1 assembler machine 1.5 Relational Databases • Definition 1: A Relational Database is a databasethat is perceived by the users as a collection oftime-varying, normalized relations (tables). • Perceived by the users: the relational model apply at the view level and logical levels. • Time-varying: the set of tuples changes with time. • Normalized: contains no repeating group (only contains atomic value). • The relational model represents a database system at a level of abstraction that removed from the details of the underlying machine, like high-level language.

1.5.1 Tables • Definition 2: A Relational Database is a database that is perceived by its users as a collection of tables (and nothing but tables).

1.5.2 Data-Manipulation Language • SQL (Structured Query Language) : widely used • E.g. find the name of the customer with customer-id 192-83-7465selectcustomer.customer-namefromcustomerwherecustomer.customer-id = ‘192-83-7465’ customer Output: customer-name Johnson

SQL (Structured Query Language) • E.g. find the balances of all accounts held by the customer with customer-id 192-83-7465selectaccount.balancefromdepositor, accountwheredepositor.customer-id = ‘192-83-7465’anddepositor.account-number = account.account-number

1.5.3 Data-Definition Language • SQL provides DDL to define database schema: • Tables • E.g. create tableaccount (account-numberchar(10),balanceinteger) • Assertions (ref. p.132) • E.g. create assertion balance-constraint check account.balance >= 1000 • integrity Constraints (ref. p.129)

3. account 4. depositor Referential Integrity Constraint create table account(account-number char(10),branch-name char(15),balance integer, primary key (account-number), create table depositor(customer-name char(20),account-number char(10), primary key (customer-name, account-number), foreign key(account-number) references account, 存款帳 references 存款戶

ODBC/JDBC 1.5.4 Data Access from Application Programs • Application programs generally access databases through one of • Language extensions to allow embedded SQL • Application program interface (e.g. ODBC/JDBC) which allow SQL queries to be sent to a database • ODBC: Open Database Connectivity for C • JDBC: Java Database Connectivity for Java language

1.6 Database Design • Database Design - The process of designing the general structure of the database: • Logical Design • Physical Design • Logical Design –Deciding on the database schema. • To find a “good” collection of relation schemas. • Business decision– What attributes should we record in the database? • Computer Science decision– What relation schemas should we have and how should the attributes be distributed among the various relation schemas? • Physical Design –Deciding on the physical layout of the database

1.6.1 Design Process • Phase I • Specification of user requirement (with domain experts) • Phase II • Conceptual design (ch. 6) • Choose a data model • Design tables • Normalization (ch. 7) • Phase III • Specification of functional requirements • Phase IV • Implementation • Logical-design • Physical-design (ch. 11, 12)

1.6.2 Database Design for Banking • Banking Database: consists 6 relations: • branch (branch-name, branch-city, assets) • customer (customer-name, customer-street, customer-only) • account (account-number, branch-name, balance) • loan (loan-number, branch-name, amount) • depositor (customer-name, account-number) • borrower (customer-name, loan-number)

6. loan 3. depositor 5. account 存款帳 4. borrower 存款戶 貸款帳 貸款戶 Example: Banking Database 1. branch 2. customer 客戶(存款戶,貸款戶) 分公司

1.6.3 Entity-Relationship Model (ch.6) • Example: Schema in the Entity-Relationship model 客戶 存款帳 存款帳 客戶(存款戶,貸款戶,信用卡戶) 存款戶

E-R Diagram for a Banking Enterprise, p.240

Entity Relationship Model (cont.) • E-R model of real world • Entities (objects) • E.g. customers, accounts, bank branch • Relationships between entities • E.g. Account A-101 is held by customer Johnson • E.g. Relationship set depositor associates customers with accounts • Widely used for database design • Database design in E-R model usually converted to design in the Relational model (coming up next) which is used for storage and processing • Relational Model (ch. 2) • E-R model (ch. 6)

<e.g.> Supplier-and-Parts Database S SP S# P# QTY S1 P1 300 S1 P2 200 S1 P3 400 S1 P4 200 S1 P5 100 S1 P6 100 S2 P1 300 S2 P2 400 S3 P2 200 S4 P2 200 S4 P4 300 S4 P5 400 S# SNAME STATUS CITY S1 Smith 20 London S2 Jones 10 Paris S3 Blake 30 Paris S4 Clark 20 London S5 Adams 30 Athens P P# PNAME COLOR WEIGHT CITY P1 Nut Red 12 London P2 Bolt Green 17 Paris P3 Screw Blue 17 Rome P4 Screw Red 14 London P5 Cam Blue 12 Paris P6 Cog Red 19 London 1.6.4 Normalization • Definition: A Relational Database is a database that is perceived by its users as a collection of tables (and nothing but tables).

SP' S' P P# ... ... ... . . . . . . . . S1, Smith, 20, London, P1, Nut, Red, 12, London, 300 S1, Smith, 20, London, P2, Bolt, Green, 17, Paris, 200 . . S4, Clark, 20, London, P5, Cam, Blue, 12, Paris, 400 S# CITY P# QTY S1 London P1 300 S1 London P2 200 . . . . S# SNAME STATUS S1 Smith . S2 . . . . . Normalization (異常) Redundancy Update Anomalies! S P SP S# P# QTY . . . . . . S# SNAME STATUS CITY s1 . . London . . . . P# ... ... ... . . . . . . . . Problem of Normalization <e.g.> or

6. borrower 1.7 Object-Based and Semistructured Databases • Extend the relational data model • by including object orientation and • constructs to deal with added data types. (video, image, …) • Allow attributes of tuples to have complex types, including • non-atomic values such as nested relations. (repeated data, …) • Preserve relational foundations, • in particular the declarative access to data, while extending modeling power.

1.7.2 Semistructured Data Models • XML (Extensible Markup Language) • Defined by the WWW Consortium (W3C) • Originally intended as a document markup language not a database language • The ability to specify new tags, and to create nested tag structures made XML a great way to exchange data, not just documents • XML has become the basis for all new generation data interchange formats. • A wide variety of tools is available for parsing, browsing and querying XML documents/data 聯合

Query DBMS Language Processor Optimizer Operation Processor Access Method File Manager Database 1.8 Data Storage and Querying • Components of Database System • Query Processor • Helps to simplify to access data • High-level view • Users are not be burdened unnecessarily with the physical details • Storage Manager • Require a large amount of space • Can not store in main memory • Disk speed is slower • Minimize the need to move data between disk and main memory Query Processor Storage Manager Goal of a DBMS: provides a way to store and retrieve data that is both convenient and efficient.

Overall System Structure Overall System Structure low-level data stored database

1.8.1 Storage Management • Storage Manager • is a program module • that provides the interface between the low-level data stored and the application programs and queries submitted to the system. • Tasks of the Storage Manager: • interaction with the file manager (part of Operating System) • Translates DML into low-level file-system commands, • i.e. responsible for storing, retrieving and updating of data in database • Data Structures of the Storage Manager • Data files: store database itself • Data Dictionary: store metadata • Indices: provide fast access to data items that hold particular values

Storage Management (cont.) • Components of Storage manager: • Authorization and Integrity Manager • Tests for the satisfaction of integrity constraints • Checks the authority of users to access data • Transaction Manager • Ensure the database in a consistent state (correct) after failures • Ensure that concurrent transaction executions proceed without conflicting • File Manager • Manages the allocation of space on disk • Manages the data structures used to representation data stored • Buffer manager • Fetches data from disk into main memory • Decides what data to cache in main memory

1.8.2 The Query Processor • DDL Interpreter • Interprets DDL statements • write the definitions (schema, view, ..) into the data dictionary • DML Compiler • Translates DML statements into an evaluation plan (or some evaluation plans) which consists low-level instructions • Query Optimization: picks the lowest cost evaluation plan • Query Evaluation Engine: • execute low-level instructions generated by the DML Compiler

Flow of Query Processing 1. Parsing and translation 2. Optimization 3. Evaluation

Query Optimizer • Alternative ways of evaluating a given query • Equivalent expressions • Different algorithms for each operation • Cost difference between a good and a bad way of evaluating a query can be enormous • Need to estimate the cost of operations • Depends critically on statistical information about relations which the database must maintain • Need to estimate statistics for intermediate results to compute cost of complex expressions

DBMS Language Processor Internal Form : • ((S SP) Optimizer Language Processor Operator Processor Access Method Access Method e.g.B-tree; Index; Hashing File System database Example: A Simple Query Processing (補) Query in SQL： SELECT CUSTOMER. NAME FROM CUSTOMER, INVOICE WHERE REGION = 'N.Y.' AND AMOUNT > 10000 AND CUTOMER.C#=INVOICE.C Query Processor Operator : SCAN C using region index, create C SCAN I using amount index, create I SORT C?and I?on C# JOIN C?and I?on C# EXTRACT name field Calls to Access Method： OPEN SCAN on C with region index GET next tuple . . . Storage Manager Calls to file system： GET10th to 25th bytes from block #6 of file #5

1.9 Transaction Management • Transaction: • A transaction is a collection of operations that performs a single logical function in a database application • Atomicity: all or nothing • Failure recovery manager • ensures that the database remains in a consistent (correct) state, • Failure: • system failures (e.g., power failures and operating system crashes) • transaction failures. • Concurrency-control manager • controls the interaction among the concurrent transactions, to ensure the consistency of the database.

1.10 Data Mining and Analysis • Data Analysis and Mining • Decision Support Systems • Data Analysis and OLAP (Online analytical processing), • Data Warehousing • Data Mining

Decision-support systems are used to make business decisions, often based on data collected by on-line transaction systems. Examples of business decisions: What items to stock? What insurance premium to change? To whom to send advertisements? Examples of data used for making decisions Retail sales transaction details Customer profiles (income, age, gender, etc.) Decision Support Systems

Data Mining (ch.18) • Data mining: • seeks to discover knowledge automatically in the form of statistical rules and patterns from large databases. E.g. p.23: Young women buy cars. • is the process of semi-automatically analyzing large databases to find useful patterns • Prediction based on past history • Predict if a credit card applicant poses a good credit risk, based on some attributes (income, job type, age, ..) and past history • Predict if a pattern of phone calling card usage is likely to be fraudulent • Descriptive Patterns • Associations • Find books that are often bought by “similar” customers. If a new such customer buys one such book, suggest the others too. (library) • Associations may be used as a first step in detecting causation 欺騙的 引起;因果關係

1.11 Database Architecture • System Structure of a Database System • Fig. 1.6 (p.25) • Application Structure • User uses database at the site • Users uses database through a network • Client: remote database users work • Sever: database system runs here • Partition of Database Application • Two-tier architecture • Three-tier architecture

Application Architectures ODBC/JDBC • Two-tier Architecture: e.g. client programs using ODBC/JDBC to communicate with a database • Three-tier Architecture: e.g. web-based applications, and applications built using “middleware”

User A1 User A2 User B1 User B2 User B3 Host Language + DSL Host Language + DSL Host Language + DSL Host Language + DSL Host Language + DSL C, C++ DSL (Data Sub. Language) e.g. SQL 3 1 2 External View B External View @ # & External schema A External schema B External/conceptual mapping B External/conceptual mapping A Database management system Dictionary (DBMS) e.g. system catalog Conceptual View Conceptual schema < DBA Conceptual/internal mapping (Build and maintain schemas and mappings) Storage structure definition (Internal schema) ... 1 2 3 100 Stored database (Internal View) # @ & 1.12 Database Users and Administrators

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CS44800: Introduction to Database Systems, Spring 2019

Lecture Schedule

Chapter 1 - Introduction ( ppt / pdf )

Chapter 2 -Database System Concepts and Architecture ( ppt / pdf )

Chapter 13 - Disk Storage, Basic File Structures and Hashing ( ppt )

Chapter 14 - Indexing Structures for Files ( ppt )

Chapter 3 - Data Modeling Using the Entity Relationship (ER) Model ( ppt / pdf )

• Hierarchical model-slides
• Network Model-slides
• Handout on Natural and Outer Joins & Relational Calculus

Chapter 6 - The Relational Algebra and Calculus ( ppt / pdf )

Chapter 7 - Relational Database Design by ER-and EERR-to-Relational Mapping ( ppt / pdf )

Chapter 15 - Algorithms for Query Processing and Optimization ( ppt )

Chapter 8 - SQL-99: Schema Definition, Constraints, and Queries and Views ( ppt )

• JDBC Tutorial
• PL/SQL Tutorial

Chapter 10 - Functional Dependencies and Normalization for Relational Databases ( ppt )

• Example of proving loss less join
• Normalization and lossless joins and FD preservations

Chapter 12 - Practical Database Design Methodology and Use of UML Diagrams ( ppt )

Chapter 16 - Practical Database Design and Tuning ( ppt )

• Concurrency Control in Database Systems , Bharat Bhargava, IEEE Trans on Knowledge and Data Engineering,11(1), Jan.-Feb. 1999
• The Transaction Concept: Virtues and Limitations , Jim Gray, VLDB, 1981
• 2PL and Conflict Graph. ( pdf )
• Locking-Serializability. ( pdf )
• Optimistic-timestamps-Failure-Commitment. ( pdf )
• It will help you learn about CC ideas on 2PL, optimistic, time stamps and about transaction failures to help you do the homework. You may read the papers on CC under handouts and check sections 2 up to 2.3.2. ( pdf )
• You may read slides on chapter 21 (7th edition) up to slides 21-22 and then 27-29 for answers to homework. ( pdf )
• Logs, failures, serializability is discussed in slides for chapter 20. ( pdf )
• Concurrency Control in Database Systems, Bharat Bhargava, IEEE Trans on Knowledge and Data Engineering,11(1), Jan.-Feb. 1999. ( pdf )
• The Transaction Concept: Virtues and Limitations , Jim Gray, VLDB, 1981. ( pdf )

Chapter 19 - Database Recovery Techniques ( ppt )

• Feature analysis of selected database recovery techniques, Bharat Bhargava and Leszek Lilien, Proceedings of the May 4-7, 1981, National Computer Conference . ACM, 1981.( pdf )
• Secure Data Warehouse - Privacy

Chapter 24 - Enhanced Data Models for Advanced Applications ( ppt )

• Introduction to Cloud Computing
• Privacy and Identity Management in Cloud Computing
• Complete Tutorial

Chapter 26 - Web Database Programming using PHP ( ppt )

Chapter 27 - XML-Extensible Markup Language ( ppt )

Chapter 29 - Overview of Data Warehousing and OLAP ( ppt )

Chapter 30 - Emerging Database Technologies and Applications ( ppt )

CS44800, Spring 2019 Purdue University, Computer Science Department

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Database Project Proposal

Database project proposal presentation, free google slides theme and powerpoint template.

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Data Schema In DBMS Powerpoint Presentation Slides

Our comprehensive Data Model in DBMS deck encompasses the essential elements required for effective data analytics. The data model template serves as the core foundational work, providing a structured framework for storing data in databases. Moreover, our professionally crafted DBMS PPT offers a concise overview of the data model, highlighting its conceptual tools and aiding in explaining the database architecture. In addition, the Data Schema presentation also includes tables, foreign keys, primary keys, views, columns, data types, stored procedures, and more, showcasing the database schema. Furthermore, our Data Modeling Techniques PPT outlines the step-by-step process of creating a data model using different techniques and tools. Also, the Database Design deck covers the conceptual, logical, and physical data model phases, characteristics, and advantages. Lastly, our Data Model Architecture module presents industry-specific data models for Healthcare, BFSI, Manufacturing, and Retail sectors. You can conveniently download our 100 percent editable and customizable template, compatible with Google Slides, to efficiently design and optimize your data architecture.

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Deliver this complete deck to your team members and other collaborators. Encompassed with stylized slides presenting various concepts, this Data Schema In DBMS Powerpoint Presentation Slides is the best tool you can utilize. Personalize its content and graphics to make it unique and thought-provoking. All the fifty eight slides are editable and modifiable, so feel free to adjust them to your business setting. The font, color, and other components also come in an editable format making this PPT design the best choice for your next presentation. So, download now.

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Slide 1 : This slide introduces Data Schema in DBMS. Slide 2 : This slide depicts the Agenda of the presentation. Slide 3 : This slide incorporates the Table of contents. Slide 4 : This slide highlights the Title for the Topics to be discussed next. Slide 5 : This slide describes the overview of data model in database management system that help businesses many ways. Slide 6 : This slide covers the benefits of data modeling such as reducing data redundancy, improved coordination, etc. Slide 7 : This slide shows the commonly used relationships notations. Slide 8 : This slide includes the Heading for the Components to be covered further. Slide 9 : This slide explains the Importance of data model creation. Slide 10 : This slide reveals the Title for the Ideas to be covered in the following template. Slide 11 : This slide talks about the architecture of data models inside which three models are conceptual, logical, and physical. Slide 12 : This slide displays the Heading for the Ideas to be discussed next. Slide 13 : This slide describes the conceptual data model's components, such as entities, attributes, etc. Slide 14 : This slide represents the main benefits and the features of using a conceptual model. Slide 15 : This slide mentions the Title for the Topics to be covered further. Slide 16 : This slide provides an overview of a logical data model. Slide 17 : This slide explains the characteristics and advantages of a logical data model. Slide 18 : This slide elucidates the Heading for the Contents to be discussed in the next template. Slide 19 : This slide shows the overview of physical model with its cardinality and relations in a more specific manner. Slide 20 : This slide outlines the characteristics and advantages of physical data model along with its impact. Slide 21 : This slide indicates the Title for the Ideas to be covered further. Slide 22 : This slide represents the difference between main models of data modeling. Slide 23 : This slide portrays the Heading for the Ideas to be discussed in the following template. Slide 24 : This slide states the process of Data modeling, and how it is done. Slide 25 : This slide shows the Title for the Contents to be covered further. Slide 26 : This slide shows in depth overview of different data modeling techniques. Slide 27 : This slide provides an overview of the hierarchical data modeling technique. Slide 28 : This slide shows the overview of network data modeling technique and also explains the structure of network data model. Slide 29 : This slide presents the overview of entity relationship modeling technique and its block diagram. Slide 30 : This slide depicts the Relational data modeling technique in DBMS. Slide 31 : This slide exhibits the overview of the object-oriented modeling technique with its components. Slide 32 : This slide reveals the Heading for the Topics to be discussed next. Slide 33 : This slide explains the data modeling tools in database management systems. Slide 34 : This slide portrays the Title for the Ideas to be covered further. Slide 35 : This slide contains structure of data model which is specific to healthcare industry. Slide 36 : This slide deals with the BFSI specific data model structure. Slide 37 : This slide includes the structure of data model which is specific to manufacturing industry. Slide 38 : This slide showcases the structure of data model which is specific to manufacturing industry. Slide 39 : This slide displays the Heading for the Ideas to be discussed in the forth-coming template. Slide 40 : This slide explains the checklist for making well-designed data model. Slide 41 : This slide exhibits the Title for the Topics to be covered further. Slide 42 : This slide represents the timeline to implement data model. Slide 43 : This slide elucidates the Heading for the Components to be discussed in the following template. Slide 44 : This slide represents the 30-60-90 days plan to implement data model. Slide 45 : This slide reveals the Title for the Ideas to be covered further. Slide 46 : This slide represents the roadmap for building data model. Slide 47 : This slide showcases the Heading for the Ideas to be covered in the following template. Slide 48 : This slide shows the data model performance tracking dashboard. Slide 49 : This is the Icons slide containing all the Icons used in the plan. Slide 50 : This slide depicts some Additional information. Slide 51 : This slide elucidates the mission, vision, and goal of the organization. Slide 52 : This slide showcases information related to the Financial topic. Slide 53 : This is the Venn diagram slide with related imagery. Slide 54 : This is the Idea generation slide for encouraging innovative ideas. Slide 55 : This slide displays the SWOT analysis. Slide 56 : This is Meet our team slide. State your team-related information here. Slide 57 : This is the Puzzle slide with related imagery. Slide 58 : This is the Thank You slide for acknowledgement.

Data Schema In DBMS Powerpoint Presentation Slides with all 63 slides:

Use our Data Schema In DBMS Powerpoint Presentation Slides to effectively help you save your valuable time. They are readymade to fit into any presentation structure.

Data model creation is crucial because it provides a structured representation of data, enabling organizations to understand their data assets better. It helps in organizing, defining, and documenting data requirements, relationships, and constraints, which aids in developing efficient databases and information systems.

Data modeling is the process of creating a conceptual, logical, and physical representation of data to understand its structure, relationships, and constraints. It helps in designing efficient databases and information systems.

Data modeling offers several benefits, including improved data understanding, reduced data redundancy, enhanced data integration, easier database design, and alignment of data with business objectives.

The conceptual data model includes essential components such as entities (representing real-world objects), attributes (characteristics of entities), and relationships (connections between entities). It provides a bird's-eye view of the data without delving into implementation details.

The logical data model offers a detailed representation of data elements and their interrelationships. It abstracts the data from any specific database technology, making it independent of the physical implementation. The advantages include improved data understanding, easier database design, and facilitating seamless data integration.

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Simple Database.

Published by Rosamond McDaniel Modified over 8 years ago

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