The document discusses the database system development lifecycle. It notes that 80-90% of database projects do not meet performance goals and are often late and over budget. Reasons for failure include a lack of complete requirements specification, inappropriate development methodology, and poor system decomposition. The solution is to follow a structured approach like the Information Systems Lifecycle, Software Development Lifecycle, or Database System Development Lifecycle. Key stages of the Database System Development Lifecycle include planning, definition, requirements collection, design, prototyping, implementation, data conversion, testing, and operational maintenance.
The document discusses the database development life cycle (DBLC), which follows a similar process to the systems development life cycle (SDLC). The DBLC involves gathering requirements, database analysis, design, implementation, testing and evaluation, and maintenance. It describes each stage in detail, including conceptual, logical, and physical data modeling during the design stage. The goal is to systematically plan and develop a database to meet requirements while ensuring completeness, integrity, flexibility, and usability.
The document discusses database design within the context of information systems and their life cycles. It describes the systems development life cycle (SDLC) and database life cycle (DBLC) as frameworks for developing and maintaining databases and applications. The database design process involves conceptual, logical, and physical design stages to model data and map the design to a target database management system. Centralized and decentralized approaches as well as top-down and bottom-up strategies are discussed for database design.
This document discusses database management systems and the database development lifecycle. It defines DBMS as software that manages databases and provides functions like data definition, retrieval, updating and administration. It describes the characteristics of data in databases and advantages like redundancy control and data sharing. The document outlines the planning, analysis, design, implementation and maintenance phases of both the software development lifecycle and database development lifecycle. It also covers different database models like hierarchical, network and relational.
This chapter discusses database design and the systems development life cycle (SDLC). It explains that the SDLC traces the development of an information system through planning, analysis, design, implementation, and maintenance phases. Within the information system, the database life cycle (DBLC) describes the development of the database through initial study, design, implementation, testing, operation, and maintenance phases. The chapter also covers topics of conceptual database design, logical design, and physical design.
This document discusses database design and the systems development life cycle (SDLC). It explains that the SDLC traces the history of an information system through planning, analysis, design, implementation, and maintenance phases. Within the information system, the database life cycle (DBLC) describes the history of the database through initial study, design, implementation, testing, operation, and maintenance/evolution phases. The chapter also covers conceptual database design strategies like top-down vs. bottom-up and centralized vs. decentralized design.
Pr dc 2015 sql server is cheaper than open sourceTerry Bunio
SQL Server was found to be cheaper than open source options for a data warehouse project with the following requirements:
- Serve 100% operational reports from 1TB of data
- No need for advanced features like big data support
- Requirement was for basic textual reporting
An investigation was conducted of SQL Server, Oracle, Sybase, MySQL, and PostgreSQL. SQL Server and PostgreSQL were evaluated further based on costs and functionality. After a 10 year total cost of ownership analysis, SQL Server was found to be cheaper despite having a higher initial license cost. The lessons learned were that open source options are not always cheaper, to test options yourself rather than rely on biased reports, and that Oracle is very expensive.
The document discusses the database development life cycle (DDLC). It outlines the 6 phases of the DDLC: database planning, database design, implementation and downloading, testing and evaluation, operation, and maintenance and evolution. The database design phase is described as the most important, involving conceptual, logical, and physical design activities like data modeling, normalization, and validating the data model. The goals and key activities of each phase are summarized.
The document discusses the database system development lifecycle. It notes that 80-90% of database projects do not meet performance goals and are often late and over budget. Reasons for failure include a lack of complete requirements specification, inappropriate development methodology, and poor system decomposition. The solution is to follow a structured approach like the Information Systems Lifecycle, Software Development Lifecycle, or Database System Development Lifecycle. Key stages of the Database System Development Lifecycle include planning, definition, requirements collection, design, prototyping, implementation, data conversion, testing, and operational maintenance.
The document discusses the database development life cycle (DBLC), which follows a similar process to the systems development life cycle (SDLC). The DBLC involves gathering requirements, database analysis, design, implementation, testing and evaluation, and maintenance. It describes each stage in detail, including conceptual, logical, and physical data modeling during the design stage. The goal is to systematically plan and develop a database to meet requirements while ensuring completeness, integrity, flexibility, and usability.
The document discusses database design within the context of information systems and their life cycles. It describes the systems development life cycle (SDLC) and database life cycle (DBLC) as frameworks for developing and maintaining databases and applications. The database design process involves conceptual, logical, and physical design stages to model data and map the design to a target database management system. Centralized and decentralized approaches as well as top-down and bottom-up strategies are discussed for database design.
This document discusses database management systems and the database development lifecycle. It defines DBMS as software that manages databases and provides functions like data definition, retrieval, updating and administration. It describes the characteristics of data in databases and advantages like redundancy control and data sharing. The document outlines the planning, analysis, design, implementation and maintenance phases of both the software development lifecycle and database development lifecycle. It also covers different database models like hierarchical, network and relational.
This chapter discusses database design and the systems development life cycle (SDLC). It explains that the SDLC traces the development of an information system through planning, analysis, design, implementation, and maintenance phases. Within the information system, the database life cycle (DBLC) describes the development of the database through initial study, design, implementation, testing, operation, and maintenance phases. The chapter also covers topics of conceptual database design, logical design, and physical design.
This document discusses database design and the systems development life cycle (SDLC). It explains that the SDLC traces the history of an information system through planning, analysis, design, implementation, and maintenance phases. Within the information system, the database life cycle (DBLC) describes the history of the database through initial study, design, implementation, testing, operation, and maintenance/evolution phases. The chapter also covers conceptual database design strategies like top-down vs. bottom-up and centralized vs. decentralized design.
Pr dc 2015 sql server is cheaper than open sourceTerry Bunio
SQL Server was found to be cheaper than open source options for a data warehouse project with the following requirements:
- Serve 100% operational reports from 1TB of data
- No need for advanced features like big data support
- Requirement was for basic textual reporting
An investigation was conducted of SQL Server, Oracle, Sybase, MySQL, and PostgreSQL. SQL Server and PostgreSQL were evaluated further based on costs and functionality. After a 10 year total cost of ownership analysis, SQL Server was found to be cheaper despite having a higher initial license cost. The lessons learned were that open source options are not always cheaper, to test options yourself rather than rely on biased reports, and that Oracle is very expensive.
The document discusses the database development life cycle (DDLC). It outlines the 6 phases of the DDLC: database planning, database design, implementation and downloading, testing and evaluation, operation, and maintenance and evolution. The database design phase is described as the most important, involving conceptual, logical, and physical design activities like data modeling, normalization, and validating the data model. The goals and key activities of each phase are summarized.
How to Design a Good Database for Your ApplicationNur Hidayat
The document provides an overview of a seminar on how to design a good database. It discusses:
- Characteristics of good database design such as considering the functionality needed from the database, breaking data into logical pieces, avoiding data separated by separators, and using a centralized name value table design.
- Steps for database design including creating conceptual, logical, and physical models and considering the use of natural versus surrogate keys and normalization versus denormalization.
- A case study example of designing an inventory system database that tracks items, warehouses, locations, and transactions. Requirements and entities for the conceptual, logical, and physical models are outlined.
The document discusses building a data warehouse in SQL Server. It provides an agenda that covers topics like an overview of data warehousing, data warehouse design, dimension and fact tables, and physical design. It also discusses components of a data warehousing solution like the data warehouse database, ETL processes, and security considerations.
This document provides an overview of database systems and concepts. It discusses how a database management system (DBMS) stores and manages data, defines various DBMS functions like security management and query languages, and describes different approaches to database development like the systems development life cycle and prototyping. It also explains the three schema architecture including the external, conceptual, and internal schemas and different levels of data abstraction.
Database Systems - Introduction to Database Design (Chapter 4/1)Vidyasagar Mundroy
This document discusses database design techniques and includes the following key points:
- It introduces database design approaches, which include bottom-up and top-down design. These differ in whether they start with attributes or entity relationships.
- The three phases of database design are discussed: conceptual, which focuses on user requirements; logical, which develops the data model; and physical, which includes implementation details.
- Other topics covered include data modeling, entity relationship modeling, normalization, and the importance of proper database design for application performance, extensibility, data integrity and security.
A database developer is responsible for designing, developing, and maintaining databases. They take databases from initial planning through implementation, ensuring effective functioning. Key responsibilities include designing data models, developing new database applications, maintaining database performance, and providing technical support. Database developers typically require a bachelor's degree in computer science or a related field and take on increased responsibilities with experience, correlating to higher salaries that can range from RM39,000 to over RM130,000 annually in Malaysia.
A database developer is responsible for designing, developing, and maintaining databases. They take databases from initial planning through implementation, ensuring effective functioning. Key responsibilities include designing data models, developing new database applications, maintaining database performance, and providing technical support. Database developers typically require a bachelor's degree in computer science or a related field and salaries range from RM39,000 to over RM130,000 depending on experience level.
Data Warehouse approaches with Dynamics AXAlvin You
Dynamics AX의 BI 구축을 위해 필요한 Data Warehouse 내용입니다.
• What is a Data Warehouse
• Data Warehouse Approaches
• Why Invest in a Data Warehouse
• Getting Started
• BI Models
• BI Solutions
It is quite possible to use Agile techniques for creating and maintaining a data architecture. Doing so will dramatically reduce the risk of failed data warehouse projects. This webinar will give you a quick overview of the benefits and challenges of Agile Data Modeling, Evolutionary Database Design, Agile Modeling, Conformed Dimensions, Bus Matrix, Database Refactoring, and an Agile framework for Agile data projects
Geek Sync | Is Your Database Environment Ready for DevOps?IDERA Software
You can watch the replay for this Geek Sync webcast, Is Your Database Environment Ready for DevOps?, in the IDERA Resource Center: http://ow.ly/oqr850A4pKu
Modern software development teams have adopted a continuous delivery approach based upon DevOps and agile development techniques. The small and frequent code changes that result from such an approach can deliver significant benefit in terms of reduced lead time for changes, a lower failure rate, and a reduced mean time to recovery when errors are encountered. So it is no wonder that the DevOps approach is rapidly becoming the de facto standard for application development and deployment.
But until recently, not enough focus has been placed on integrating database development and management into DevOps practices and procedures. Most of the automation and tooling focuses on coding, development, and deployment rather than on the design and administrative requirements of database systems. Failing to include proper database implementation best practices into DevOps will result in problems down-the-line, including poor performance, data integrity issues, difficult to maintain systems, and other problems.
This webinar will examine the core database administration practices that need to be integrated into your DevOps pipeline to achieve success. We will discuss critical tactics that need to be included in your database DevOps approach, and take a look at different ways to successfully integrate database administration into DevOps, without negating the advantages and benefits of DevOps.
Speaker: Craig S. Mullins is president and principal consultant of Mullins Consulting, Inc. where he focuses on data management strategy and consulting. He has been named by IBM as a Gold Consultant and he writes the monthly DBA Corner column for Database Trends & Applications magazine. Craig has over three decades of experience in all facets of database systems development and has written three popular books on database systems and administration. You can follow Craig on Twitter at @craigmullins.
This document discusses data operations management. It defines data operations management as developing, maintaining, and supporting structured data to maximize value. Key activities include database support and data technology management. Database administrators play an important role in ensuring database availability, performance, integrity, and recoverability through activities like backups, monitoring, tuning, and setting service level agreements.
CS3270 - DATABASE SYSTEM - Lecture (1)Dilawar Khan
This document outlines the key topics to be covered in a database course, including: understanding database concepts and the relational model, learning SQL for data manipulation and definition, database design techniques like entity-relationship modeling and normalization, and hands-on experience with Microsoft SQL Server. The course objectives are to help students understand databases and DBMS systems, apply relational concepts and SQL, and be able to design database applications. The document also provides an introduction to databases by comparing traditional file-based systems with the database approach.
The Kimball Lifecycle is a methodology for developing a data warehouse and business intelligence system. It consists of several phases: project planning, requirements gathering, dimensional modeling, ETL design, application development, deployment, maintenance, and growth. The lifecycle emphasizes gathering business requirements, designing dimensional data models and ETL processes, developing BI applications to meet user needs, and providing ongoing support and expansion of the system.
Decision support systems and business intelligenceShwetabh Jaiswal
This document discusses decision support systems and business intelligence. It describes how the modern business environment requires computerized systems to help with complex decision making. Business intelligence transforms raw data into useful information through methodologies, processes and technologies. Decision support systems couple individual expertise with computer capabilities to improve decision quality for semi-structured problems. Both systems use similar architectures of data warehouses, analytics, and user interfaces to enable analysis and informed decisions.
This document provides an overview of data warehousing, including its definition, types, components, architecture, database design, OLAP, and metadata repository. It discusses the differences between OLTP and data warehousing systems and describes the key steps in building a data warehouse, including data extraction, transformation, loading, storage, analysis, delivery of information to users, and ongoing management of the data warehouse system.
This document provides an overview of data warehousing, including its definition, types, components, architecture, database design, OLAP, and metadata repository. It discusses the differences between OLTP and data warehousing systems and describes the key steps in building a data warehouse, including data extraction, transformation, loading, storage, analysis, delivery of information to users, and ongoing management of the data warehouse system.
This document provides an overview of data warehousing, including its definition, types, components, architecture, database design, OLAP, and metadata repository. It discusses the differences between OLTP and data warehousing systems and describes the key steps in building a data warehouse, including data extraction, transformation, loading, storage, analysis, delivery of information to users, and ongoing management of the data warehouse system.
This document provides an overview of data warehousing, including its definition, types, components, architecture, database design, OLAP, and metadata repository. It discusses the differences between OLTP and data warehousing systems and describes the key steps in building a data warehouse, including data extraction, transformation, loading, storage, analysis, delivery of information to users, and ongoing management of the data warehouse system.
Main Java[All of the Base Concepts}.docxadhitya5119
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The document provides an overview of a seminar on how to design a good database. It discusses:
- Characteristics of good database design such as considering the functionality needed from the database, breaking data into logical pieces, avoiding data separated by separators, and using a centralized name value table design.
- Steps for database design including creating conceptual, logical, and physical models and considering the use of natural versus surrogate keys and normalization versus denormalization.
- A case study example of designing an inventory system database that tracks items, warehouses, locations, and transactions. Requirements and entities for the conceptual, logical, and physical models are outlined.
The document discusses building a data warehouse in SQL Server. It provides an agenda that covers topics like an overview of data warehousing, data warehouse design, dimension and fact tables, and physical design. It also discusses components of a data warehousing solution like the data warehouse database, ETL processes, and security considerations.
This document provides an overview of database systems and concepts. It discusses how a database management system (DBMS) stores and manages data, defines various DBMS functions like security management and query languages, and describes different approaches to database development like the systems development life cycle and prototyping. It also explains the three schema architecture including the external, conceptual, and internal schemas and different levels of data abstraction.
Database Systems - Introduction to Database Design (Chapter 4/1)Vidyasagar Mundroy
This document discusses database design techniques and includes the following key points:
- It introduces database design approaches, which include bottom-up and top-down design. These differ in whether they start with attributes or entity relationships.
- The three phases of database design are discussed: conceptual, which focuses on user requirements; logical, which develops the data model; and physical, which includes implementation details.
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A database developer is responsible for designing, developing, and maintaining databases. They take databases from initial planning through implementation, ensuring effective functioning. Key responsibilities include designing data models, developing new database applications, maintaining database performance, and providing technical support. Database developers typically require a bachelor's degree in computer science or a related field and take on increased responsibilities with experience, correlating to higher salaries that can range from RM39,000 to over RM130,000 annually in Malaysia.
A database developer is responsible for designing, developing, and maintaining databases. They take databases from initial planning through implementation, ensuring effective functioning. Key responsibilities include designing data models, developing new database applications, maintaining database performance, and providing technical support. Database developers typically require a bachelor's degree in computer science or a related field and salaries range from RM39,000 to over RM130,000 depending on experience level.
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Dynamics AX의 BI 구축을 위해 필요한 Data Warehouse 내용입니다.
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• Data Warehouse Approaches
• Why Invest in a Data Warehouse
• Getting Started
• BI Models
• BI Solutions
It is quite possible to use Agile techniques for creating and maintaining a data architecture. Doing so will dramatically reduce the risk of failed data warehouse projects. This webinar will give you a quick overview of the benefits and challenges of Agile Data Modeling, Evolutionary Database Design, Agile Modeling, Conformed Dimensions, Bus Matrix, Database Refactoring, and an Agile framework for Agile data projects
Geek Sync | Is Your Database Environment Ready for DevOps?IDERA Software
You can watch the replay for this Geek Sync webcast, Is Your Database Environment Ready for DevOps?, in the IDERA Resource Center: http://ow.ly/oqr850A4pKu
Modern software development teams have adopted a continuous delivery approach based upon DevOps and agile development techniques. The small and frequent code changes that result from such an approach can deliver significant benefit in terms of reduced lead time for changes, a lower failure rate, and a reduced mean time to recovery when errors are encountered. So it is no wonder that the DevOps approach is rapidly becoming the de facto standard for application development and deployment.
But until recently, not enough focus has been placed on integrating database development and management into DevOps practices and procedures. Most of the automation and tooling focuses on coding, development, and deployment rather than on the design and administrative requirements of database systems. Failing to include proper database implementation best practices into DevOps will result in problems down-the-line, including poor performance, data integrity issues, difficult to maintain systems, and other problems.
This webinar will examine the core database administration practices that need to be integrated into your DevOps pipeline to achieve success. We will discuss critical tactics that need to be included in your database DevOps approach, and take a look at different ways to successfully integrate database administration into DevOps, without negating the advantages and benefits of DevOps.
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This document discusses data operations management. It defines data operations management as developing, maintaining, and supporting structured data to maximize value. Key activities include database support and data technology management. Database administrators play an important role in ensuring database availability, performance, integrity, and recoverability through activities like backups, monitoring, tuning, and setting service level agreements.
CS3270 - DATABASE SYSTEM - Lecture (1)Dilawar Khan
This document outlines the key topics to be covered in a database course, including: understanding database concepts and the relational model, learning SQL for data manipulation and definition, database design techniques like entity-relationship modeling and normalization, and hands-on experience with Microsoft SQL Server. The course objectives are to help students understand databases and DBMS systems, apply relational concepts and SQL, and be able to design database applications. The document also provides an introduction to databases by comparing traditional file-based systems with the database approach.
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This document provides an overview of data warehousing, including its definition, types, components, architecture, database design, OLAP, and metadata repository. It discusses the differences between OLTP and data warehousing systems and describes the key steps in building a data warehouse, including data extraction, transformation, loading, storage, analysis, delivery of information to users, and ongoing management of the data warehouse system.
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2. Agenda
• What is Database Design?
• Why Database Design is Important ?
• Database development life cycle?
• Requirements analysis?
• Database designing?
• Implementation?
• Types of Database Techniques?
KS
channel
3. Database Design
• Database Design is a collection of processes that facilitate.
• designing
• Development
• implementation and maintenance of enterprise data management
systems.
• It helps produce database systems
• The main objectives of database designing are to produce logical
and physical designs models of the proposed database system
KS
channel
4. Why Database Design is Important
• Database designing is crucial to high performance database
system.
• Apart from improving the performance, properly designed
database are
• easy to maintain
• improve data consistency and are cost effective in terms of
disk storage space.
KS
channel
6. Requirements analysis
• Planning - This stages concerns with planning of entire Database
Development Life Cycle It takes into consideration the Information
Systems strategy of the organization.
• System definition - This stage defines the scope and boundaries
of the proposed database system.
KS
channel
7. Database designing
• Logical model - This stage is concerned with developing a
database model based on requirements. The entire design is on
paper without any physical implementations or specific DBMS
considerations.
• Physical model - This stage implements the logical model of the
database considering the DBMS and physical implementation
factors.
KS
channel
8. Implementation
• Data conversion and loading - this stage is concerned with
importing and converting data from the old system into the new
database.
• Testing - this stage is concerned with the identification of errors in
the newly implemented system .It checks the database against
requirement specifications.
KS
channel
9. Types of Database Techniques
• Normalization
• ER Modeling
KS
channel