data design and component design

1. Data Designs
Software Engineering.

2. KEY CONCEPTS
DATA: Data describes a real-world information
resource that is important to your application.
Data describes the things, people, products,
items, customers, assets, records, and —
ultimately — data structures that your
application finds useful to categorize, organize,
and maintain.
DESIGN : has been described as a multistep
process in which representations of data and
program structure, interface characteristics, and
procedural detail are synthesized from
information requirements. In general we can say
that the DESIGN IS INFORMATION DRIVEN.

3. SOFTWARE ARCHITECTURE : of a program or
computing system is the structure or structures
of the system, which comprise software
components, the externally visible properties of
those components, and the relationship among
them. The architecture is not the operational
software rather is a representation that enables
to :
Analyze the effectiveness of the design in
meeting its stated requirements.
Consider architectural alternatives and,
Reduce risks associated with the construction of
the software.

4. Now, what does the term
“software components” means ?
In the context of architectural design, a software
component can be something as simple as a
program module or an object-oriented class but,
It can also be extended to include databases and
can also enable the configuration of a network of
clients and servers.

5. Software Architecture
considers two levels of the
design pyramid :
Architectural
Design
Data/class
Design

6. DATA DESIGN
The data design action translates data objects into data
structures at the software component level.
Data Design is the first and most important design activity.
Here the main issue is to select the appropriate data
structure i.e. the data design focuses on the definition of
data structures.
Data design is a process of gradual refinement, from the
coarse "What data does your application require?" to the
precise data structures and processes that provide it. With
a good data design, your application's data access is fast,
easily maintained, and can gracefully accept future data
enhancements.

7. Data Design Includes :
Identifying the data.
Defining specific data types & storage
mechanisms.
Insuring data integrity by using business rules and
other run-time enforcement mechanisms.

8. Concepts in Data Design:
 Data Modeling: Data modeling is the initial step in data design. It
involves creating a conceptual representation of the data and its
relationships within the software system. This is often done using
techniques like Entity-Relationship Diagrams (ERDs) or Unified
Modeling Language (UML) class diagrams. These diagrams depict
entities (such as objects, concepts, or people) and their attributes, as
well as the relationships between these entities.
 Normalization: Normalization is the process of organizing data in a
database to reduce redundancy and improve data integrity. This
involves breaking down large tables into smaller ones and using
relationships between these tables to link data logically. Normalization
helps prevent anomalies like data duplication and ensures efficient
querying and maintenance.

9.  Data Storage: Data can be stored in various forms, including relational
databases, NoSQL databases (such as document, key-value, columnar, or
graph databases), and even flat files. The choice of data storage depends
on factors like data volume, complexity, access patterns, and
performance requirements.
 Data Structures: Data structures refer to the way data is organized and
stored in memory or on disk. In software engineering, you often work
with various data structures like arrays, linked lists, trees, graphs, and
hash tables. These structures impact the efficiency of data retrieval,
insertion, and deletion operations.
 Indexing: Indexing involves creating indexes on specific columns in a
database table to speed up data retrieval. Indexes act like a roadmap,
allowing the database management system to quickly locate data based
on specific criteria. However, over-indexing can lead to performance
issues during data insertion and updates.

10.  Data Integrity: Ensuring data integrity is vital in data design. It involves
setting constraints, such as unique constraints or foreign key constraints,
to maintain the accuracy and consistency of data. This prevents the
insertion of erroneous or inconsistent data into the system.
 Data Security: Data design also includes considering security aspects,
such as access control, encryption, and data masking. Sensitive data
should be protected from unauthorized access and potential breaches.
 Scalability: Data design should accommodate scalability requirements. As
the application grows and more data is generated, the data storage
mechanisms should be capable of handling increased loads without
sacrificing performance.

11. Process of Data Design:
 Requirements Analysis: Understand the application's data requirements, including the
types of data to be stored, relationships between data entities, and anticipated usage
patterns.
 Conceptual Design: Create a high-level data model that outlines entities, attributes, and
relationships. This model abstracts the actual implementation details.
 Logical Design: Transform the conceptual model into a logical model that represents how
the data will be organized in a database. Apply normalization techniques to minimize
redundancy and improve data integrity.
 Physical Design: Translate the logical design into an actual database schema, choosing
specific data storage mechanisms, defining data types, and creating indexes.
 Implementation: Develop the necessary code to interact with the data storage
mechanisms, including database queries, data retrieval, and data manipulation
operations.
 Testing: Test the data design to ensure that data is stored, retrieved, and manipulated
correctly. Performance testing is essential to identify bottlenecks and optimize query
performance.
 Optimization and Maintenance: Continuously monitor the data design for performance
issues and make necessary optimizations. As the application evolves, the data design
might need to be updated to accommodate new requirements.

12. Data Design at the
Architectural Level.
The challenge is to extract useful information
from dozens of databases serving many
applications encompassing hundreds of gigabytes
of data, particularly when the information
desired is cross functional.
To combat this challenge data mining techniques,
also called KNOWLEDGE DISCOVERY IN
DATABASES (KDD) are developed, that navigate
through existing databases in order to extract
appropriate business-level information.

13. An Alternative solution called DATA
WAREHOUSE, adds additional layer to data
architecture. Data Warehouse is a separate
data environment that is not directly
integrated with day to day applications but
encompasses all data used by a business. In a
way it is a large, independent database that
access to the data that are stored in
databases that serve the set if applications
required by a business.

14. Data Design at the
Component Level.
Data Design at the component level focuses on
the representation of data structures that are
directly accessed by one or more software
components.

15. What Actually these Architectural
and component level elements
mean ?
The ARCHITECTURAL DESIGN for the software is
equivalent to the floor plan of a house, which
depicts the overall layout of the rooms, their
size, shape, and relationship to one another.
ARCHITECTURAL DESIGN ELEMENTS gives us an
overall view of the software.

16. COMPONENT DESIGN for the software is equivalent
to the set of detailed drawings for each room in the
house. These drawings depict wiring and plumbing
within each room, the switches, showers, tubs,
drain, the flooring to be used and every other
detail related with the room.
COMPONENT LEVEL DESIGN ELEMENTS for software
fully define the internal detail of each software
component.

17. Concepts in Component-Level Design:
 Modularity: Modularity is a central concept in component-level design. It
involves dividing a complex system into smaller, self-contained modules
or components. Each module addresses a specific aspect of functionality,
making the system easier to understand, develop, test, and maintain.
 Cohesion: Cohesion refers to how closely the responsibilities and tasks
within a component are related. High cohesion implies that a
component focuses on a specific, well-defined purpose, while low
cohesion indicates that a component may have multiple unrelated
responsibilities. Components with high cohesion are easier to
comprehend and maintain.
 Coupling: Coupling measures the degree of interdependence between
components. Low coupling implies that components are relatively
independent and can be modified without affecting other components.
High coupling increases the complexity of changes and may lead to
unintended side effects when modifying components.

18.  Interfaces: Components interact with each other through well-defined
interfaces. An interface specifies the methods, functions, or communication
protocols that other components can use to interact with a particular
component. Clear and consistent interfaces facilitate integration and
communication between components.
 Abstraction: Abstraction involves hiding complex implementation details and
exposing only the necessary functionality and information to other
components. This simplifies the interaction between components and allows
changes to be made to the underlying implementation without affecting the
rest of the system.
 Information Hiding: Information hiding restricts direct access to internal data
and methods of a component, exposing only what is necessary for external
interactions. This prevents unintended modification of internal state and
encourages the use of defined interfaces.
 Reusability: Well-designed components are often reusable in different parts
of the system or even across different projects. Reusability reduces
development effort and promotes consistency in software development.

19. Process of Component-Level
Design:
 Requirement Analysis: Understand the functional and non-functional
requirements of the system. Identify the major functionalities that need to
be implemented.
 Identify Components: Identify the components required to implement the
functionalities specified in the high-level design. Break down the system
into smaller, manageable units of functionality.
 Define Component Interfaces: Specify the interfaces for each component.
These interfaces should define the methods, inputs, outputs, and
communication protocols required for interactions between components.

20.  Design Internal Structure: For each component, design its internal
structure, including data structures, algorithms, and methods. Ensure that
the component's responsibilities are well-defined and cohesive.
 Ensure Cohesion and Low Coupling: Aim for high cohesion within each
component and minimize coupling between components. This promotes
maintainability and flexibility.
 Implement Components: Develop the code for each component according
to the defined interfaces and internal design. Follow programming best
practices to ensure the quality and readability of the code.
 Testing: Test each component in isolation using unit tests to verify its
correctness and functionality. Additionally, conduct integration testing to
ensure that components interact as expected.

21.  Documentation: Document the purpose, functionality, interfaces, and usage instructions
for each component. This documentation aids in understanding and using the
components in the future.
 Integration: Integrate the components to form the complete system. Test the integrated
system to identify and address any issues that arise during component interaction.
 Optimization and Refinement: Analyze the system's performance and identify areas for
optimization. Refine the design and implementation as needed to improve efficiency
and maintainability.
 Maintenance: As the system evolves, continue to maintain, update, and enhance the
components to meet changing requirements.
 In conclusion, component-level design is a crucial phase in software engineering that
involves decomposing a system into modular components with well-defined interfaces
and responsibilities. By focusing on modularity, cohesion, coupling, and clear interfaces,
component-level design promotes software that is easier to develop, test, maintain, and
scale.