Data Modeling

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This is the process of creating a detailed representation of a database structure, including relationships between data entities and constraints on that data.

Entity-Relationship Diagrams (ERD): ERD is a graphical representation of the entities, attributes, and relationships between them in a database. It is the foundation of data modeling and helps to visualize the data schema.
Normalization: Normalization is a process of organizing data in a database and is used to avoid redundancy and dependency. It ensures that the database is structured efficiently and avoids anomalies that could lead to data loss.
Data Modeling Techniques: These techniques are used to create a data model and define data requirements, such as conceptual, logical and physical data modeling.
Data Architecture: Data architecture defines the structure, integration, and operations of data in an organization. It includes data models, policies, rules, standards, and frameworks.
Data Warehouse and Data Mart: A data warehouse is a centralized repository of data that is used to support business intelligence activities. On the other hand, a data mart is a subset of a data warehouse that is designed for a specific business unit or department.
Data Governance: Data governance is a process that ensures the quality, consistency, and security of data in an organization. It includes roles, responsibilities, policies, standards, and processes that ensure data is managed effectively.
ETL (Extract, Transform, Load): ETL is a process used to extract data from various sources, transform it into a common format, and load it into a destination database.
Dimensional Modeling: Dimensional modeling is a technique used to design data warehouses and data marts. It involves creating dimensions and fact tables to represent business processes.
Master Data Management: Master data management is a process that ensures that master data is consistent, accurate, and available to all users. It involves defining and managing a single source of truth for key business data.
Business Intelligence: Business intelligence is the process of turning raw data into insights that can be used to make informed business decisions. It includes technologies, tools, and processes used to collect, analyze, and present data.
Entity-Relationship (ER) Modeling: A technique to represent the relationships between different entities in a business system. ER modeling lays out the structure of databases in a visual manner to help in proper data organization.
Data Flow Diagram (DFD) Modeling: A graphical representation that displays how data flows through a system. This modeling is used to improve data functionality across different levels of business data processing.
UML (Unified Modeling Language): An object-oriented modeling language that supports a graphical notation for specifying, constructing, and documenting software models throughout their lifecycle.
Dimensional Modeling: A technique used mainly by data warehouse designers to model the data architecture. It involves organizing business data into workable structures called dimensions and fact tables.
Conceptual Modeling: A high-level modeling technique that provides an overview of business processes, data, and activities. A conceptual model represents a simple yet comprehensive view of the business entities and their relationships to comprehensively understand the business.
Object-Oriented Modeling: A modeling technique that focuses on identifying objects and their relationships to develop software applications. It is majorly used for developing complex business applications.
Logical Modeling: A modeling technique that describes the layout of data and the relation between tables without being restricted or tied to any particular physical schema or database structure.
Physical Modeling: A precise modeling technique that represents the physical data layout of a database. It involves constructing database tables, data types, and various constraints.
Data Vault Modeling: A modeling technique used in business intelligence systems for modern-day analytics. Data vault modeling encapsulates the concepts of ER and Dimensional modeling, focusing on the historical data of businesses.
"Data modeling in software engineering is the process of creating a data model for an information system by applying certain formal techniques."
"It may be applied as part of broader Model-driven engineering (MDD) concept."
"The process of creating a data model for an information system by applying certain formal techniques."
"It may be applied as part of broader Model-driven engineering (MDD) concept."
"Creating a data model for an information system by applying certain formal techniques."
"Data modeling in software engineering is the process of creating a data model for an information system by applying certain formal techniques."
"The process of creating a data model for an information system."
"A data model for an information system."
"The process of creating a data model for an information system by applying certain formal techniques."
"It may be applied as part of broader Model-driven engineering (MDD) concept."
"Applying certain formal techniques."
"Creating a data model for an information system."
"The process of creating a data model for an information system."
"A data model for an information system."
"It may be applied as part of broader Model-driven engineering (MDD) concept."
"The process of creating a data model for an information system by applying certain formal techniques."
"The process of creating a data model for an information system by applying certain formal techniques."
"Creating a data model for an information system."
"It may be applied as part of broader Model-driven engineering (MDD) concept."
"Creating a data model for an information system by applying certain formal techniques."