Slide 2 data models

This document provides an introduction and overview of databases and the basic operations used to manage data in a database using Microsoft Access 2007. It defines what a database is, how data is organized in tables with rows and columns, and when it is appropriate to use a database. It also outlines and provides examples of the basic CRUD (create, read, update, delete) operations used in structured query language (SQL) to manipulate data, including inserting, selecting, updating, and deleting records from database tables.

SSIS is a platform for data integration and workflows that allows users to extract, transform, and load data. It can connect to many different data sources and send data to multiple destinations. SSIS provides functionality for handling errors, monitoring data flows, and restarting packages from failure points. It uses a graphical interface that facilitates transforming data without extensive coding.

The document defines metadata as data about data that provides a summary and roadmap for a data warehouse. It discusses three main types of metadata: business metadata which contains ownership and definition information; technical metadata which includes database structure and attributes; and operational metadata which tracks data currency and lineage. Finally, the document outlines the key roles of metadata as a directory to locate data warehouse content and map data transformations, and notes that correctly defining stored metadata presents a challenge.

This document provides an overview of database management systems and related concepts. It discusses data hierarchy, traditional file processing, the database approach to data management, features and capabilities of database management systems, database schemas, components of database management systems, common data models including hierarchical, network, and relational models, and the process of data normalization.

This document discusses data warehousing, including its definition, importance, components, strategies, ETL processes, and considerations for success and pitfalls. A data warehouse is a collection of integrated, subject-oriented, non-volatile data used for analysis. It allows more effective decision making through consolidated historical data from multiple sources. Key components include summarized and current detailed data, as well as transformation programs. Common strategies are enterprise-wide and data mart approaches. ETL processes extract, transform and load the data. Clean data and proper implementation, training and maintenance are important for success.

ETL (Extract, Transform, Load) is a process that allows companies to consolidate data from multiple sources into a single target data store, such as a data warehouse. It involves extracting data from heterogeneous sources, transforming it to fit operational needs, and loading it into the target data store. ETL tools automate this process, allowing companies to access and analyze consolidated data for critical business decisions. Popular ETL tools include IBM Infosphere Datastage, Informatica, and Oracle Warehouse Builder.

This document defines database and DBMS, describes their advantages over file-based systems like data independence and integrity. It explains database system components and architecture including physical and logical data models. Key aspects covered are data definition language to create schemas, data manipulation language to query data, and transaction management to handle concurrent access and recovery. It also provides a brief history of database systems and discusses database users and the critical role of database administrators.

This document provides an overview of non-relational (NoSQL) databases. It discusses the history and characteristics of NoSQL databases, including that they do not require rigid schemas and can automatically scale across servers. The document also categorizes major types of NoSQL databases, describes some popular NoSQL databases like Dynamo and Cassandra, and discusses benefits and limitations of both SQL and NoSQL databases.

The document discusses dimensional modeling and data warehousing. It describes how dimensional models are designed for understandability and ease of reporting rather than updates. Key aspects include facts and dimensions, with facts being numeric measures and dimensions providing context. Slowly changing dimensions are also covered, with types 1-3 handling changes to dimension attribute values over time.

This document provides an overview of databases and database management systems (DBMS). It discusses how databases evolved from file systems to address flaws in data management. It describes what a DBMS is and its functions in managing the database structure and controlling data access. The document also summarizes different database models including hierarchical, network, relational, entity-relationship, and object-oriented models. It highlights advantages and disadvantages of each model.

The document compares conceptual, logical, and physical data models. Conceptual models show entities and relationships without attributes or keys. Logical models add attributes, primary keys, and foreign keys. Physical models specify tables, columns, data types, and foreign keys to represent the database implementation. The complexity increases from conceptual to logical to physical models.

DBMS stores data as files while RDBMS stores data in tabular form with relationships between tables. DBMS is meant for small organizations and single users, does not support normalization, and lacks security features. RDBMS supports large data, multiple users, normalization, security, distributed databases, and examples include MySQL, PostgreSQL, and Oracle. The key difference is that RDBMS represents data in tables with relationships while DBMS stores data as files without relationships.

The document compares file systems and database management systems (DBMS) for storing a company's 500GB of employee, department, product, and sales data. It notes several drawbacks of using a file system, including data redundancy, integrity issues, restricted concurrent access, and lack of flexibility. It then outlines key advantages of using a DBMS instead, such as data sharing, enforcement of security and integrity, reduction of redundancy, and support for concurrent access and crash recovery.

This document discusses object-relational and extended relational databases. It begins with an introduction and agenda. It then covers database design for ORDBMS, including complex data types, structured types, type inheritance, and array/multiset types. It discusses creating and querying collection-valued attributes. Finally, it covers nesting and unnesting relations to transform between normalized and denormalized forms. The key topics covered in 3 sentences or less are: database design for ORDBMS supports objects, classes, and inheritance; structured types allow user-defined complex attributes; type inheritance and subtables allow modeling specialization hierarchies; and arrays and multisets allow modeling ordered and unordered collections as attributes.

The document provides an introduction to database management systems (DBMS) and database models. It defines key terms like data, database, DBMS, file system vs DBMS. It describes the evolution of DBMS from 1960 onwards and different database models like hierarchical, network and relational models. It also discusses the roles of different people who work with databases like database designers, administrators, application programmers and end users.