Database Systems Introduction To Databases And Data Warehouses Free Pdf Download

Database Systems: Introduction to Databases and Data Warehouses covers the design and use of both operational and analytical database systems in detail. A solid theoretical foundation and meaningful hands-on experiences are provided. This understanding prepares students to apply their knowledge in today's business environments.

Save time with running examples

The ZAGI Retail Company Sales Department Database Example is used to illustrate the main concepts for both operational databases and data warehouses. Use of a running example enables quick and straightforward coverage, because students are already familiar with the details of the case.

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Susan V. Vrbsky is an associate professor and interim department head of Computer Science at the University of Alabama. Dr. Vrbsky is the director of the Cloud and Cluster Computing Laboratory. Her research has explored topics including data intensive computing, real-time databases, database security, mobile databases, and green computing. She has co-authored over 100 peer-reviewed publications.

Svetlozar Evtimov Nestorov is an Assistant Professor in the Quinlan School of Business at Loyola University Chicago. His research and teaching focus on data mining, data warehousing, and web technologies.

An introductory, yet comprehensive, database textbook intended for use in undergraduate and graduate information systems database courses. This text also provides practical content to current and aspiring information systems, business data analysis, and decision support industry professionals.

Database Systems: Introduction to Databases and Data Warehouses covers both analytical and operations database as knowledge of both is integral to being successful in today's business environment. It also provides a solid theoretical foundation and hands-on practice using an integrated web-based data-modeling suite.

A database is information that is set up for easy access, management and updating. Computer databases typically store aggregations of data records or files that contain information, such as sales transactions, customer data, financials and product information.

Databases were first created in the 1960s. These early databases were network models where each record is related to many primary and secondary records. Hierarchical databases were also among the early models. They have tree schemas with a root directory of records linked to several subdirectories.

E.F. Codd created the relational database while at IBM. It became the standard for database systems because of its logical schema, or the way it is organized. The use of a logical schema separates the relational database from physical storage.

There are many types of databases. They may be classified according to content type: bibliographic, full text, numeric and images. In computing, databases are often classified based on the organizational approach they use.

Relational. This tabular approach defines data so it can be reorganized and accessed in many ways. Relational databases are comprised of tables. Data is placed into predefined categories in those tables. Each table has columns with at least one data category, and rows that have a certain data instance for the categories which are defined in the columns. Information in a relational database about a specific customer is organized into rows, columns and tables. These are indexed to make it easier to search using SQL or NoSQL queries.

Relational databases use SQL in their user and application program interfaces. A new data category can easily be added to a relational database without having to change the existing applications. A relational database management system (RDBMS) is used to store, manage, query and retrieve data in a relational database.

Typically, the RDBMS gives users the ability to control read/write access, specify report generation and analyze use. Some databases offer atomicity, consistency, isolation and durability, or ACID, compliance to guarantee that data is consistent and that transactions are complete.

Distributed databases can be homogeneous, where all physical locations have the same underlying hardware and run the same operating systems and database applications. They can also be heterogeneous. In those cases, the hardware, OS and database applications can be different in the various locations.

Cloud. These databases are built in a public, private or hybrid cloud for a virtualized environment. Users are charged based on how much storage and bandwidth they use. They also get scalability on demand and high availability. These databases can work with applications deployed as software as a service.

NoSQL. NoSQL databases are good when dealing with large collections of distributed data. They can address big data performance issues better than relational databases. They also do well analyzing large unstructured data sets and data on virtual servers in the cloud. These databases can also be called non-relational databases.

Object-oriented. These databases hold data created using object-oriented programming languages. They focus on organizing objects rather than actions and data rather than logic. For instance, an image data record would be a data object, rather than an alphanumeric value.

Graph. These databases are a type of NoSQL database. They store, map and query relationships using concepts from graph theory. Graph databases are made up of nodes and edges. Nodes are entities and connect the nodes.

Graph databases use SPARQL, a declarative programming language and protocol, for analytics. SPARQL can perform all the analytics that SQL can perform, and can also be used for semantic analysis, or the examination of relationships. This makes it useful for performing analytics on data sets that have both structured and unstructured data. SPARQL lets users perform analytics on information stored in a relational database, as well as friend-of-a-friend relationships, PageRank and shortest path.

The DBMS provides physical and logical independence from data. Users and applications do not need to know either the physical or logical locations of data. A DBMS can also limit and control access to the database and provide different views of the same database schema to multiple users.

A database management system (DBMS) is system software for creating and managing databases. A DBMS makes it possible for end users to create, protect, read, update and delete data in a database. The most prevalent type of data management platform, the DBMS essentially serves as an interface between databases and users or application programs, ensuring that data is consistently organized and remains easily accessible.

The DBMS manages the data; the database engine allows data to be accessed, locked and modified; and the database schema defines the database's logical structure. These three foundational elements help provide concurrency, security, data integrity and uniform data administration procedures. The DBMS supports many typical database administration tasks, including change management, performance monitoring and tuning, security, and backup and recovery. Most database management systems are also responsible for automated rollbacks and restarts as well as logging and auditing of activity in databases and the applications that access them.

The DBMS provides a centralized view of data that can be accessed by multiple users from multiple locations in a controlled manner. A DBMS can limit what data end users see and how they view the data, providing many views of a single database schema. End users and software programs are free from having to understand where the data is physically located or on what type of storage medium it resides because the DBMS handles all requests.

The DBMS can offer both logical and physical data independence to protect users and applications from having to know where data is stored or from being concerned about changes to the physical structure of data. So long as programs use the application programming interface (API) for the database that the DBMS provides, developers won't have to modify programs just because changes have been made to the database.

A DBMS is a sophisticated piece of system software consisting of multiple integrated components that deliver a consistent, managed environment for creating, accessing and modifying data in databases. These components include the following:

NoSQL DBMS. Well-suited for loosely defined data structures that may evolve over time, NoSQL DBMS may require more application involvement for schema management. There are four types of NoSQL database systems: document databases, graph databases, key-value stores and wide-column stores. Each uses a different type of data model, resulting in significant differences between each NoSQL type.

NewSQL DBMS. Modern relational systems that use SQL, NewSQL database systems offer the same scalable performance as NoSQL systems. But NewSQL systems also provide ACID (atomicity, consistency, isolation and durability) support for data consistency. A NewSQL DBMS is engineered as a relational, SQL database system with a distributed, fault-tolerant architecture. Other typical features of NewSQL system offerings include in-memory capability and clustered database services with the ability to be deployed in the cloud. Many NewSQL DBMS packages have fewer features and components and a smaller footprint than legacy relational offerings, making them easier to support and understand. Some vendors now eschew the NewSQL label and describe their technologies as distributed SQL databases. CockroachDB, Google Cloud Spanner, NuoDB, Volt Active Data and YugabyteDB are examples of database systems in this category.

IMDBMS. An in-memory database management system predominantly relies on main memory for data storage, management and manipulation. By reducing the latency associated with reading from disk, an IMDBMS can provide faster response times and better performance but can consume more resources. Therefore, an in-memory database is ideal for applications that require high performance and rapid access to data, such as data stores that support real-time HTAP (hybrid transactional and analytical process). Any type of DBMS (relational, NoSQL, etc.) can also support in-memory processing. SAP HANA and Redis are examples of in-memory database systems. e24fc04721