Learn how to handle many-to-many relationships in database design with Many-to-many: Multiple records in one table are related to multiple records in another table. 6, Programming. 7, Law. 8, Commerce. 9, Physical Education Hibernate Tips is a series of posts in which I describe a quick and easy. RDBMSs allow you to link data across multiple databases, which enables . The power of a relational database is in the links and relations. One-to-Many: One row in one table is connected to zero, one, or more than one rows in another table. .. The term is also used to describe high-level concepts like relational, flat -file. describe the differences between data, information, and knowledge;; define the term You have already been introduced to the first two components of information The goal of many information systems is to transform data into information in order In a relational database, all the tables are related by one or more fields.
The Difference between a Database and a Spreadsheet Many times, when introducing the concept of databases to students, they quickly decide that a database is pretty much the same as a spreadsheet. After all, a spreadsheet stores data in an organized fashion, using rows and columns, and looks very similar to a database table. This misunderstanding extends beyond the classroom: To be fair, for simple uses, a spreadsheet can substitute for a database quite well.
If a simple listing of rows and columns a single table is all that is needed, then creating a database is probably overkill. In our Student Clubs example, if we only needed to track a listing of clubs, the number of members, and the contact information for the president, we could get away with a single spreadsheet.
However, the need to include a listing of events and the names of members would be problematic if tracked with a spreadsheet. A database allows data from several entities such as students, clubs, memberships, and events to all be related together into one whole. Though not good for replacing databases, spreadsheets can be ideal tools for analyzing the data stored in a database.
How to Handle a Many-to-Many Relationship in Database Design
A spreadsheet package can be connected to a specific table or query in a database and used to create charts or perform analysis on that data. Structured Query Language Once you have a database designed and loaded with data, how will you do something useful with it? Almost all applications that work with databases such as database management systems, discussed below make use of SQL as a way to analyze and manipulate relational data.
As its name implies, SQL is a language that can be used to work with a relational database. From a simple request for data to a complex update operation, SQL is a mainstay of programmers and database administrators.
To give you a taste of what SQL might look like, here are a couple of examples using our Student Clubs database. The following query will retrieve a list of the first and last names of the club presidents: President" The following query will create a list of the number of students in each club, listing the club name and then the number of members: Club ID" An in-depth description of how SQL works is beyond the scope of this introductory text, but these examples should give you an idea of the power of using SQL to manipulate relational data.
Many database packages, such as Microsoft Access, allow you to visually create the query you want to construct and then generate the SQL query for you. Other Types of Databases The relational database model is the most used database model today. However, many other database models exist that provide different strengths than the relational model.
NoSQL arose from the need to solve the problem of large-scale databases spread over several servers or even across the world. For a relational database to work properly, it is important that only one person be able to manipulate a piece of data at a time, a concept known as record-locking.
A NoSQL database can work with data in a looser way, allowing for a more unstructured environment, communicating changes to the data over time to all the servers that are part of the database. Database Management Systems Screen shot of the Open Office database management system To the computer, a database looks like one or more files. In order for the data in the database to be read, changed, added, or removed, a software program must access it. Many software applications have this ability: But what about applications to create or manage a database?
That is the purpose of a category of software applications called database management systems DBMS. DBMS packages generally provide an interface to view and change the design of the database, create queries, and develop reports. Most of these packages are designed to work with a specific type of database, but generally are compatible with a wide range of databases.
For example, Apache OpenOffice. Both Access and Base have the ability to read and write to other database formats as well. Microsoft Access and Open Office Base are examples of personal database-management systems.
These systems are primarily used to develop and analyze single-user databases. These databases are not meant to be shared across a network or the Internet, but are instead installed on a particular device and work with a single user at a time.
Enterprise Databases A database that can only be used by a single user at a time is not going to meet the needs of most organizations. As computers have become networked and are now joined worldwide via the Internet, a class of database has emerged that can be accessed by two, ten, or even a million people.
These databases are sometimes installed on a single computer to be accessed by a group of people at a single location. Other times, they are installed over several servers worldwide, meant to be accessed by millions. These relational enterprise database packages are built and supported by companies such as Oracle, Microsoft, and IBM. The open-source MySQL is also an enterprise database. As stated earlier, the relational database model does not scale well.
The term scale here refers to a database getting larger and larger, being distributed on a larger number of computers connected via a network. Some companies are looking to provide large-scale database solutions by moving away from the relational model to other, more flexible models. Developers can use the App Engine Datastore to develop applications that access data from anywhere in the world.
Big Data A new buzzword that has been capturing the attention of businesses lately is big data. The term refers to such massively large data sets that conventional database tools do not have the processing power to analyze them.
For example, Walmart must process over one million customer transactions every hour. Storing and analyzing that much data is beyond the power of traditional database-management tools. Understanding the best tools and techniques to manage and analyze these large data sets is a problem that governments and businesses alike are trying to solve. The metadata about that value would be the field name Year of Birth, the time it was last updated, and the data type integer.
Another example of metadata could be for an MP3 music file, like the one shown in the image below; information such as the length of the song, the artist, the album, the file size, and even the album cover art, are classified as metadata. Metadata about a camera image Public Domain Data Warehouse As organizations have begun to utilize databases as the centerpiece of their operations, the need to fully understand and leverage the data they are collecting has become more and more apparent.
However, directly analyzing the data that is needed for day-to-day operations is not a good idea; we do not want to tax the operations of the company more than we need to. Further, organizations also want to analyze data in a historical sense: How does the data we have today compare with the same set of data this time last month, or last year?
From these needs arose the concept of the data warehouse. The concept of the data warehouse is simple: However, the execution of this concept is not that simple. A data warehouse should be designed so that it meets the following criteria: It uses non-operational data.
Chapter 4: Data and Databases – Information Systems for Business and Beyond
This means that the data warehouse is using a copy of data from the active databases that the company uses in its day-to-day operations, so the data warehouse must pull data from the existing databases on a regular, scheduled basis. The data is time-variant. This means that whenever data is loaded into the data warehouse, it receives a time stamp, which allows for comparisons between different time periods.
The data is standardized. Because the data in a data warehouse usually comes from several different sources, it is possible that the data does not use the same definitions or units. In order for the data warehouse to match up dates, a standard date format would have to be agreed upon and all data loaded into the data warehouse would have to be converted to use this standard format.
This process is called extraction-transformation-load ETL. There are two primary schools of thought when designing a data warehouse: The bottom-up approach starts by creating small data warehouses, called data marts, to solve specific business problems. As these data marts are created, they can be combined into a larger data warehouse. The top-down approach suggests that we should start by creating an enterprise-wide data warehouse and then, as specific business needs are identified, create smaller data marts from the data warehouse.
Data warehouse process top-down Benefits of Data Warehouses Organizations find data warehouses quite beneficial for a number of reasons: The process of developing a data warehouse forces an organization to better understand the data that it is currently collecting and, equally important, what data is not being collected. A data warehouse provides a centralized view of all data being collected across the enterprise and provides a means for determining data that is inconsistent.
Once all data is identified as consistent, an organization can generate one version of the truth. This is important when the company wants to report consistent statistics about itself, such as revenue or number of employees. By having a data warehouse, snapshots of data can be taken over time. This creates a historical record of data, which allows for an analysis of trends. A data warehouse provides tools to combine data, which can provide new information and analysis. Data Mining Data mining is the process of analyzing data to find previously unknown trends, patterns, and associations in order to make decisions.
Generally, data mining is accomplished through automated means against extremely large data sets, such as a data warehouse.
Some examples of data mining include: An analysis of sales from a large grocery chain might determine that milk is purchased more frequently the day after it rains in cities with a population of less than 50, A bank may find that loan applicants whose bank accounts show particular deposit and withdrawal patterns are not good credit risks. A baseball team may find that collegiate baseball players with specific statistics in hitting, pitching, and fielding make for more successful major league players.
In some cases, a data-mining project is begun with a hypothetical result in mind. For example, a grocery chain may already have some idea that buying patterns change after it rains and want to get a deeper understanding of exactly what is happening. In other cases, there are no presuppositions and a data-mining program is run against large data sets in order to find patterns and associations.
Privacy Concerns The increasing power of data mining has caused concerns for many, especially in the area of privacy.
In fact, a whole industry has sprung up around this technology: These firms combine publicly accessible data with information obtained from the government and other sources to create vast warehouses of data about people and companies that they can then sell. This subject will be covered in much more detail in chapter 12 — the chapter on the ethical concerns of information systems. Business Intelligence and Business Analytics With tools such as data warehousing and data mining at their disposal, businesses are learning how to use information to their advantage.
The term business intelligence is used to describe the process that organizations use to take data they are collecting and analyze it in the hopes of obtaining a competitive advantage. Business analytics is the term used to describe the use of internal company data to improve business processes and practices. Knowledge Management We end the chapter with a discussion on the concept of knowledge management KM. All companies accumulate knowledge over the course of their existence. Some of this knowledge is written down or saved, but not in an organized fashion.
Much of this knowledge is not written down; instead, it is stored inside the heads of its employees. Summary In this chapter, we learned about the role that data and databases play in the context of information systems.
Data is made up of small facts and information without context. If you give data context, then you have information. Knowledge is gained when information is consumed and used for decision making.
Relational databases are the most widely used type of database, where data is structured into tables and all tables must be related to each other through unique identifiers. A database management system DBMS is a software application that is used to create and manage databases, and can take the form of a personal DBMS, used by one person, or an enterprise DBMS that can be used by multiple users. A data warehouse is a special form of database that takes data from other databases in an enterprise and organizes it for analysis.
Data mining is the process of looking for patterns and relationships in large data sets. Redundant data wastes disk space and creates maintenance problems. If data that exists in more than one place must be changed, the data must be changed in exactly the same way in all locations. A customer address change is much easier to implement if that data is stored only in the Customers table and nowhere else in the database.
What is an "inconsistent dependency"? While it is intuitive for a user to look in the Customers table for the address of a particular customer, it may not make sense to look there for the salary of the employee who calls on that customer.
The employee's salary is related to, or dependent on, the employee and thus should be moved to the Employees table.Office 2010 Class #43: Access One To Many Relationship Between Tables
Inconsistent dependencies can make data difficult to access because the path to find the data may be missing or broken. There are a few rules for database normalization. Each rule is called a "normal form. As with many formal rules and specifications, real world scenarios do not always allow for perfect compliance.
In general, normalization requires additional tables and some customers find this cumbersome. If you decide to violate one of the first three rules of normalization, make sure that your application anticipates any problems that could occur, such as redundant data and inconsistent dependencies. The following descriptions include examples.
First Normal Form Eliminate repeating groups in individual tables. Create a separate table for each set of related data. Identify each set of related data with a primary key.
Do not use multiple fields in a single table to store similar data. For example, to track an inventory item that may come from two possible sources, an inventory record may contain fields for Vendor Code 1 and Vendor Code 2. What happens when you add a third vendor? Adding a field is not the answer; it requires program and table modifications and does not smoothly accommodate a dynamic number of vendors.
Instead, place all vendor information in a separate table called Vendors, then link inventory to vendors with an item number key, or vendors to inventory with a vendor code key. Second Normal Form Create separate tables for sets of values that apply to multiple records.