Online database - Wikipedia
Database Terminology is explained in this comprehensive list of definitions. The Internet of Things (IoT) is frequently considered a vast grid of data Foreign and primary keys explicitly define the direct relationships between tables. In this lesson, we'll be looking at databases, which are computer mechanisms Simply put, dayline.info is rich in content and convenient to use. Entities and attributes. One way to approach database design is to think in terms of entities, their attributes, and the relationships between them. An entity.
This is accomplished by adding a record with the student ID and the club ID in the Memberships table. The design of the Student Clubs database also makes it simple to change the design without major modifications to the existing structure.
For example, if the design team were asked to add functionality to the system to track faculty advisors to the clubs, we could easily accomplish this by adding a Faculty Advisors table similar to the Students table and then adding a new field to the Clubs table to hold the Faculty Advisor ID.
Data Types When defining the fields in a database table, we must give each field a data type. For example, the field Birth Year is a year, so it will be a number, while First Name will be text. Most modern databases allow for several different data types to be stored. Some of the more common data types are listed here: The database designer can identify the maximum length of the text. There are usually a few different number types that can be selected, depending on how large the largest number will be.
First, a data type tells the database what functions can be performed with the data. For example, if we wish to perform mathematical functions with one of the fields, we must be sure to tell the database that the field is a number data type. So if we have, say, a field storing birth year, we can subtract the number stored in that field from the current year to get age. The second important reason to define data type is so that the proper amount of storage space is allocated for our data.
For example, if the First Name field is defined as a text 50 data type, this means fifty characters are allocated for each first name we want to store.
However, even if the first name is only five characters long, fifty characters bytes will be allocated. It may be prudent to reduce the size of the field so we do not waste storage space. 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. 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.Connect To SQL Server Database Over Network - Enable Network Access in SQL Server
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. 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.
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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.
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