The Power of the Database – Musato Technologies
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The Power of the Database: The backbone of IT

1. What is a database?

A database is a collection of data that is stored for a specific purpose and organized in a manner that allows its Databasecontents to be easily accessed, managed, and updated. Although this definition includes stored data collections such as libraries, file cabinets, and address books, when we talk about databases we almost invariably mean a collection of data that is stored on a computer. There are two basic categories of a database. The most commonly encountered category is the transactional database, used to store dynamic data, such as inventory contents, which is subject to change on an ongoing basis. The other category is the analytical database, used to store static data, such as geographical or chemical test results, which is rarely altered.

Strictly speaking, a database is just the stored data itself, although the term is often used, erroneously, to refer to a database and its management system (DBMS).

A brief history of the database:

The first attempts at computer databases arose around the mid-twentieth century. Early versions were file-oriented. A database file became known as a table because its structure was the same as a paper-based data table. For the same reason, the columns within a table were called fields and the rows were called records. Computers were evolving during that same time period, and their potential for data storage and retrieval was becoming recognized.

The earliest computer databases were based on a flat file model, in which records were stored in text format. In this model, no relationships are defined between records. Without defining such relationships, records can only be accessed sequentially. For example, if you wanted to find the record for the fiftieth customer, you would have to go through the first 49 customer records in sequence first. The flat file model works well for situations in which you want to process all the records, but not for situations in which you want to find specific records within the database.Database

The hierarchical model, widely used in mainframe environments, was designed to allow structured relationships that would facilitate data retrieval. Within an inverted tree structure, relationships in the hierarchical model are parent-child and one-to-many. Each parent table may be related to multiple child tables, but each child table can only be related to a single parent table. Because table structures are permanently and explicitly linked in this model, data retrieval was fast. However, the model’s rigid structure causes some problems. For example, you can’t add a child table that is not linked to a parent table: if the parent table was “Doctors” and the child table was “Patients,” you could not add a patient record independently. That would mean that if a new patient came into a community’s health care system, under this system, their record could not be added until they had a doctor. The hierarchical structure also means that if a record is deleted in a parent table, all the records linked to it in child tables will be deleted as well.

Also based on an inverted tree structure, the next approach to database design was the network model. The network model allowed more complex connections than the hierarchical model: several inverted trees might share branches, for example. The model connected tables in sets, in which a record in an owner table could link to multiple records in a member table. Like the hierarchical model, the network model enabled very fast data retrieval. However, it also had a number of problems. For example, a user would need a clear understanding of the database structure to be able to get information from the data. Furthermore, if a set structure was changed, any reference to it from an external program would have to be changed as well.

In the 1970s, the relational database was developed to deal with data in more complex ways. The relational model eventually dominated the industry and has continued to do so through to the present day. We’ll explore the relational database in some detail in the next segment.

2. What is a relational database?

In the relational database model, data is stored in relations, more commonly known as tables. Tables, records (sometimes known as tuples), and fields (sometimes known as attributes) are the basic components. Each individual piece of data, such as the last name or a telephone number, is stored in a table field and each record comprises a complete set of field data for a particular table. In the following example, the table maintains customer shipping address information. Last_Name and other column headings are the fields. A record, or row, in the table, comprises the complete set of field data in that context: all the address information that is required to ship an order to a specific customer. Each record can be identified by, and accessed through, a unique identifier called a primary key. In the Customer_Shipping table, for example, the Customer_ID field could serve as a primary key because each record has a unique value for that field’s data.


ID First Last Apt. Address City State Zip
101 John Smith 147 123 1st Street Chicago IL 60635
102 Jane Doe 13 C 234 2nd Street Chicago IL 60647
103 June Doe 14A 243 2nd Street Chicago IL 60647
104 George Smith N/A 345 3rd Street Chicago IL 60625

The term relational comes from set theory, rather than the concept that relationships between data drive the database. However, the model does, in fact, work through defining and exploiting the relationships between table data. Table relationships are defined as one-to-one (1:1), one- to-many (1: N), or (uncommonly) many-to-many (N: M):

If a pair of tables has a one-to-one relationship, each record in Table A relates to a single record in Table B. For example, in a table pairing consisting of a table of customer shipping addresses and a table of customer account balances, each single customer ID number would be related to a single identifier for that customer’s account balance record. The one-to-one relationship reflects the fact that each individual customer has a single account balance.
If a pair of tables has a one-to-many relationship, each individual record in Table A relates to one or more records in Table B. For example, in a table pairing consisting of a table of university courses (Table A) and a table of student contact information (Table B), each single course number would be related to multiple records of student contact information. The one-to-many relationship reflects the fact that each individual course has multiple students enrolled in it.
If a pair of tables has a many-to-many relationship, each individual record in Table A relates to one or more records in Table B, and each individual record in Table B relates to one or more records in Table A. For example, in a table pairing consisting of a table of employee information and a table of project information, each employee record could be related to multiple project records and each project record could be related to multiple employee records. The many-to-many relationship reflects the fact that each employee may be involved in multiple projects and that each project involves multiple employees.

3. Where did the relational model come from?

The relational database model developed from the proposals in “A Relational Model of Data for Large Shared Databanks,” a paper presented by Dr. E. F. Codd in 1970. Codd, a research scientist at IBM, was exploring better ways to manage large amounts of data than were currently available. The hierarchical and network models of the time tended to suffer from problems with data redundancy and poor data integrity. By applying relational calculus, algebra, and logic to data storage and retrieval, Codd enabled the development of a more complex and fully articulated model than had previously existed.

One of Codd’s goals was to create an English-like language that would allow non-technical users to interact with a database. Based on Codd’s article, IBM started their System R research group to develop a relational database system. The group developed SQL/DS, which eventually became DB2. The system’s language, SQL, became the industry’s de facto standard. In 1985, Dr. Codd published a list of twelve rules for an ideal relational database. Although the rules may never have been fully implemented, they have provided a guideline for database developers for the last several decades.

Codd’s Rules:

  • The Information Rule: Data must be presented to the user in table format.
    Guaranteed Access Rule: Data must be reliably accessible through a reference to the table name, primary key, and field name.
  • Systematic Treatment of Null Values: Fields that are not primary keys should be able to remain empty (contain a null value).
  • Dynamic On-Line Catalog Based on the Relational Model: The database structure should be accessible through the same tools that provide data access.
  • Comprehensive Data Sublanguage Rule: The database must support a language that can be used for all interactions (SQL was developed from Codd’s rules ).
  • View Updating Rule: Data should be available in different combinations (views) that can also be updated and deleted.
  • High-level Insert, Update and Delete: It should be possible to perform all these tasks on any set of data that can be retrieved.
  • Physical Data Independence: Changes made to the architecture underlying the database should not affect the user interface.
  • Logical Data Independence: If the logical structure of database changes, that should not be reflected in the way the user views it.
  • Integrity Independence: The language used to interact with the database should support user constraints that will maintain data integrity.
  • Distribution Independence: If the database is distributed (physically located on multiple computers) that fact should not be apparent to the user.
  • Nonsubversion Rule: It should not be possible to alter the database structure by any other means than the database language.

4. What are other types of the database there?

Although the relational model is by far the most prevalent one, there are a number of other models that are better suited to particular types of data. Alternatives to the relational model include:

  • Flat-File Databases: Data is stored in files consisting of one or more readable files, usually in text format.
  • Hierarchical Databases: Data is stored in tables with parent/child relationships with a strictly hierarchical structure.
  • Network Databases: Similar to the hierarchical model, but allows more flexibility; for example, a child table can be related to more than one parent table.
  • Object-Oriented Databases: The object-oriented database model was developed in the late 1980s and early 1990s to deal with types of data that the relational model was not well-suited for. Medical and multimedia data, for example, required a more flexible system for data representation and manipulation.
    Object-Relational Databases: A hybrid model, combining features of the relational and object-oriented models.

5. What languages are used to interact with databases?

SQL (Structured Query Language) is by far the most common language used to interact with relational databases. Originally developed for use with IBM’s DB2, the standard — often pronounced “sequel” — is promoted in various formats by both the American National Standards Institute (ANSI) and the International Standards Organization (ISO).

SQL commands are fairly straightforward and easy to understand. For example, if you wanted a list of all your customers within a specific zip code area, the following command (based on the table in the response to question #2, above) for example, will return that information, which in this case would be “George Smith.”

select First_Name, Last_Name   from Customer_Shipping   where Zip = '60625';

Most databases use SQL, although many use proprietary extensions specific to their own products.

6. How can I ensure a good database design?

Hands down, the most important thing you can do to ensure a successful database design is to put enough resources into the planning stage. The proliferation of off- the-shelf databases and database applications has led many people to a number of erroneous conclusions, such as:

  • Off-the-shelf databases can be easily customized.In fact, although there are ready-made databases available for any number of applications, their design typically differs significantly from the ideal model for your specific needs. And tailoring them to fit is often more complicated than starting from scratch.Anyone can create a perfectly functional database.In fact, almost anyone could create a perfectly functional database — if they took the time to learn what they needed to know before they started to develop.
  • You can jump right into the development process, making adjustments as you go along.In fact, you could build a database without a carefully constructed plan. You could also build a house in that manner — but it’s not advisable. Databases are complicated constructions. Whether or not major problems rear their ugly heads through the development phase, they are bound to pop up in implementation. Fixing those problems can be difficult, time-consuming, and expensive. Furthermore, because of the intricate ways that data is connected to a database, a problem in one area can affect data in other areas in surprising ways.
  • Databases came into being because of the computer, and the two have enjoyed a mutually beneficial symbiotic relationship ever since, each helping the other grow by leaps and bounds. Somewhat ironically, however, the best way to start a plan for database development is to take out paper, a pencil — and a big eraser.

    Database development is a three-phase process. In the first phase, you should create the logical design for the database, based solely on the data you want to store, rather than thinking of the specific software that will be used to create it, or the types of reports that will be created from it. In this phase, you define tables and fields and establish primary and foreign keys and integrity constraints. In the second phase, you implement your plan within the database software program, and in the third phase, you develop the end-user application that will allow your user(s) to interact with the database.

    7. What are the most important things to keep in mind during the design phase?

    Create your design on paper first, as completely as possible.

    1. Eliminate as much redundancy of data as possible.
    2. Start from scratch — don’t try to use parts of a database with structural problems.
    3. Make sure that each table represents a single subject.
    4. Assign a primary key whose value clearly identifies each record and only a single record.
    5. Ensure that each field represents a single value.
    6. Take the time to be certain of data integrity.

8. What is normalization and why do I need to know about it?

In short:

Well, normalized data makes programming (relatively) easy, and works very well in multi-platform, enterprise wide environments. Non-normalized data leads to heartbreak. — Steve Litt

Normalization is a guiding process for database table design that ensures, at four levels of stringency, increasing confidence that results of using the database are unambiguous and as intended. Basically a refinement process, normalization tests a table design for the way it stores data, so that it will not lead to the unintentional deletion of records, for example, and that it will reliably return the data requested.

Normalization degrees of relational database tables:

First normal form (1NF). This is the “basic” level of normalization and generally corresponds to the definition of any database, namely:

  • It contains two-dimensional tables with rows and columns corresponding, respectively, to records and fields.
  • Each field corresponds to the concept represented by the entire table: for example, each field in the Customer_Shipping table identifies some component of the customer’s shipping address.
  • No duplicate records are possible.
  • All field data must be of the same kind. For example, in the “Zip” field of the Customer_Shipping table, only five consecutive digits will be accepted.

Second normal form (2NF). In addition to 1NF rules, each field in a table that does not determine the contents of another field must itself be a function of the other fields in the table. For example, in a table with three fields for customer ID, the product sold, and price of the product when sold, the price would be a function of the customer ID (entitled to a discount) and the specific product.

Third normal form (3NF). In addition to 2NF rules, each field in a table must depend on the primary key. For example, using the customer table just cited, removing a record describing a customer purchase (because of a return perhaps) will also remove the fact that the product has a certain price. In the third normal form, these tables would be divided into two tables so that product pricing would be tracked separately. The customer information would depend on the primary key of that table, Customer_ID, and the pricing information would depend on the primary key of that table, which might be Invoice_Number.

Domain/key normal form (DKNF). In addition to 3NF rules, a key, which is a field used for sorting, uniquely identifies each record in a table. A domain is the set of permissible values for a field. By enforcing key and domain restrictions, the database is assured of being freed from modification anomalies. DKNF is the normalization level that most designers aim to achieve.

9. Database Words-to-Go Glossary:

Browse through database vocabulary in a handy printable glossary.

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Gideon E. M
Author: Gideon E. M

Gideon Ebonde M. is the CEO and Chief Software Architect at Musato Technologies. He is experienced Software developer with a demonstrated history of working in the information technology and services industry. He has a strong engineering professional skilled in Mobile Application Development, Enterprise Software, AI, Robotics, IoT, Servers, Cloud and business application. He is an accomplished DevOps software engineer and a visionary computer scientist and engineer.