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Why You Need Database Normalization
Database normalization, or data normalization, is a technique to organize the contents of the tables for transactional databases and data warehouses. Normalization is part of successful database design without normalization, database systems can be inaccurate, slow, and inefficient, and they might not produce the data you expect.
Date : 24/11/2016
Author Information
Uploaded by : Ramesh
Uploaded on : 24/11/2016
Subject : Database
When you normalize a database, you have four goals: arranging data into logical groupings such that each group describes a small part of the whole minimizing the amount of duplicate data stored in a database organizing the data such that, when you modify it, you make the change in only one place and building a database in which you can access and manipulate the data quickly and efficiently without compromising the integrity of the data in storage.
Data normalization helps you design new databases to meet these goals or to test databases to see whether they meet the goals. Sometimes database designers refer to these goals in terms such as data integrity, referential integrity, or keyed data access. Ideally, you normalize data before you create database tables. However, you can also use these techniques to test an existing database.
Data normalization is primarily important in the transactional, or online transactional processing (OLTP), database world, in which data modifications (e.g., inserts, updates, deletes) occur rapidly and randomly throughout the stored data. In contrast, a data warehouse contains a large amount of denormalized and summarized data precalculated to avoid the performance penalty of ad hoc joins. In a data warehouse, updates happen periodically under extremely controlled circumstances. End users` updates to data in data warehouses are uncommon. This article addresses the normalization of OLTP data.
Data and Information
Data and information are terms people use interchangeably in everyday speech, but they mean different things. Data are raw facts, such as a name, a number, or a date. Information is organized, processed data. A data item (e.g., the date 7/15/99) means little. When you associate the data with other data items, such as a deadline and a subject, you can create information. For example, the deadline for your next project might be 7/15/99. You store data in a database you retrieve information from the database.
One cornerstone of database design and data normalization is that data organization for storage differs from the information most people want to see. For example, a manager of a sporting goods supply company might want to see, for one sale, who the customer was, the destination of the order, the billing address, the contact phone number, the placement time of the order, the order`s shipping destination, when and how delivery occurred, what articles the order included, and which of the company`s employees was the key person in the sale. The organization of data in the company`s database differs from the particular information the manager wants.
Data normalization helps you design new databases to meet these goals or to test databases to see whether they meet the goals. Sometimes database designers refer to these goals in terms such as data integrity, referential integrity, or keyed data access. Ideally, you normalize data before you create database tables. However, you can also use these techniques to test an existing database.
Data normalization is primarily important in the transactional, or online transactional processing (OLTP), database world, in which data modifications (e.g., inserts, updates, deletes) occur rapidly and randomly throughout the stored data. In contrast, a data warehouse contains a large amount of denormalized and summarized data precalculated to avoid the performance penalty of ad hoc joins. In a data warehouse, updates happen periodically under extremely controlled circumstances. End users` updates to data in data warehouses are uncommon. This article addresses the normalization of OLTP data.
Data and Information
Data and information are terms people use interchangeably in everyday speech, but they mean different things. Data are raw facts, such as a name, a number, or a date. Information is organized, processed data. A data item (e.g., the date 7/15/99) means little. When you associate the data with other data items, such as a deadline and a subject, you can create information. For example, the deadline for your next project might be 7/15/99. You store data in a database you retrieve information from the database.
One cornerstone of database design and data normalization is that data organization for storage differs from the information most people want to see. For example, a manager of a sporting goods supply company might want to see, for one sale, who the customer was, the destination of the order, the billing address, the contact phone number, the placement time of the order, the order`s shipping destination, when and how delivery occurred, what articles the order included, and which of the company`s employees was the key person in the sale. The organization of data in the company`s database differs from the particular information the manager wants.
This resource was uploaded by: Ramesh