Having an adequate data model to serve specific business needs of an organization is important. Evaluate the need for denormalization within an organization. Provide at least three

Arianna Contardo

RE: Week 7 Discussion

Top of Form

 

Denormalization is the process of adding data that is precomputed and redundant to an otherwise normalized relational database in order to improve read performance of the database. When it comes to normalizing a database, this involves removing redundancy so that only a single copy exists of every piece of information. In order for a database to be denormalized, it has to be normalized first.

(1)

This is a technique that is used for the purpose of improving data access performances. When a database has been normalized, and methods like indexing are not actually enough, this is when denormalization serves as a final option to speed up the retrieval of data. In this process, data is systematically combined in order to quickly get information. In this process, relations are brought down to lower than normal forms, thus, reducing the overall integrity of data. However, it increases the performance of data retrieval. Rather than performing multiple costly JOINS on many tables, normalization helps to bring together information that is logically or commonly combined. Due to lower forms, database anomalies appear. The redundancy issue finds a solution in adding software level limitations when it comes to putting data into a database.

(2)

To improve website performance, database optimization is essential. Typically, in order to do this, developers normalize a database-in other words, they restructure it in a way to enhance data integrity and reduce data redundancy. Although, sometimes normalization is not in fact enough. Therefore, in order to improve database performance even further, developers will use denormalization.

Significantly speeding up data retrieval is the main purpose of denormalization. However, it should be used for specific purposes:

 

-In order to enhance query performance

A normalized database typically requires the joining of many tables to fetch queries. Although, the more this happens, the slower the query. Therefore, as a countermeasure, by copying values between child and parent tables, you can add redundancy, thus, helping to reduce the amount of joins required for a query. 

 

-Making a database more convenient to manage

There are no calculated values that are essential for applications in a normalized database. To calculate these values on the spot would require time, thus, slowing down query execution. To provide calculated values, you can denormalize a database. Once they are generated and added to tables, downstream programmers are easily able to create their own queries and reports without having any knowledge that is in depth of the apps code or API.

 

-Accelerating and facilitating reporting

Oftentimes, applications need to provide a lot of statistical and analytical information. Generating reports from live data is not only time consuming, but can also negatively impact the overall performance of a system. To meet this challenge, you can denormalize your database. If you needed to provide a sales summary total for one or more customers, a normalized database would aggregate and calculate all invoice details many times. This is a pretty lengthy process, so in order to help speed it up, you could maintain the year-to-date sales summary in a table storing the user details.

 

These features make it quite useful and appealing for data consumers.

(3)

 

Overall, the advantages of denormalization are:

Fewer errors- By working with a smaller number of tables, there are fewer bugs when it comes to retrieving information from a database.

Speed- Retrieving data is faster because JOIN queries are costly on a normalized database.

-Simplicity- Having a smaller number of tables makes retrieving data more straightforward

(1)

 

 

 

 

References:

1. Wright, G., & Vaughan, J. (2022). denormalization. Retrieved November 17, 2022, from https://www.techtarget.com/searchdatamanagement/definition/denormalization  

2. Dancuk, M. (2021, June 3). What Is Database Denormalization? Retrieved November 17, 2022, from https://phoenixnap.com/kb/database-denormalization

3. B., G., & B., A. (2020, January 13). When and How You Should Denormalize a Relational Database. Retrieved November 17, 2022, from https://rubygarage.org/blog/database-denormalization-with-examples

Bottom of Form

USEFUL NOTES FOR:

Evaluate the need for denormalization within an organization.

Introduction

A data warehouse is a database that stores and organizes large amounts of data for analysis. Data warehouses are often used by businesses to extract valuable information from the mass amounts of data that they collect, but it’s important to ensure that your data warehouse is properly denormalized before you start using it. This article will explain how denormalization works within an organization and why it’s important to do so when designing your own data warehouse.

Evaluate the need for denormalization within an organization.

Denormalization is a concept that’s used to explain how data can be stored in a way that makes it easier for the computer to process. When there isn’t enough information available in your database, you need denormalization. In order to do this, you’ll need to use some sort of data compression or summarization before storing any more data into your database.

Denormalization doesn’t mean that the information isn’t important; rather it means that there isn’t enough room in the current table structure for all of your customers’ information because each customer has multiple products purchased by them over time (for example). This can lead us down several paths: * Adding columns/fields as needed until we have enough space left over * Changing our schema so that we don’t have any more columns than necessary (this would require an upgrade) Or even just making sure we keep track of what type each column contains so if one needs expanding later on down line it won’t cause problems

Ensure that data warehouses are correctly denormalized based on the needs of the organization.

Denormalization is the process of storing redundant data in a database. This can be used to improve query performance and reduce redundancy in data warehouses. Denormalization can also be used to improve the performance of an application by reducing the number of joins that need to be performed when accessing data from multiple tables or views.

Choose appropriate designs to store data in a data warehouse.

There are several types of designs for storing data in a data warehouse. The first type is a relational database design, which is used when you want to store the data in tables that have relationships between each other. For example, if you have an employee table and a job table, then your employee can be related to their job by being assigned to that job and receiving an ID which references both the employee and their assigned position. This allows you to retrieve information about both employees who work at different companies (e.g., McDonalds) as well as those who work at multiple jobs within one company (e.g., shift supervisor). In addition, it also allows them access this information without having any additional knowledge about how these relationships were created or maintained over time–so long as they’re consistent enough across all relevant fields within each respective database instance (e..g., name), then everything should work out fine!

Data warehouse design issues include denormalization, star schema and snowflake schema designs, and dimensional tables.

Denormalization is a technique that is used to simplify the data storage structure in a data warehouse. This can be done by creating intermediate tables between the source and final destination tables. The end result is that you have less information in your final destination table and more information in intermediary tables, which makes it easier for users to query against those intermediate tables and retrieve specific values rather than having to scan through all rows of one large table at once.

Star schema design: Star schemas are considered to be an acceptable alternative solution when compared with snowflake schemas because they don’t require any additional hardware resources or software licenses during implementation; however, they do require more maintenance work than snowflake designs due to their complexity (see Figure 1).

Denormalize when needed.

Denormalization is a technique to reduce redundant data. It can be used to improve performance and reduce storage requirements, but only when necessary.

Denormalization does not always have to be done; however, it can help if you need to access your data frequently or if the amount of data being stored exceeds what your database server can handle on its own.

Conclusion

Denormalization is a key design decision that will affect the performance of your data warehouse. If you have any questions about denormalization, or need help with any other aspect of designing for Hadoop and Big Data, please get in touch with us. We would love to hear from you!