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Database Normalization for Faster Data Science

Today, data is being generated in exceptionally large amounts in a variety of formats at almost incomprehensible speeds. According to Datacenter News, the world generated 64.2 zettabytes of data last year, and much of it was streaming data. Now the majority of data is unstructured or semi-structured, making it difficult to visualize and analyze. Data normalization “cleans up” data so organizations can make use of it. In this article, we look at data normalization in the context of databases and how your company can benefit from it.

What Is Data Normalization?

Data normalization applies a set of formal rules to develop standardized, organized data, and eliminates data anomalies that cause difficulty for analysis. The clean data can then be easily grouped, understood, and interpreted. Without normalization, valuable data will go unused. 

Depending on your use case, data normalization may happen prior to or after loading the data into your data warehouse or platform. Some platforms, such as Snowflake, allow complete flexibility so you can store massive amounts of raw data and normalize only the data you need as you need it.

Note that data normalization is also done in the context of changing the distribution shape of data in order to build machine learning models. When used in this way, the purpose of normalization is to change the values of numeric columns in the data set so a common scale is being used without distorting differences in the ranges of values or losing information. In this context, data normalization is not required unless features have different ranges.

Benefits of Data Normalization

Before we dive into the specifics of the rules involved in data normalization, let’s look at the benefits you can expect from the normalization process. 

  • Eliminate duplicates for efficiency: Much of the work that normalization accomplishes is related to eliminating duplicates, which frees space and allows your systems to run more efficiently.

  • Ensure data is logically organized: Normalization applies a set of rules to associate attributes with the data, allowing it to be organized by attribute. Because of this, you can organize data more effectively.

  • Increase accuracy: One of the goals of normalization is standardization, which simultaneously ensures that the data is accurate. 

  • Query data from multiple sources easily: When data is standardized, you can combine data sets and run queries on the larger set. Many analyses require looking at multiple data sets from multiple sources.

  • More easily update data: When data has been normalized, you can more easily update it because you don’t have to be concerned about updating duplicates.

  • Facilitate better data governance: Better governance is one of the most valuable benefits of data normalization. The normalization process is an essential part of creating high-quality data that’s been vetted and is ready for use. And because normalization also allows you to effectively organize data, you can track it more easily.

Data Normalization Rules

Data normalization rules are sequential—as you move through each rule, you normalize the data further. For this reason, you can think of normalization rules as “levels” of normalization. Although there are five rules in total, only three are commonly used for practical reasons, since too much normalization results in inflexibility in the data model. 

1NF (First Normal Form) rule

The first rule is about ensuring there are no repeating entries in a group. All attributes must have a unique name, and entities may consist of only two dimensions. (You’ll need to create a new entity for additional dimensions.) Each entry must have only a single value for each cell, and each record must be unique. The goal of this first rule is to make it easier to search the data.

2NF (Second Normal Form) rule

The second rule is designed to eliminate redundant data. Before applying the 2NF rule, you must be sure that the 1NF rule has been fully applied. Data that is in the 2NF state will have only one primary key. For this reason, you must separate data by placing all subsets of data that can be placed in multiple rows into separate tables. Relationships can then be created via foreign key labels.

3NF (Third Normal Form) rule

The third rule eliminates transitive dependency. As before, data must have achieved 1NF and 2NF status before you can apply the 3NF rule. Transitive dependency is when a nonprime attribute depends on other nonprime attributes instead of depending on the prime attributes or primary key. So the third rule ensures that no attribute within an entity is dependent on a nonprime attribute that depends on the primary key. For this reason, if the primary key is changed, all data impacted by the change has to be put into a new table with a new foreign key.

Data Warehouse Normalization with Snowflake

Snowflake was built for data science. The Snowflake Data Cloud supports virtually every data model and normalization, enabling you to collect and process internal and third-party data with ease. Using Snowflake, you can efficiently realize the value of your models with a unified platform that enables cross-functional teams to build scalable data preparation and model inference pipelines to transform data into actionable business insights.

Spin up a Snowflake free trial to:

  • Explore the Snowflake UI and sample data sets

  • Process semi-structured data with full JSON support

  • Instantly scale compute resources up and down to handle unexpected workloads and unique concurrency needs

  • Set up and test-run data pipelines and connect to leading BI tools

  • Experiment with programmatic access

To see Snowflake’s potential for improving your data ingestion, sign up for a free trial