How to normalize data

If you’re aiming for peak database performance and clarity within New Relic's observability tools, data normalization is key. This guide will break down the essentials, from the core concepts to practical implementation. We’ll help you build data structures that are not only robust but also perfectly aligned with New Relic’s monitoring capabilities.

What is data normalization?

Data normalization streamlines your database, making its information consistent and reliable – crucial for effective monitoring and insights within New Relic. It's about transforming data into a clean, orderly system, reducing redundancy and making sure your updates are smooth and error-free. This isn't just good practice; it's foundational for leveraging New Relic's full potential in tracking and analyzing your application's performance.

Why is data normalization important?

As a foundation for database health and efficiency, data normalization provides several benefits for organizations that rely on data-driven decisions. When properly implemented, it significantly reduces redundant data, which not only saves storage space but also eliminates the risk of inconsistencies that occur when the same information exists in multiple places but isn’t updated uniformly.

The importance of data normalization becomes especially apparent as your data grows: a well-normalized database can more easily adapt to evolving business needs without requiring extensive redesigns. But normalization improves data integrity at all stages of growth, ensuring that relationships between tables are logical and well-defined, and preventing anomalies that could corrupt your data during insertion, update, or deletion operations. 

Different normal forms in database normalization

Database normalization follows the progressive application of normal forms, each building upon the last to eliminate different types of data anomalies. These forms provide a structured approach to organizing data, from the most basic requirements to more advanced refinements. Understanding the different types of data normalization forms will help you design efficient and reliable database structures.

1NF

First Normal Form (1NF) is the most fundamental level of database normalization, focusing on eliminating repeating groups and ensuring atomic values. A table is in 1NF when each cell contains a single value, and each record is unique.

For example, consider a Contacts table where the PhoneNumbers field contains "555-1234, 555-5678, 555-9012" as a comma-separated list. This violates 1NF because the field isn't atomic—it contains multiple values. When you need to search for a specific phone number or count how many numbers a contact has, you'll face significant query challenges. Instead, ensure each field contains exactly one value, with no lists, arrays, or delimited strings hiding multiple values in a single field.

2NF

Second Normal Form (2NF) reflects the fact that your data should only depend on the primary key, not portions of it. To achieve 2NF, you may need to build separate tables when information applies across multiple data records, and connect these tables using foreign key relationships.

Consider an example of a sales database: a customer’s shipping details may appear in Orders, Shipments, and Invoices tables, but they must remain consistent everywhere. Storing this information in each table is redundant in the best-case scenario, and will lead to inconsistencies in the worst case. Instead, you should maintain a single source of truth for customer information in one location and reference it through relationship keys where it’s needed.

3NF

Third Normal Form (3NF) further refines database structure by eliminating transitive dependencies. A table achieves 3NF when it’s already in 2NF and no non-key column depends on any other non-key column.

For example, if a Customer table contains both ZIP code and City columns, where City depends on ZIP code (rather than directly on the primary key), this creates a transitive dependency. To comply with 3NF, you would need to move the ZIP code and City information to a separate table, maintaining only the essential relationship to the primary key in the original table.

Note that while 3NF is a general best practice, there are practical considerations that may warrant exceptions. Following 3NF too strictly can lead to the creation of numerous tiny tables, which, while theoretically correct, could negatively impact query performance and system complexity. Database designs must take the specific use case into consideration when deciding how to balance data integrity with performance needs, which can mean allowing for some denormalization.

Beyond 3NF: BCNF, 4NF, and 5NF

While the first three normal forms effectively address most database design considerations, there are other, more advanced forms for specific situations. These forms are less commonly implemented in everyday database design but can be valuable in complex systems where data integrity is of the utmost importance. For most applications, achieving 3NF is considered sufficient for balancing data integrity with practical performance needs.

Boyce-Codd Normal Form (BCNF) is sometimes called 3.5NF because it’s a slightly stronger version of 3NF that addresses anomalies not handled by 3NF when multiple overlapping candidate keys exist. Fourth Normal Form (4NF) deals with multi-valued dependencies, which occur when two attributes in a table are independent of each other but both dependent on a third attribute. Fifth Normal Form (5NF) handles join dependencies that can’t be decomposed into simpler dependencies.

How to normalize data

Normalizing a database is a systematic process that transforms an unnormalized model into a well-structured, efficient design. Here’s a step-by-step approach for a greenfield database project:

1. Identify the entities in your data model: Determine the distinct objects or concepts your database will track. Common examples include customers, products, orders, etc.
2. Create tables and primary keys: For each entity, create a separate table with a primary key that uniquely identifies each row. For example, CustomerID for customers or OrderID for orders.
3. Apply 1NF: Remove repeating groups by placing them in separate tables. For instance, if a customer can have multiple phone numbers, create a separate PhoneNumbers table rather than creating Phone1, Phone2, Phone3 fields.
4. Apply 2NF: Ensure non-key attributes depend on the entire primary key. For example, in an OrderDetails table with OrderID and ProductID as the composite key, move ProductName to the Products table since it depends only on ProductID.
5. Apply 3NF: Remove attributes that depend on other non-key attributes. If CustomerZipCode determines CustomerCity in your Customers table, move both to a separate ZipCodes table to eliminate this transitive dependency.
6. Review and refine: Review your tables for any remaining anomalies, test with sample data, and adjust as needed based on your specific business requirements. Consider normal forms beyond 3NF if your use case warrants them.

For example, imagine a (poorly designed) Sales table containing columns for SaleID, CustomerName, CustomerAddress, ProductID, ProductName, CategoryName, and Price. To normalize this, you would first create separate tables for Customers, Products, and Sales (1NF). Then you would move product information to its own table (2NF). Finally you’d create a Categories table for category information that depends on the category ID, not the product directly (3NF).

While normalization improves data integrity, consider where strategic denormalization might benefit performance. For example, a report-oriented database might keep some calculated fields or summary data to reduce complex joins during queries.