Understanding When and Why to Normalize Your Data

What is Normalization?

Data normalization, or database normalization, is a foundational process in relational database design. It’s about structuring data logically to reduce redundancy, minimize anomalies, and enforce data integrity. While the core idea is simple—break down large, repetitive tables into smaller, more focused ones—the application of normalization is governed by formal rules known as normal forms. The design choices made here have lasting consequences for performance, scalability, and maintenance.

At its best, normalization gives us clean, consistent, and update-safe schemas. At its worst, it can complicate query logic, especially in analytical workloads. But before debating when to normalize or denormalize, we need to understand how normalization works—and why it matters.

Why Normalize?

Let’s anchor this with a practical example: imagine a music streaming service managing data for songs, albums, artists, playlists, and user preferences.

In an unnormalized schema, song details may be copied across multiple rows, user playlists may store artist and album data redundantly, and updating an artist’s name might require scanning thousands of rows. This design can quickly lead to:

• Update anomalies: Changing an artist’s name in one place but missing others leads to inconsistency.

• Insert anomalies: You can’t add a new artist unless they already have a song.

• Delete anomalies: Deleting a playlist might accidentally remove all reference to an artist or album if dependencies aren’t separated.

Normalization handles this by segmenting data into well-structured, interrelated tables. Each table represents a single subject (e.g., Artists, Albums, Users) and links to others via foreign keys—ensuring changes in one area don’t ripple destructively through the database.

The Stages of Normalization

First Normal Form (1NF): Eliminate Multi-Valued Fields

A table is in 1NF if:

• Each field contains only atomic (indivisible) values

• There are no repeating groups

• There is a clear primary key

Before 1NF

Here, the Subjects column violates atomicity—it stores multiple values in a single field.

After 1NF

This redesign ensures each column holds a single value, and each record represents one fact.

Second Normal Form (2NF): Remove Partial Dependencies

A table is in 2NF if:

• It is in 1NF

• Every non-key attribute is fully functionally dependent on the entire primary key (especially relevant for composite keys)

Before 2NF

• ProductName and UnitPrice depend only on ProductID

• CustomerName depends only on InvoiceID

This violates 2NF because some fields depend on part, not all, of the composite key.

After 2NF

Invoices

Products

Sales

Now each table has a single responsibility, and dependencies are cleanly isolated.

Third Normal Form (3NF): Remove Transitive Dependencies

A table is in 3NF if:

• It is in 2NF

• No non-prime attribute depends on another non-prime attribute (i.e., no transitive dependencies)

Before 3NF

Here, DepartmentName and DepartmentLocation depend on DepartmentID, which in turn depends on EmployeeID. That’s a transitive dependency.

After 3NF

Employees

Departments

Now the schema is free from transitive dependencies. Each attribute is directly related to its table’s key.

Boyce-Codd Normal Form (BCNF): Tighten the Rules

A table is in BCNF if:

• It is in 3NF

• Every determinant is a candidate key

This catches edge cases where 3NF is satisfied but anomalies still exist due to overlapping dependencies.

Example (Before BCNF)

Here, ProfessorName depends only on ProfessorID, which is part of the composite key—not valid in BCNF.

After BCNF

Professors

Courses

Now every determinant (e.g., ProfessorID) is a candidate key in its own table.

Normalization vs. Denormalization

Normalization and denormalization represent two opposing strategies in relational database design—each with distinct benefits, trade-offs, and performance implications. Choosing between them isn’t just a technical decision—it’s an architectural one, driven by query patterns, latency expectations, data freshness requirements, and the capabilities of your query engine.

Let’s break down the trade-offs:

When to Normalize: Best Practices

Use normalization when:

• Your system handles frequent inserts/updates/deletes (e.g., user-facing applications, financial systems, logistics).

• You need referential integrity (e.g., foreign keys that must point to valid entries).

• You want to support schema flexibility—new entities, attributes, or relationships without changing dozens of tables.

• Your team works with complex business logic where separating dimensions (e.g., Users, Products, Departments) is critical to avoiding anomalies.

Best Practice: Normalize by default unless there is a proven bottleneck in your query engine. This makes your schema easier to evolve, debug, and audit.

Best Practice: Normalize dimension tables (e.g., Customers, Products, Stores) even in reporting pipelines. These rarely change but support reuse and maintainability.

Example: In an ecommerce system, normalize orders, customers, products, and warehouses. This lets you independently update product pricing, customer addresses, or fulfillment status without risking conflicting data.

When to Denormalize: Best Practices

Use denormalization when:

• You are building read-optimized systems where query latency must be sub-second.

• Your analytics platform struggles with JOIN performance or high concurrency.

• You are designing dashboards, embedded analytics, or pre-aggregated cubes for consumption at scale.

• You want to reduce dependencies across services or teams—“just give me a flat table.”

Best Practice: Denormalize selectively. Identify performance-critical queries and denormalize only what's necessary.

Best Practice: Use materialized views or precomputed summaries instead of hardcoded denormalization—especially if your engine supports automatic view maintenance.

Best Practice: Keep source-of-truth tables normalized and create denormalized reporting layers downstream.

Example: For a marketing dashboard that breaks down ad impressions, clicks, and conversions by campaign, you may join AdEvents, Campaigns, and Users into a wide table or materialized view for real-time drilldowns. These tables can be rebuilt or refreshed without touching the source data model.

How StarRocks Changes the Equation

Historically, denormalization was a defensive strategy. Traditional query engines (like Hive, MySQL, or Redshift) struggled with JOIN performance, especially across large fact tables or highly concurrent workloads. Engineers would flatten data structures not because it was elegant, but because it was necessary.

Modern OLAP engines like StarRocks make that trade-off obsolete.

StarRocks capabilities that reduce the need for denormalization:

• Vectorized execution engine: Processes thousands of rows per CPU cycle, accelerating joins, filters, and aggregations.

• Cost-Based Optimizer (CBO): Automatically chooses efficient join orders and execution plans.

• Colocate and bucket shuffle join support: Makes distributed joins scalable and low-latency.

• Materialized views: Allow JOIN-heavy queries to be precomputed with automatic refresh, preserving normalization.

• Native support for normalized schemas: No need to pre-flatten data—StarRocks handles real-time JOINs even at high concurrency.

Case Study: Demandbase

Demandbase migrated from ClickHouse to StarRocks and moved away from an architecture that required 49 denormalized clusters. With StarRocks, they preserved their normalized schema and gained the ability to JOIN fact and dimension tables in real time, reducing query complexity and boosting dashboard responsiveness.

Case Study: Vivo

Vivo’s BI workloads suffered under Presto and ClickHouse due to slow joins and manual denormalization. By adopting StarRocks, they retained normalized ORC tables and used materialized views for acceleration—achieving 3–5× faster query speeds with 65% lower latency for ad-hoc analytics.

Normalization should be your default strategy—it enforces clean data modeling, simplifies updates, and supports long-term evolution. But performance realities matter, especially at scale.

The modern approach is not to pick one or the other, but to normalize your source-of-truth and optimize your query path. Use engines like StarRocks to reclaim performance without sacrificing design principles. And when you do need to denormalize, do it surgically—with materialized views, not ad hoc pipelines.

In today’s landscape, thanks to advanced OLAP engines, you don’t have to choose between “fast” and “clean.” You can—and should—have both.

Final Thoughts

Normalization is more than a theoretical construct—it’s the backbone of maintainable, scalable, and logically sound data systems. By enforcing rules that eliminate redundancy and clarify data dependencies, normalization reduces the likelihood of anomalies, makes updates safer, and helps developers manage growing schema complexity over time.

Yet the real-world practice of normalization always intersects with the realities of performance. The more normalized your schema becomes, the more your queries depend on JOIN performance, query planning, and indexing strategy. Historically, that’s where systems hit a wall—forcing teams to denormalize, flatten, or precompute data simply to meet SLA demands for speed.

This is where modern engines like StarRocks mark a turning point. With native support for normalized schemas, vectorized execution, intelligent JOIN planning, and automatically maintained materialized views, StarRocks lets teams preserve the correctness and flexibility of normalized data while still delivering sub-second performance—even at scale.

The takeaway is simple:
Normalize for correctness, denormalize for speed—but only when your engine demands it.
And with a high-performance OLAP engine like StarRocks, you might find that normalization and speed are no longer at odds.

Frequently Asked Questions (FAQ)

Q1. What is the main goal of normalization?

Normalization aims to reduce data redundancy and improve data integrity by organizing data into multiple related tables. Each table represents one subject or concept and is linked to others through keys. This makes data easier to update, maintain, and scale without inconsistency or duplication.

Q2. What problems does normalization solve?

Normalization helps prevent:

• Update anomalies: Inconsistent updates across redundant data.

• Insert anomalies: Inability to add data without unnecessary dependencies.

• Delete anomalies: Unintended data loss when deleting records.

It ensures each data point is stored only once and linked via foreign keys—improving logical coherence and reducing maintenance risk.

Q3. When should I avoid normalization?

Normalization introduces JOINs—which can be expensive if your engine struggles with them. You may want to avoid deep normalization if:

• You’re building a read-heavy analytics workload with strict latency goals

• You lack a performant engine or materialized view system

• You need to serve wide, flat tables to non-technical consumers (e.g., embedded dashboards)

In such cases, selective denormalization or pre-aggregation might make sense—especially for performance-critical queries.

Q4. Does StarRocks support normalized schemas?

Yes. StarRocks is designed to query normalized schemas efficiently. It supports:

• High-performance JOINs (including broadcast, colocate, and shuffle joins)

• Cost-Based Optimization (CBO) for choosing optimal join orders

• Materialized views that preserve logical structure while improving performance

• Real-time updates and federated access to Iceberg tables without flattening

This makes StarRocks well-suited for real-time analytics on normalized data.

Q5. What are best practices for schema normalization?

• Always normalize dimension tables (e.g., Products, Users, Locations) to support reuse and update safety.

• Normalize fact tables to the extent that performance allows; avoid unnecessary joins across massive transactional records unless your engine handles them well.

• Use surrogate keys for clarity when natural keys are composite or subject to change.

• Normalize first, then denormalize selectively based on query patterns and engine behavior.

Q6. What’s the difference between 3NF and BCNF?

• 3NF removes transitive dependencies: non-key attributes cannot depend on other non-key attributes.

• BCNF tightens the rule: every determinant must be a candidate key.

While all BCNF tables are in 3NF, not all 3NF tables satisfy BCNF. The distinction matters most when your schema involves overlapping or alternate candidate keys.

Q7. How does normalization impact performance?

Normalization increases the number of tables and JOINs in queries. On traditional engines, this can slow things down. On modern MPP and vectorized systems like StarRocks, JOIN performance is optimized—making normalization viable without sacrificing speed.

To improve performance:

• Use materialized views for frequently joined data

• Ensure appropriate indexes and distribution strategies (e.g., colocated joins)

• Monitor query plans using EXPLAIN to detect inefficient paths

Q8. Can I normalize data stored in a data lake?

Yes. Modern table formats like Apache Iceberg and Delta Lake support normalized schemas across partitioned tables. Engines like StarRocks or Trino can query these schemas efficiently with proper metadata caching and vectorized execution.

For example, TRM Labs normalized blockchain event data across Iceberg tables (e.g., chains, addresses, events) and queried them in real time using StarRocks—achieving high performance without denormalization.

Q9. Is denormalization a bad practice?

Not inherently. Denormalization is a tool, not a mistake—but it comes with trade-offs:

• Redundant data = more storage + risk of inconsistency

• Harder to update or backfill logic

• Hard-coded business logic baked into table structures

It’s best used strategically in reporting layers, not in core data models.

Q10. What’s the best way to balance normalization and performance?

• Keep your source-of-truth normalized

• Use materialized views or aggregation tables to optimize high-frequency queries

• Choose an engine (like StarRocks) that supports real-time JOINs and vectorized processing

• Monitor query patterns and apply denormalization only where performance demands it

This layered approach gives you correctness, flexibility, and speed—without locking yourself into rigid schema designs or unnecessary duplication.