TL;DR: Normalization vs. Denormalization
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Normalization is a technique in relational database design that reduces data redundancy and enforces integrity. It does this by splitting complex tables into smaller, logically structured ones—following rules called normal forms (1NF through 5NF, including BCNF).
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Denormalization intentionally reintroduces redundancy to improve query performance and simplify access, often at the cost of consistency and flexibility.
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Normalization is ideal for OLTP systems where write performance and accuracy matter.
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Denormalization is useful for OLAP or real-time dashboards where speed and simplicity outweigh the overhead of joins.
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Historically, denormalization was a necessary evil in real-time analytics due to the poor performance of join operations at scale.
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With StarRocks, that trade-off is no longer mandatory. It supports normalized schemas at scale thanks to fast distributed joins, runtime filters, and native JSON handling—illustrated in the Demandbase case study.
Understanding Normalization
What is Database Normalization?
If you’re just starting to work with relational databases, one of the most important design principles you’ll encounter is normalization. Think of it as the process of cleaning up your data model to eliminate repetition, enforce structure, and prevent common errors.
In simple terms, normalization is a method of organizing data in SQL databases to reduce redundancy and avoid anomalies during inserts, updates, and deletions. It does this by splitting large, complex tables into smaller, more focused ones, while preserving the relationships between them.
Here’s a formal definition often used in database design:
“Database normalization is the process of structuring a relational database in accordance with a series of so-called normal forms in order to reduce data redundancy and improve data integrity.”
This process isn’t ad hoc. It follows a well-established set of rules known as the normal forms—each one addressing a specific class of problems.
Why Normalize?
Let’s ground this in a few practical motivations:
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Prevent Data Inconsistency: Without normalization, a single piece of information (like a customer’s phone number) might be stored in multiple places. If it changes, you now have to update every occurrence.
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Avoid Data Anomalies: Poorly structured tables can make inserting, updating, or deleting data error-prone.
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Improve Storage and Query Efficiency: Repeated data wastes space and can slow down queries over time.
Common Signs You Need Normalization:
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You find the same value repeated across many rows.
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You’re writing update scripts to sync duplicate fields.
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Deleting one row causes loss of unrelated data.
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Your data model is hard to evolve or scale.
What Triggers the Need for Normalization?
Normalization typically becomes essential when you start seeing signs of data anomalies:
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Insert anomalies: You can't add a new record without adding unrelated data (e.g., can’t add a new borrower without also adding a book).
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Update anomalies: A change in one place requires manual updates across multiple rows.
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Delete anomalies: Deleting one record accidentally removes other useful information.
Normalization prevents these by organizing the schema so each table represents one thing, and one thing only.
The Normal Forms Explained
Each stage of normalization is guided by a rule called a normal form. These forms—1NF through 5NF, with Boyce-Codd Normal Form (BCNF) as a stricter enhancement of 3NF—represent progressively stronger levels of schema organization.
While normalization theory goes all the way to 5NF (and beyond in academia), most real-world systems apply normalization up to Third Normal Form (3NF) or BCNF. These levels strike a practical balance between minimizing redundancy, preserving data integrity, and keeping queries manageable.
Higher forms like 4NF and 5NF are typically used only in complex, highly interrelated data models.
Source
Overview: Normal Forms
Normalization progresses through a series of normal forms (NF)—rules that guide you in building increasingly robust schemas. Here’s a quick comparison:
| Normal Form |
Description |
Key Requirement |
Redundancy Reduction |
Used in Practice? |
| 1NF |
Atomic values |
No multi-valued or nested fields |
Low |
Always (baseline requirement) |
| 2NF |
Remove partial dependencies |
Be in 1NF; all non-key attributes depend on the whole primary key |
Moderate |
Yes (when composite keys exist) |
| 3NF |
Remove transitive dependencies |
Be in 2NF; non-key attributes must not depend on other non-key columns |
High |
Most common in OLTP systems |
| BCNF |
All determinants are candidate keys |
Every determinant (left-hand side of a dependency) must be a candidate key |
High |
Yes, especially with complex keys |
| 4NF |
Remove multi-valued dependencies |
Be in BCNF; eliminate independent multi-valued facts |
Very High |
Rare |
| 5NF |
Remove join dependencies |
Be in 4NF; decompose tables to avoid redundancy from complex joins |
Maximal |
Very rare (advanced modeling) |
Normal Forms with Simple Examples
1NF — First Normal Form
Rule: Every column must contain only atomic values (indivisible), and no repeating groups.
Example:
Bad: favorite_colors = "red, blue"
Good: Split into multiple rows or a separate table
2NF — Second Normal Form
Rule: Applies to tables with composite primary keys. All non-key attributes must depend on the entire key, not just part of it.
Example: In a table with (student_id, course_id) as the key, storing student_name violates 2NF (it depends only on student_id).
Fix: Move student_name to a separate students table.
3NF — Third Normal Form
Rule: Remove transitive dependencies.
Example:
Bad: employee_id → department_id → department_name
Fix: Split into two tables:
Boyce-Codd Normal Form (BCNF)
Rule: Every determinant must be a candidate key.
Let’s slow down here. BCNF builds on 3NF, but fixes edge cases involving multiple candidate keys.
Scenario: Consider a table of room bookings:
| Room |
Time |
Instructor |
| A101 |
9 AM |
Smith |
| A101 |
10 AM |
Johnson |
Let’s say:
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Each instructor teaches in one room at a fixed time.
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Each (Room, Time) uniquely determines the instructor.
However, it’s possible that Instructor also uniquely determines Room (they always teach in the same one). Now, Instructor → Room is a functional dependency—but Instructor is not the primary key. That violates BCNF.
Fix: Decompose into:
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Instructors table with Instructor → Room
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Room_Schedule table with Room, Time → Instructor
This ensures that all determinants are candidate keys, preserving data integrity.
4NF — Fourth Normal Form
Rule: Remove multi-valued dependencies.
Example: If a teacher teaches multiple subjects and works at multiple locations, and they are unrelated, storing both in one table creates redundant combinations.
Fix: Split into:
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teacher_subjects
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teacher_locations
Each row captures a single relationship.
5NF — Fifth Normal Form
Rule: Remove join dependencies. All data should be reconstructible from smaller, non-redundant tables without introducing anomalies.
Example: A product may have:
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multiple vendors
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multiple colors
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multiple sizes
A single table with all combinations explodes in size.
Fix: Create separate tables for each dimension and join only when needed.
Why Stop at 3NF (or BCNF)?
Most production databases stop at 3NF or BCNF because these levels:
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Offer strong protection against data anomalies
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Keep the number of tables manageable
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Make query logic easier to understand and maintain
Going beyond BCNF into 4NF or 5NF introduces more joins, more complexity, and usually marginal benefit—unless you're dealing with unusually intricate data models.
That said, BCNF is often worth applying, especially when dealing with composite keys or functional dependencies beyond the scope of 3NF.
Exploring Denormalization
What is Denormalization?
Once you've learned about normalization, denormalization may seem like a step backward. In a way, it is—but it’s often a deliberate, practical trade-off.
Denormalization is the process of reintroducing controlled redundancy into a database schema to improve performance. Instead of distributing related data across many small, normalized tables, denormalization combines some of that data into fewer, wider tables. This can significantly reduce the need for complex joins, especially in read-heavy environments like dashboards or reporting systems.
Denormalization doesn't ignore structure—it restructures with performance in mind.
Where normalization prioritizes data integrity, denormalization prioritizes query speed and simplicity.
Why Denormalize?
There are several reasons teams intentionally denormalize parts of their schema:
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Faster reads: Joins are expensive—especially across large tables. Flattening data can drastically improve query performance.
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Simpler SQL: Fewer joins means simpler query logic. This is ideal for dashboards or ad hoc analysis.
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Real-time requirements: When response times must be sub-second, avoiding joins becomes crucial.
When Normalization Slows You Down
Fully normalized schemas can create performance bottlenecks when:
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Answering simple questions requires joining many tables.
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Large fact tables are joined repeatedly with the same dimensions.
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Real-time dashboards need low-latency, high-concurrency access.
Denormalization helps by reducing the operational cost of joins.
How Denormalization Works
Common denormalization techniques include:
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Flattening tables: Merging fact and dimension tables into one.
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Duplicating data: Copying attributes (like names or prices) across tables to eliminate joins.
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Precomputing aggregates: Storing totals, averages, or flags in advance to avoid expensive groupings at query time.
Example
In a normalized schema:
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orders: order_id, customer_id, product_id, quantity
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products: product_id, product_name, price
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customers: customer_id, name, email
To show a customer’s order history, you’d need to join three tables.
A denormalized version:
| order_id |
customer_name |
email |
product_name |
price |
quantity |
| 123 |
Alice Smith |
alice@x.com |
Widget A |
12.99 |
2 |
This avoids any joins—everything is already in one row.
Comparison of Normalization and Denormalization
Normalization and denormalization are not opposing design philosophies—they are two sides of the same database engineering coin. Now that you understand what normalization and denormalization are, let’s look at how they differ in practice—and how to choose between them depending on your system’s priorities.
At their core, normalization and denormalization represent a trade-off between data integrity and query performance. One favors structure, clarity, and consistency; the other favors speed and ease of access. Your use case—OLTP or OLAP, write-heavy or read-heavy, real-time or batch—should inform which strategy to lean on.
Normalization vs. Denormalization: Side-by-Side
| Dimension |
Normalization |
Denormalization |
| Primary goal |
Reduce redundancy, enforce integrity |
Improve read performance, reduce JOIN overhead |
| Structure |
Many narrow, relational tables |
Fewer wide, flat tables with redundancy |
| Data integrity |
Strong—single source of truth |
Weaker—data duplication can introduce inconsistencies |
| Write performance |
Efficient—less to update |
Slower—same data may need to be updated in many places |
| Read performance |
Slower—requires joins |
Faster—pre-joined or duplicated fields |
| Query complexity |
Higher—JOINs and normalization-aware query logic |
Lower—simpler, more direct queries |
| Storage usage |
Lower—no duplicated values |
Higher—redundant data across rows |
| ETL/ELT complexity |
Simple—clear rules, clean transformations |
Complex—requires sync logic for duplicated fields |
| Schema evolution |
Easier—each table models one concept |
Harder—changes may cascade across multiple fields |
| Best suited for |
OLTP, operational systems with strong consistency needs |
OLAP, analytics, dashboards, and real-time reporting |
When Normalization Makes Sense
Use normalization when:
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You are building transactional or write-heavy systems.
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Data accuracy and referential integrity are critical (e.g., banking, logistics, CRM).
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You want a schema that is easy to evolve, extend, and maintain.
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You aim to minimize update, delete, and insert anomalies.
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JOIN performance is not a bottleneck, or the system supports JOINs efficiently.
Normalization is especially strong in OLTP workloads, where multiple users are constantly modifying data and strict consistency is required.
When Denormalization Makes Sense
Use denormalization when:
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You are building a read-heavy system where speed is critical.
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You want to reduce query complexity, especially for dashboards or BI tools.
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You need pre-joined, aggregated, or wide-format data for performance reasons.
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JOIN performance is poor, or JOINs add unacceptable latency to user-facing queries.
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You can tolerate or engineer around occasional inconsistency.
Denormalization shines in OLAP workloads, particularly when latency, concurrency, and responsiveness are top priorities.
Pitfalls to Consider
Risks of Over-Normalization:
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Excessive joins on large tables can lead to slow queries.
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Complex query logic makes onboarding, debugging, and development harder.
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Normalized schemas can be too rigid for exploratory or ad hoc analytics.
Risks of Over-Denormalization:
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Data drift becomes real—out-of-sync values across the schema.
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Schema changes require global rewrites or heavy backfill pipelines.
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Writes are more expensive, especially when upstream fields change frequently.
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More logic is pushed into ETL pipelines or application code.
Why You No Longer Need to Choose—If You're Using StarRocks
The normalization vs. denormalization debate often ends in a compromise: either you suffer the bloat and inflexibility of denormalized schemas or struggle with poor join performance on normalized ones. But that compromise is quickly becoming outdated.
StarRocks, particularly in its managed form via CelerData Cloud, offers a third option—one that supports normalized schemas at scale, without sacrificing performance.
To illustrate this, let’s look at Demandbase, a leading provider of B2B account-based marketing solutions. Their core product, Demandbase One, powers customer-facing analytics for thousands of users and integrates terabytes of marketing, sales, and advertising data into a single analytics platform. The architecture behind this experience had to support:
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Dozens of highly customizable reports.
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Live, interactive queries with strict performance requirements.
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Semi-structured data formats (e.g., nested JSON).
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Constant schema evolution as customers onboard new data sources.
Why StarRocks Eliminated the Need for Denormalization
StarRocks enabled Demandbase to normalize their data model and still meet sub-second latency goals, thanks to architectural advantages purpose-built for join-heavy workloads:
1. Distributed, High-Performance JOINs
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Broadcast and Shuffle Join Support: Selects the best join strategy based on table size and distribution.
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Colocate Joins: Ensures related data lives on the same node, turning distributed joins into fast, local operations.
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Runtime Filters: Filters irrelevant data early during scans based on join keys from the smaller table, reducing I/O and compute cost.
2. Advanced Query Optimization
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Cost-Based Optimizer (CBO): Reorders joins and rewrites plans using real-time statistics to avoid performance pitfalls.
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Foreign Key Constraints: Enables join elimination when no columns are selected from related tables—saving query time.
3. Real-Time Ingestion and Schema Evolution
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Data was streamed via Debezium + Kafka into StarRocks using routine load, achieving sub-minute freshness for key lookup tables.
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StarRocks’ support for partial updates made it possible to apply column-level changes without recomputing the entire dataset—enabling real-time changes without denormalizing.
4. Efficient Semi-Structured Data Handling
From 40 Clusters to 1: The Power of Normalization at Scale
By transitioning from denormalized views in ClickHouse to normalized tables in StarRocks, Demandbase:
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Shrunk their total infrastructure footprint from 40 clusters to one.
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Reduced storage usage by over 10x, avoiding duplication of large account and person objects.
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Cut ETL recompute time from days to minutes.
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Enabled live dashboards and report builders to query against normalized data using StarRocks’ powerful join engine and CBO.
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Achieved faster onboarding of customer data and schema changes—without breaking production pipelines.
Notably, Demandbase simulated the old denormalized views using StarRocks views, allowing their legacy queries to work unchanged. This enabled a fast, low-risk migration while gradually reaping the benefits of a normalized model.
Rethinking Denormalization: It’s Not the Default Anymore
The key insight from Demandbase’s journey is this:
When JOINs are fast and predictable, denormalization becomes a choice—not a requirement.
With StarRocks, normalization no longer implies sacrificing performance. Instead, it allows for a more maintainable, agile, and storage-efficient architecture—without giving up interactivity or speed.
So the question shifts from “how can we avoid JOINs?” to “can our engine handle them at scale?” With StarRocks, the answer is unequivocally yes.
FAQ: Normalization, Denormalization, and Modern Analytics with StarRocks
What’s the main difference between normalization and denormalization?
Normalization reduces redundancy by splitting data into multiple related tables. Denormalization does the opposite—merging tables and duplicating data to reduce the need for joins during queries.
Why would I choose normalization?
Use normalization when:
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Data integrity is critical
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You're dealing with transactional systems (OLTP)
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You want cleaner schema evolution
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Your system or engine supports fast JOINs
Why would I choose denormalization?
Use denormalization when:
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You need fast reads in a read-heavy environment
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You’re building dashboards or BI tools
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JOIN performance is poor or unpredictable
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Schema evolution is rare, and you can manage data consistency externally
What are the risks of over-normalizing?
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Performance bottlenecks due to excessive joins
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Complicated query logic, especially for ad hoc reporting
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Difficult onboarding and debugging
What are the risks of over-denormalizing?
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Redundant data increases storage usage
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Writes become expensive and error-prone
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Schema changes require full backfills or ETL rewrites
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Data inconsistencies can creep in without tight coordination
Can I use both normalization and denormalization in the same system?
Absolutely. Many systems use a hybrid model—keeping core entities normalized while denormalizing some views or summary tables for performance.
What is BCNF and how is it different from 3NF?
BCNF (Boyce-Codd Normal Form) is a stricter version of 3NF. It eliminates edge cases where a non-primary candidate key can cause functional dependency violations that 3NF does not catch. It's often useful in schemas with overlapping candidate keys or composite keys.
When should I stop at 3NF or BCNF instead of going to 4NF or 5NF?
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Stop at 3NF or BCNF when the schema is stable, join performance is acceptable, and there are no multi-valued or complex join dependencies.
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Go to 4NF/5NF only when dealing with highly interrelated, multidimensional data and advanced modeling requirements.
Why was denormalization historically favored in analytics?
Because many OLAP engines (like ClickHouse) struggle with high-performance joins at scale. Flattened tables were the only way to meet real-time demands—until newer engines like StarRocks emerged.
How does StarRocks change the equation?
StarRocks was built for modern, real-time analytics workloads. It eliminates the need to denormalize by offering:
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High-performance distributed joins (broadcast, shuffle, colocate)
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Cost-based optimizer (CBO) with automatic join reordering
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Runtime filtering to reduce unnecessary data scans
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Native JSON support and partial update capabilities
Can you give a real-world example of this in practice?
Yes—Demandbase, a B2B marketing analytics company, migrated from ClickHouse (with 40 clusters of denormalized data) to a single StarRocks cluster. They normalized their schema, cut storage usage 10x, improved data freshness, and retained sub-second query performance. StarRocks let them simulate old denormalized views with fast joins, enabling a seamless migration.
What if I’m working with Iceberg or a lakehouse architecture?
StarRocks integrates natively with Apache Iceberg (e.g., via external tables or preloading). This lets you keep data in open formats while supporting fast, interactive queries—ideal for modern data platforms where governance and real-time performance both matter.
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