Star Schema vs Snowflake Schema: Building a Case in Favor of the Star Schema

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Deciphering massive information streams to discover hidden insights is an essential aspect of today’s business environment, and a go-to solution that is time-tested and proven as a dependable data modeling solution is the deployment of a Star Schema.

With the help of business leaders and data experts, this post builds a case in favor of the Star Schema, discussing scenarios that render this schema a perfect fit.

We’ve even lined up for you a Techronicler roundup where 9 business leaders and experts discuss in detail their experience with a Star Schema and reasons for voting it the schema of choice.

Insight Roundup: Experts and Business Leaders Tilt the Scale in Favor of the Star Schema

Also, don’t forget to check out the other side of the conversation featuring the Snowflake Schema’s exhaustive list of advantages.

Star Schema vs Snowflake Schema: Building a Case in Favor of the Snowflake Schema

Strategic Advantages of the Star Schema

Faster Query Performance

Reduced joins leading to computationally less exhaustive operations, pre-aggregated data that further reduce calculations required for query executions, and optimization of Business Intelligence tools that lead to efficiency in data retrieval and visualization are among the first advantages of the Star Schema. To some businesses, it’s the faster query performance that stands out.

James Owen, Co-founder, Click Intelligence leads this conversation, pointing out why the Star Schema works better for their needs. “The reason is simple, it offers faster query performance,” says James.

“With its structure, data is organized into a central fact table and a few related dimension tables. This setup means fewer joins are needed, which speeds up query execution. For analytical queries, this speed is important.”

Reviewing the Snowflake schema in line with these details, James deems the Star schema to be faster. “Snowflake schemas involve multiple normalized tables, which means more joins to get the data. This can make queries take longer and can be more complex for users to understand and analyze. Even though modern database optimizations have improved the performance of Snowflake schemas, the gap in performance between the two is still noticeable.”

Model Development

In the case of Umair Majeed, CEO, Datics AI, the requirements of data analytics and software engineering for high-performance querying to implement machine learning models and gain business insights is what drives the choice for the Star Schema.

“The star schema’s denormalized structure optimizes query speed, enabling fast prototyping and iteration,” says Umair.

“For example, when building a product recommendation engine, we needed to analyze customer behavior across multiple touchpoints – web, mobile, and in-store. The star schema’s dimensional modeling allowed us to connect data points and see the full customer journey. We identified key correlations within days rather than weeks, accelerating model development.”

“While the snowflake schema may suit some use cases, in data science, performance is paramount. The star schema’s simplified, denormalized design gives us speed and flexibility to uncover key insights and build innovative solutions. Despite the extra storage space, the performance benefits of a star schema are essential for our business.”

Feature engineering is indeed a smooth affair in a Star schema, with pre-aggregated data providing readily available features for model training. Direct relationships make it simpler to join and combine data, and feature creation and selection become more efficient.

With faster data retrieval and a reduced memory footprint added to a list that includes a focus on predictive analytics, a Star schema indeed proves advantageous for a scenario revolving around model development.

Speed to Insight

The distinct advantage of the Star Schema in this scenario is simplicity and performance—it provides clean, intuitive table structures that enable quick insights without sacrificing efficiency, “ says Cache Merrill, Founder of Zibtek.

“Its denormalized structure, where fact tables are directly connected to dimension tables, offers significant speed advantages. This is particularly beneficial in environments requiring frequent, quick reports or dashboards, such as retail or sales analytics.”

“Compared to the Snowflake Schema, the Star Schema minimizes the number of joins, leading to faster queries, even as data scales. While Snowflake schemas reduce storage redundancy, the performance overhead due to multiple joins can hinder real-time reporting.”

“Choose a Star Schema when speed to insight is the business’s north star,” Cache concludes.

“Thinking of scenarios where companies have to access their data on run mode, say in fast-moving industries like retailing, I have always been inclined toward the Star Schema,” says James White CEO of Amazing Moves, reiterating why the schema is a popular choice.

“For example, in e-commerce, on days of major sales, the role of real-time analytics is very important. The star schema architecture provides for speedier access to customer behavior and sales trends, which might spell the difference between maximizing revenues or missing key opportunities. And it’s pretty user-friendly, too-meaning even nontechnical teams are able to pull reports with ease, without needing the assistance of a data engineer always,” James adds.

Yet another business leader, Aziz Bekishov, CEO of DC Mobile Notary, relays the advantages of the schema in terms of “fast, intuitive reporting with minimal complexity”.

“In scenarios like retail sales analytics, where quick querying of aggregated data (like sales by region or product) is essential, the Star schema shines. It’s easy to understand, as it places fact tables at the center with direct connections to dimension tables. This reduces the need for complex joins, speeding up queries,” says Aziz.

Simplicity

“Star schema is best suited for businesses that need good query performance and simplicity,” says Andrew Johnson, Digital Strategist at Giveaways. “Simple structure, faster queries, and efficient reporting. It has the ability to achieve scalability as opposed to the Snowflake schema, which is way less optimal.”

“Using the Star Schema, companies may extract insights from their data and take informed actions to increase sales and revenue as well as customer satisfaction. The schema is also put to use by retail companies to analyze purchase patterns of customers, most-sold products, and inventory optimizations,” reveals Andrew.

Mark McShane, Founder of Cupid PR agrees. “The Star Schema is simpler and faster to query which is important for generating campaign reports and metrics on a regular basis. With fewer joins required data from ads, clicks, conversions and client demographics can be accessed quickly and easily,” says Mark.

“For a small agency running ad campaigns and reporting to clients the Star Schema would be the better choice. The Star Schema is simpler and faster to query which is important for generating campaign reports and metrics on a regular basis. With fewer joins required data from ads, clicks, conversions and client demographics can be accessed quickly and easily.”

Mark attributes these advantages to the Star schema’s user-friendly design, saying, “Even non technical team members or marketing analysts with basic SQL skills can extract insights from the data without having to navigate complex relationships. This means reporting is faster and the agency can get results to clients quicker.”

For a smaller business with limited technical resources,” says Mark, “the Star Schema is a practical choice.”

Organized Structure

The organized structure of the Star schema is yet another factor that makes it a winner in certain scenarios. A straightforward structure helps even non-technical users understand its workings and run queries. Providing a clear and efficient way to model and analyze data, the schema works perfectly for data warehouses used for reporting and business intelligence.

“The star schema is like a very well-arranged filing system where fast data analysis is required,” says Alex Muz, Founder of StudyX.

“Using the star schema, the data needed for analysis stays in one place, kind of like having one particular drawer where things are already sorted and ready. This means you don’t have to hunt through different folders or files to find what you’re looking for, which saves time and makes things run smoother.”

“While snowflake schema will take much more time. In firms where data-driven decisions are important, accessing data in a fast way becomes critical. The star schema does its job by helping speed up retrieving data, which can be significant for making fast decisions in companies that depend on AI systems,” concludes Alex.

The Star Schema: A Simple Yet Powerful Schema Delivering Straightforward Performance

As these business leaders and experts have pointed out, the Star Schema sure has to its name a distinct set of advantages like faster query performance, model development, speed to insight, simplicity, and an organized structure.

The perfect combination of simplicity and performance make it a solution that works well for smaller businesses where denormalization and simple relationships deliver faster query performance and real-time insights.

Connected Posts:

Insight Roundup: Experts and Business Leaders Tilt the Scale in Favor of the Star Schema

Star Schema vs Snowflake Schema: Building a Case in Favor of the Snowflake Schema

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