Do you struggle to make sense of the enormous amount of information your business generates every day? Well, you’re not alone. The global data warehousing market is forecast to reach US$ 64.79 billion by 2030, indicating that organizations must manage their data effectively.
Many, however, are not there yet. A bewildering 95% of data warehouse projects fail to meet business requirements.
So, what goes wrong? And more importantly, how can you dodge this fate?
Building a data warehouse with a strong foundational design or schema is essential here. We are now going to dive deep into the Snowflake schema in data warehousing, a highly effective approach.
Let’s dissect this sophisticated data modeling technique to understand how it can reduce data redundancy, leading to improved data integrity and higher-quality insights.
What is a Snowflake Schema in Data Warehousing?
A snowflake schema in data warehousing is a multi-dimensional data model where dimension tables are segmented into subdimensions. Its common usage is seen in business intelligence and reporting in data marts, relational databases, and OLAP data warehouses.
Snowflake schemas are highly valuable when managing large and complex datasets. Thus, large organizations dealing with substantial data and having intricate analytical needs benefit the most.
Snowflake schema is an extension (a more complex version) of a star schema. Its central ‘fact table’ comprises all the information about events. It also has multiple ‘dimension tables’ with information about these events’ dimensions. The dimension tables are a database (DB) structuring strategy to reduce redundancy and overhead.
To truly understand this DB schema, here is a peek into its key components.
Components
The Snowflake database schema encompasses three core components in a hierarchical structure. It is a complex structure with more joins, but it provides higher data integrity and lower redundancy.
Fact Table
A fact table is the central table holding the largest volume of data. It contains quantitative measures, including profit, sales amount, quantity, etc., along with foreign keys linking to the dimension tables.
Dimension Tables
Dimension tables offer descriptive attributes that add context to the fact table measures. The schema normalizes these dimensions and breaks down hierarchical levels.
Subdimension tables
Sub-dimension tables are a distinguishing feature of the snowflake schema and hold specific attributes of a dimension to create the true ‘snowflake’ pattern.
Key Characteristics of the Snowflake Schema
Snowflake schema is an efficient and powerful data warehouse structure that uses your data optimally. Its distinctive characteristics make it stand out from its simpler cousin, the star schema.
Let’s check them out:
Hierarchical Structure
It follows a hierarchical structure containing fact tables, dimension tables, and subdimension tables. These tables contain linked attributes for reduced redundancy.
Joins
SQL queries require multiple table joins due to high hierarchical dimension tables.
Normalization
Its design of organizing data into multiple related tables is normalized.
Multiple Level
All of its various dimension table levels are linked to the central fact table.
Try out data warehousing services by Aegis to achieve the maximum benefits.
Advantages of Snowflake Schema Design
Businesses drawing information from a gigantic amount of data rows in a product or customer dimension table benefit from this schema. Popular industries utilizing the Snowflake schema include logistics and supply chain, finance and banking, retail, telecommunications, BI and analytics, healthcare, and e-commerce.
Its extensive list of benefits stems from its unique characteristics. A few key advantages of snowflake schema design are:
1. Improved Data Integrity
All the information gets stored only once, making it easier to manage updates and changes. It ultimately reduces the risk of inconsistencies and improves the overall data integrity.
2. Reduced Data Redundancy and Storage Efficiency
The snowflake schema normalizes dimension tables, eliminating duplicate data. It makes the storage more efficient and uses less disk space.
3. Better Scalability and Flexibility
The structure is appropriate for large, hierarchical, and complex datasets. It can easily adapt to your changing business requirements while sporting granular analysis.
4. Support for Complex Hierarchical Reporting
Its hierarchical structure stands on a central fact table containing the measures of interest. It segregates into dimension tables with the context providing attributes. The entire structure supports complex hierarchies, making it highly useful for multi-level reporting.
5. Simplified Data Maintenance
You can analyze data at different levels since this schema supports drill-down analysis and multi-level relationships.
If you can find the right Snowflake consulting expert who understand the importance of schemas and how to implement them for DWHs, you can gain all the above-listed perks.
Challenges and Disadvantages of Snowflake Schema Implementation
Despite all the advantages, there are certain challenges and disadvantages to snowflake schema implementation. It excels in certain aspects but also tags along potential pitfalls, particularly due to its deeply hierarchical data scenarios.
Here are a few hurdles to expect during the deployment and operations of a data warehouse built on a snowflake schema. A few common outcomes can include higher development costs and unforeseen performance bottlenecks.
1. Increased Query Complexity
The normalization feature can render this schema more complex to design and understand. Additional tables and relationships can increase the intricacy level, especially for large data warehouses. You might face difficulty when navigating a schema.
2. Higher ETL/ELT Complexity
The complexity level of the ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) processes increases. This largely happens because you’re in charge of managing data loading, transformation, consistency, and integrity of a hefty number of tables.
3. Design and Management Overhead
The need for multiple joins stretches the processing time and overhead. It ultimately slows down the query execution, especially if the dataset is very large or the analytical queries are complex.
4. Challenging Schema Evolution
Evolving the schema, like adding new hierarchies or attributes, can be more challenging. It is mostly because one change impacts multiple existing queries and tables.
5. Impact on OLAP Tools
More joins mean higher impact on the performance of interactive querying and cube processing in OLAP (Online Analytical Processing) environments.
Snowflake Schema vs Star Schema
Two foundational data modelling approaches in data warehousing are the snowflake schema and the star schema. Your analytical goals and data requirements form the basis of the final data structure you implement. It also means thoroughly evaluating the objectives of your data warehousing application.
| FEATURE | SNOWFLAKE SCHEMA | STAR SCHEMA |
| Structure | The central fact table is connected to normalized dimension tables, which are further connected to sub-dimension tables | The central fact table is connected to denormalized dimension tables |
| Data Redundancy | Lower redundancy | Higher redundancy |
| Data Integrity | Higher data integrity because of normalization | More susceptible to data anomalies upon improper management |
| Normalization | Normalized with hierarchical attributes broken down into separate but related sub-dimension tables | Denormalized with all dimension attributes typically found in a single table |
| Storage Space | Uses less storage space | Uses more storage space |
| Query Performance | Slower query performance due to the overhead of more joins | Faster query performance due to fewer joins |
| Use Cases | High data integrity, detailed hierarchical analysis, and minimal redundancy | Fast query performance, ease of use, and simplicity |
Building a Smarter Data Foundation with Aegis
What you need to transform your data into actionable insights is a truly smart data foundation—a well-designed Snowflake schema. With this strategic move, you can break down silos, use insights in real time, and make bolder decisions.
To make things easier and to increase the chances of success, you need a development partner. At Aegis, our SnowPro-certified developers utilize their hands-on experience for customized business solutions across industries.
Intrigued and ready to build a smarter data foundation? Connect with our certified experts for Snowflake development services for a chance to transform your data into your most valuable asset.
FAQs
Q1. What is Starflake schema?
Starflake schema is a hybrid modeling approach that combines the best elements of the star schema and the Snowflake schema.
Q2. Why is it called a star schema?
The star schema gets its name from its star-like structure. It has a central table with multiple other tables around it.
Q3. What is a Snowflake schema in data mining?
A Snowflake schema in data mining is a kind of DB schema. It normalizes dimension tables for lower redundancy and high data integrity.
