How to Choose the Right Data Warehouse Architecture for Your Business Needs
25 April 2024
Unlock The Power of Your Data: Data Lake vs. Data Warehouse for Smarter Business Decisions (2024)
26 April 2024
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Published by
BluePi
Data-Driven Business Transformation
Star Schema vs Snowflake Schema: find out the Warehouse model that is right for you
Discussion of Data Warehouse Design in the Introductory Section
Information retrieval from data warehouse systems depends to a great extent on proper data structuring and organization, as it allows for speedy and precise analysis and report preparation.
To deal with the data modeling challenges dimensions data warehouses are facing, two different techniques: Star Schema and Snowflake Schema, are widely used as industry standards.
This distinctiveness and commonality allow each approach to offer unique benefits in response to specific business needs.
This comprehensive guide will disentangle the particularities of the two schemas, we’ll explore the differentiation between them, and every recommendation will only help you choose the right design for your enterprise’s data warehouse.
One of the key concepts is understanding the Star Schema
The choice of the Star Schema design is the dimensional model technique involving the creation of the central fact table surrounded by multiple dimension tables. This format is similar to a star figure, with the fact table at the core while the dimension tables radiate outward from the fact table.
Key characteristics of the Star Schema:Key characteristics of the Star Schema:
1. Fact Table
The numbers showing up in this table are usually intangible correlates or numbers, most often seen as information or measures. Such examples are transactions connected to sales, website visits, or accounts under financial control.
2. Dimension Tables:
These tables are responsible for storing description attributes, which may, for instance, serve as product details, customer information, or geographical data holders.
3. Denormalized Structure:
Dimension tables for star schema design are, in most cases, denormalized with the aim of enhancing compute speed. Moreover, the need to join tables is minimized by doing so.
Pros:
- Symmetric approach, easy to follow, and practically working.
- With its specialization in read-heavy jobs and quick operation, it is tuned for fast query performance.
- Ensures data reliability and efficiency using aggregations and summarizations.
- For use where you need to analyze the data or specify the event on different levels.
Cons:
- Denormalized structure causes the data to be redundant and, together, leads to an escalation of storage necessities.
- The absence of the tools of data visualization that allow one to handle sophisticated hierarchies and relationships makes it challenging to represent things like high-level decisions or collaboration.
– Because of the denormalization, which is quite slow for write-heavy procedures, it may not be so fast for queries containing much data.
Exploring the Snowflake Schema
The Snowflake Schema is an extension of the Star Schema, designed to handle more complex data relationships and hierarchies. In this schema, dimension tables are further normalized into multiple levels, creating a structure resembling a snowflake.
Key characteristics of the Snowflake Schema:
1. Fact Table:
2. Dimension Tables:
Dimension tables are divided into multiple levels, with each level representing a different level of detail or hierarchy.
3. Normalized Structure:
Dimension tables in a Snowflake Schema are highly normalized, eliminating data redundancy and promoting data integrity.
Pros:
- Highly normalized structure reduces data redundancy and storage requirements
- Supports complex hierarchies and relationships between dimensional data
- Flexibility to handle changes in data structures and hierarchies
- Suitable for write-heavy workloads due to normalized structure
Cons:
- Increased complexity in design and implementation
- Potentially slower query performance due to multiple joins required
- More complex maintenance and administration tasks
- Less intuitive structure compared to the Star Schema
Factors to Be Aware Of When Making the Choice of Schema undefined
1. Data Complexity:
Estimate the difficulty of your data sources and the associations between dimensional data. If you adhere to simple and flat hierarchies, then this is enough for a Star Schema.
On the other hand, if you have intricate branches or complex relationships, a Snowflake Schema will be able to do a better job.
2. Query Performance:
Evaluate the nature of the activities you do. When you are mostly doing read-dominated operations such as reporting and analytics, the denormalized structure that is infused in a Star Schema allows for rapid query performance.
Also, if you have work that uses writing as a medium, the normalized Snowflake Schema may be preferable.
3. Data Volume and Storage:
Think about the amount of data that needs to be stored and the storage costs that entail. The normalized platform of the Snowflake Schema leads to decreased storage demand, whereas the denormalized structure of the Star Schema may lead to storage requirements based on data redundancy.
4. Skills and Resources:
Assess the knowledge and expertise of your data warehousing team. The Star Schema’s simplicity can be an asset during the startup of organizations or when the teams lack resources and experience. The Snowflake Schema’s complexity will require people with more complex skills and training as well.
5. Future Growth and Scalability:
Think of any possible growth in your data warehouse and design scalability. The flexibility of the Snowflake Schema in introducing complex hierarchies and relationships may be an advantage to you if you foresee major developments and changes in your data structure.
There are going to be trends like hybrid approaches and, of course, the best practices.
Mostly, the combined model, which is the step toward the direction of both Star Schema and Snowflake Schema, could be tailored to be the most useful one. This method would be able to synergistically combine both schematic highlights and address their weaknesses.
Best practices for data warehouse design include:Best practices for data warehouse design include:
Undertake diligent data exploration and information gathering to familiarize yourself with your data as well as the analytical needs of your scenario.
In the case of a simple reporting-oriented data marts, you might opt for a Star Schema and a Snowflake Schema in the large case of an analytical-oriented data warehouse.
Conduct data governance and documentation correctly to address problems of inconsistency and ensure the maintainability of data.
Keep checking, upgrading, and fine tuning your data warehouse architecture as your business conditions evolve.
Integrate data warehouse automation by utilizing tools and methods that will simplify the design/implementation workflow .
Conclusion:
Selecting the correct data warehouse design is among those objects that matter for the purpose of implementing true analysis, reporting, and decision-making in the company.
Nonetheless, each of the Star Schema and Snowflake Schema possesses its own advantages; the best option is the one that is best suited to your particular business needs, which are your data complexity, performance needs and requirements, and your future growth plans.
Through the process of scrutinizing these factors and adhering to the frameworks of good practices, it is possible to opt for the right schema for an optimal data warehouse or to use the hybrid approach to ensure the robustness and scalability of a data warehouse that brings data-driven insights and informative decisions.
About the Author
Published by
BluePi
Data-Driven Business Transformation
Published by
Divya Dass
A data-driven solutions architect, leverages his expertise in data science, data lake management, data warehousing, and cloud CDPs to lead impactful data projects across diverse domains. A skilled communicator and collaborator, Divya translates data insights into actionable business strategies, continuously evolving and optimizing data-driven operations within the company.
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