Why Choose Standard Views Over Materialized Views in Snowflake?
Get it right.
As we know, both standard views and materialized views serve as powerful mechanisms to simplify query complexity and enhance data accessibility. However, deciding whether to use a standard view or a materialized view depends on your specific use case and performance needs. In this bit o writing, we’ll explore the reasons why you might choose to use standard views over materialized views in Snowflake.
Understanding Standard and Materialized Views
Before diving into the reasons, it’s essential to understand the basic differences between standard views and materialized views:
- Standard Views: A standard view is essentially a saved SQL query that is executed on-demand. When you query a standard view, Snowflake dynamically retrieves the data from the underlying tables based on the view’s query logic.
- Materialized Views: A materialized view, on the other hand, stores the result set of a query physically in the database. When you query a materialized view, Snowflake retrieves the precomputed data, which can lead to faster query performance but at the cost of storage and maintenance overhead.
Reasons to Choose Standard Views
- Real-Time Data Access
- Standard views provide real-time access to the underlying data. Whenever you query a standard view, it pulls the latest data directly from the tables. This is ideal when your use case requires up-to-the-minute accuracy, such as in dashboards or real-time analytics.
- No Storage Overhead
- Unlike materialized views, standard views do not require additional storage because they don’t store the query results. This can be particularly beneficial if you’re working with large datasets and want to minimize storage costs while still organizing your data through reusable query logic.
- Simpler Maintenance
- Standard views don’t require periodic refreshes or maintenance. Since they are dynamically executed, there’s no need to worry about keeping the data up-to-date, which simplifies the management and reduces the operational overhead.
- Lower Cost
- Because standard views don’t store any data, you avoid the additional costs associated with storage and maintenance. This can result in significant cost savings, particularly for use cases where the performance boost provided by materialized views is not necessary.
- Flexibility and Agility
- With standard views, you have the flexibility to modify the underlying data structures without needing to rebuild or refresh the views. This agility allows for faster iteration and adaptation to changing business requirements or data models.
- Ideal for Complex, Infrequent Queries
- If you have complex queries that are not run frequently, standard views can be a better choice. The dynamic nature of standard views ensures that you don’t incur the overhead of storing and maintaining precomputed results that are rarely accessed.
- Separation of Logic and Data
- Standard views enable a clean separation between query logic and physical data storage. This separation can lead to better code organization, easier debugging, and more straightforward query optimization.
When to Consider Materialized Views
While standard views offer numerous advantages, there are cases where materialized views might be more appropriate. If your queries are frequently run and involve large datasets or complex joins that result in significant performance delays, materialized views can drastically improve query performance by serving precomputed results.
Conclusion
Choosing between standard views and materialized views in Snowflake is ultimately about balancing the needs for performance, real-time data access, cost, and maintenance overhead. Standard views shine in scenarios where real-time data is crucial, costs need to be minimized, and simplicity is valued. On the other hand, if query performance is a bottleneck and you can afford the storage and maintenance costs, materialized views might be the right tool for the job.
In many cases, a combination of both standard and materialized views can be used to optimize different aspects of your data architecture, allowing you to leverage the strengths of each based on specific use cases. Understanding the trade-offs and benefits of each type of view will help you make informed decisions that align with your business goals and technical requirements.
As always, it’s essential to consider your specific needs and use cases when deciding which approach to take. In many instances, the flexibility and cost-effectiveness of standard views make them an excellent choice for a wide range of applications in Snowflake.
