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SQL views, materialized views, and tables: What scenarios are best for each?

A table is a storage architecture model that stores your data in an organized way (in rows and columns) in a database.

Although tables in SQL databases typically contain massive amounts of data, they’re not always in a format that can be used by data analysts. The data records in these tables need to be filtered before they can be used efficiently. Having the ability to filter your data sets is also important if you want to make only certain amounts of the table’s data available to certain users for security reasons.

The solution is to use SQL views, materialized views, or create a new table that contains only the filtered data the user needs.

A table is a storage architecture model that stores your data in an organized way (in rows and columns) in a database. You can query any table using predicates on the table’s columns. Views also called “named queries,” are used to simplify these queries and securely access data.

A table is essentially the physical storage of your data, whereas a view is a way of looking at that data. When you query a table, you fetch the data from the table directly. However, when you query a view, you’re querying a query that’s stored in the view’s definition.

Materialized views are similar to views in that they’re essentially views that have a query built into its definition, and the query uses that view to fetch data from the database table. The difference is that materialized views have their own storage, as well, that acts as a cache between the table and the queries that are operating on the materialized view.

Because materialized views have their own storage, they can be refreshed based on a specified schedule, which triggers the query to run again.

In this article, we’ll take a closer look at what SQL views and materialized views are and when you should use each one.

SQL Views

SQL views, also called “named queries” or “virtualized tables,” are derived from one (or multiple) tables in the database. This means that the rows of a view aren’t stored in the database and therefore don’t have a physical existence. Put simply, views are virtual-only and run the query definition when they’re accessed.

Here’s an example of when you should use SQL views.

Let’s say you have an acme_company database that stores information about Suppliers and Products. Since one supplier can have many products, there’s a one-to-many relationship here. Instead of creating a join between three tables – Suppliers, SuppliersMfgProducts, Products – whenever you need to get data from the tables, you can simply create a view. The view would form the virtualized table that’s created as a result of the query. The key benefit here is that you would now only be fetching data from one table instead of fetching data from three joined tables.

It’s also worth mentioning that SQL views make it easy to access multiple data types and store complex queries.

While SQL views make querying tables a bit more efficient, they also limit the types of operations (such as the update operation) that can be applied to them. In other words, views can be queries but can’t be updated. That said, the information in a view is always up to date, which implies that the view isn’t materialized when it’s defined. In fact, it fetches data when a query is specified on it. Generally speaking, the database management system (DBMS) you use is responsible for keeping views up to date.

It would be far more efficient to use a materialized view to prepare and aggregate the data you need once.

Materialized Views

In simple terms, materialized views are the results of a query that are saved as tables. This means that materialized views do have a physical existence, because they take up disk space in the server.

Here’s an example of when you should use materialized views.

When you’re working with large datasets, there’s always a chance that some queries will be referred to by numerous dashboards or data visualizations. Ideally, you don’t want to run these queries over and over again, as this takes up valuable computing resources.

It would be far more efficient to use a materialized view to prepare and aggregate the data you need once. This way, you can simply query against the materialized view. This is particularly useful if you build statistical models for answering predictive questions.

One of the key benefits of using materialized views is that the information underlying the query, i.e. the information that’s fetched from the database table, can be updated. This means that you don’t have to recreate materialized views each time they’re needed. As a result, this simplifies complex data preparation and aggregation.

Materialized views also make it easy to keep data in sync which is particularly useful for data analysts that work with dimension tables.

SQL Views vs Materialized Views: Which Is Better?

SQL views evaluate the data in the tables underlying the view’s definition when they’re queried. So, the benefit of using SQL views is that they always return the latest data. However, their efficiency depends entirely on the select statement they’re based on. For example, if the select statement joins multiple tables, the view likely won’t perform optimally.

On the flip side, with materialized views, the underlying query’s result has been saved to a table i.e. it takes up physical storage space on the server. This essentially means that when you query a materialized view, you’re querying a table. In this way, materialized views improve performance and allow you to maintain a source of truth. This also means that data analysts can clean and transform the data in-warehouse instead of when it’s being presented or used by other apps.

Materialized views take up physical storage space and have scheduled updates based on their query definition. So, the data you fetch from a materialized view is only as up to date as when the materialized view was last updated. Views, on the other hand, are virtual-only and only run the query definition when they’re accessed.

Materialized views bring many performance improvements to the table while ensuring data consistency by allowing you to pre-join complex views and compute summaries beforehand.

Conclusion

Companies deal with large, ever-growing datasets on a regular basis. As a data scientist, it’s important to take a step back and evaluate when it’s the right time to use SQL views, materialized views, and tables in your projects.

SQL views make accessing multiple data types and storing complex queries easier whereas materialized views can help you improve efficiency and boost performance.

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