A view in BigQuery is a saved query that you can reference like a table. BigQuery offers three kinds, and they solve different problems. Standard views and materialized views are mostly a question of cost and performance, while authorized views are a security mechanism. The Professional Cloud Database Engineer exam tends to test whether you can pick the right one for a given scenario, so it helps to be precise about what each type does and when you would reach for it.
Standard views are the most commonly used type. A standard view functions as a virtual table that represents the result of a query. It does not store any data of its own. Every time you access the view, the underlying query runs again to generate the results. Because the computation happens on every access, a standard view that is queried frequently tends to be more expensive, since the computation cost adds up over time.
That access pattern is what drives the decision. You would generally choose a standard view when the query is accessed infrequently, or when the computation size is low and the amount of data the query processes is relatively small. Standard views are also the better option when the storage costs for a materialized view would be too high or simply unnecessary for the use case. Finally, a standard view always displays the latest data without storing anything separately, so it is a good fit when you need real-time reflection of the underlying data and do not want to manage refreshes.
A materialized view stores pre-computed and cached query results. Instead of recalculating the data on every run, BigQuery keeps the results, which allows quicker access to data that is used repeatedly. For frequently accessed data, and especially for large datasets, this is usually the most cost-effective option. The pre-computed results reduce the computational charges that come with executing queries on the fly, so an organization can avoid paying for the same recomputation over and over.
You would choose a materialized view over a standard view when the query is accessed frequently, because the performance gains from caching outweigh the storage the cache requires. They are particularly useful when computation costs are high but storage costs are relatively low. The trade-off is that a materialized view does not reflect changes in real time the way a standard view does, so they fit best when real-time data reflection is not required.
The contrast between the two comes down to a small set of factors. Standard views compute on access and store nothing, which favors infrequent queries, low computation, and real-time freshness. Materialized views store cached results, which favors frequent access, high computation cost, low storage cost, and cases where stale data between refreshes is acceptable. On the Professional Cloud Database Engineer exam, a scenario that emphasizes a large dataset queried many times usually points toward a materialized view, while one that emphasizes infrequent access or a need for the absolute latest values points toward a standard view.
Authorized views serve a different purpose. They are a security mechanism that lets users query specific data without granting them access to the underlying tables. You define a query that limits what is shared, and users see only the output of that query. The actual source tables stay protected. This is useful when sensitive data needs to be guarded but certain results still need to be shared.
By exposing only the result of a defined query, an authorized view lets an organization control data visibility and maintain stricter data governance. It is especially valuable in multi-user environments where different users or teams need different levels of access. You can share the relevant results with each group while keeping the full dataset out of reach, which enhances security without forcing people to work blind. Where standard and materialized views are about how a query result is computed and cached, an authorized view is about who is allowed to see it.
Most questions reduce to two distinctions. First, between standard and materialized views, decide on access frequency, computation size, storage cost, and whether real-time freshness matters. Second, recognize that authorized views are not in that cost-and-performance conversation at all. If a scenario is about restricting access to sensitive data while still sharing a defined slice of it, the answer is an authorized view, regardless of how the underlying query performs.
Our Professional Cloud Database Engineer course covers BigQuery views alongside BigQuery cost controls and data access governance, with practice questions that drill these distinctions.
