How Materialized Views Boost Query Performance and Simplify Data Access

When data stored in one or more databases is not in a format suitable for required query operations, generating

prepopulated views

helps support efficient queries and improve application performance.

Context and problem

Developers and data administrators often focus on how data is stored rather than how it is read. The chosen storage format is tied to data size, integrity, and type. For example, NoSQL document stores represent data as aggregates containing all entity information, which can negatively impact queries that need only a subset of entity data, such as order summaries for multiple customers.

Solution

A common solution is to generate a view in advance that materializes data in a format suited to the desired result set. The

Materialized View pattern

describes generating

prepopulated data views

when source data is unsuitable for queries, making queries difficult, or performance suffers due to data nature.

These materialized views contain only the data needed for queries, allowing applications to retrieve information quickly. Views can include computed columns, current values, combined values, or transformation results, and can be optimized for specific queries. The view and its data are fully discardable and can be rebuilt from source data; they are never updated directly by the application, acting as a dedicated cache.

When source data changes, the view must be refreshed, either automatically, on detection of changes, or manually. The diagram below illustrates possible usage of the materialized view pattern.

Issues and considerations

How and when the view is refreshed; automatic refresh on source changes may cause overhead if changes are frequent, so scheduled tasks, external triggers, or manual regeneration may be needed.

In event‑sourced systems, materialized views are essential; otherwise assess whether they add value, as they are tailored to specific queries and can increase storage costs.

Consider data consistency when generating or updating the view; concurrent source changes may lead to temporary inconsistencies.

Decide where to store the view; it need not reside in the same storage or partition as the source data.

If the view is transient, it can be stored in cache or less reliable locations.

Maximize view value by adding computed or transformed columns based on existing data or query parameters.

When supported, index the materialized view to further improve performance; many relational databases and Hadoop‑based solutions support view indexing.

When to use this pattern

When data is difficult to query directly or requires complex queries to extract from normalized, semi‑structured, or unstructured storage.

Creating a temporary view can significantly improve query performance or serve as a source for UI, reports, or data transfer objects.

Supporting occasionally connected scenarios where the data store is not always available; the view can be cached locally.

Simplifying queries and exposing data without requiring knowledge of the source format, e.g., joining tables across databases or domains.

Providing access to a subset of source data for security or privacy reasons.

Bridging different data stores to leverage their strengths, such as using efficient cloud storage for reference data and a relational database for the materialized view.

In micro‑service architectures, keeping services loosely coupled; materialized views can integrate data from multiple services.

Situations where the pattern is less useful:

Source data is simple and easily queryable.

Source data changes very rapidly, making view maintenance overhead prohibitive.

Strong consistency is required; the view may not always be perfectly in sync with source data.

Workload design

Architects should evaluate how the materialized view pattern fits into workload design and supports the pillars of the Azure Well‑Architected Framework, such as performance efficiency by storing complex computation results and reducing overall resource consumption.

Example

The diagram below shows an example of generating a sales summary using the materialized view pattern. Data from orders, order items, and customers across different partitions in an Azure storage account are combined into a view that contains total sales per product in the electronics category and the number of customers who purchased each item.

Creating this materialized view requires a complex query, but exposing the result as a view allows users to retrieve it easily for reporting or dashboards, with scheduled updates (e.g., weekly).

While this example uses Azure Table storage, many relational database management systems also provide native support for materialized views.

Next steps

Data consistency basics: maintain updated summary information in materialized views, possibly using eventual consistency when real‑time updates are impractical.

Related patterns when implementing this approach:

Command Query Responsibility Segregation (CQRS) – update materialized views in response to events.

Event sourcing – often combined with CQRS to maintain view information.

Indexed table pattern – create secondary indexes for views when the underlying store lacks native secondary indexing.