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Amazon QuickSight is a fully managed business intelligence (BI) and data visualization service provided by Amazon Web Services (AWS). It enables users to create interactive and insightful visualizations from their data, empowering them to gain valuable insights, make data-driven decisions, and communicate complex information effectively.
Amazon Redshift offers materialized views as a solution to tackle these challenges. Materialized views store precomputed result sets derived from SQL queries on one or multiple base tables. When querying a materialized view, you can use SELECT statements just like with other tables or views in the database.
Create and Refresh Materialized Views
Creating a materialized view is based on the Amazon Redshift Tables:
CREATE MATERIALIZED VIEW <unique view name>
AS <select query>
Refreshing a materialized view is to update the unchanged data from an original table to the view and use the Refresh materialized view statement at any time to manually refresh the materialized views.
REFRESH MATERIALIZED VIEW <view name>
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How Materialized Views Are Better than Regular Views
- Regular View is a simple SQL query that does not store the table data and only results in the response for that moment. But Materialized View stores the table data physically of a specific query with a unique view name. The materialized view data is precomputed and stored as a table structure, which helps not to eliminate the data and recompute the query every time.
- While Regular View provides a logical representation of data, you need to execute queries each time, which can be time-consuming for complex queries and large datasets. However, the Materialized view improves the query performance as it has its pre-computed power, and results are available readily from the stored data in less time.
- Regular views will display real-time data in a base table and result in the latest data available during query execution. Materialized Views contain a snapshot of the data at their last refresh.
Steps to use Materialized Views in Amazon Quicksight
- Create a materialized view in Amazon Redshift Query Editor, stored in a public or a database.
- Connect Amazon Quicksight to the Data Source of Amazon Redshift.
- Import the created materialized view from a data source into Amazon QuickSight for visualization and analysis.
- Build Dashboards by analyzing the dataset of a materialized view.
- Use custom SQL query to the materialized view to enhance performance by utilizing the precomputed results.
- It is better to schedule the refresh materialized view query in the Amazon Redshift query editor so that the materialized view will be refreshed with the newly inserted data in the base table.
Advantages of Materialized Views
- Improves Query Performance – Querying a materialized view is much faster than querying the base tables directly. If Amazon QuickSight dashboards frequently perform specific aggregations or calculations, materialized views can boost performance. Instead of recalculating the same aggregations repeatedly, Amazon QuickSight can access the pre-computed results from the materialized views, improving dashboard interactivity and responsiveness.
- Reduces Database Workload – Materialized views store the precomputed results, which does not repeatedly affect the table for querying.
- Caching and Performance Optimization – The materialized view follows a caching mechanism for frequently accessed queries. They allow for faster data retrieval without recomputing the query results each time.
- Real-time data updates on Dashboard – Materialized views can be refreshed frequently (e.g., hourly or daily) to provide near-real-time data updates to Amazon QuickSight dashboards. This can help balance data freshness and query performance for your analytics.
The partnership between Amazon Redshift materialized views and Amazon QuickSight offers a potent combination for optimizing data analysis. With accelerated query performance, dynamic visualizations, and efficient resource allocation, Amazon QuickSight dashboards improve the speed of changing the data dynamically by using Amazon Redshift materialized views.
Drop a query if you have any questions regarding Materialized Views and we will get back to you quickly.
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1. Which databases support materialized views?
ANS: – Materialized views are supported by several relational database management systems (RDBMS), such as PostgreSQL, Oracle, SQL Server, and Amazon Redshift.
2. Are materialized views suitable for real-time data analysis?
ANS: – Materialized views can provide near-real-time data analysis by setting the schedule query feature in Redshift query editor with the below command in a specified interval. So that materialized views also get updated with the newly inserted data of a base table.
REFRESH MATERIALIZED VIEW <MATERIALIZED VIEW NAME>
3. Do materialized views offer only read mode?
ANS: – Materialized views are typically read-only, meaning you cannot directly modify the data in the view. The data in a materialized view results from a pre-computed query and is updated only through regular refreshes based on the data in the base tables.
WRITTEN BY Sridhar Andavarapu
Sridhar works as a Research Associate at CloudThat. He is highly skilled in both frontend and backend with good practical knowledge of various skills like Python, Azure Services, AWS Services, and ReactJS. Sridhar is interested in sharing his knowledge with others for improving their skills too.