What is it: Materialized View Process

Screenshot 2023-12-06 at 12 16 45 PM

Building a data materialized view involves several key components that work together to create and maintain a persistent representation of data for efficient querying and analysis. These components include:

  • Data Source: The data source provides the raw data that will be materialized into the view. This can be a relational database, a data warehouse, or any other data storage system.
  • Materialized View Definition (MVD): The MVD defines the view’s structure and content, specifying the source tables, columns, and any transformations or aggregations to be applied. It outlines the data elements and operations involved in creating the view.
  • Materialization Engine: The materialization engine is responsible for generating and maintaining the materialized view. It extracts data from the source, applies the MVD’s transformations, and stores the resulting data in a physical format.
    Refresh Mechanism: The refresh mechanism ensures that the materialized view remains up-to-date with the source data. It detects changes in the source data and triggers a refresh process to update the view accordingly.
  • View Metadata: View metadata stores information about the view’s structure, dependencies, and refresh history. It provides context and insights into the view’s creation and maintenance.
  • Access Control: Access control mechanisms regulate user access to the materialized view, ensuring that only authorized users can query and modify the view data.
  • Performance Optimization: Performance optimization techniques are employed to improve the efficiency of materialized view creation, querying, and maintenance. This includes indexing, data partitioning, and query caching.
    Monitoring and Alerting: Monitoring and alerting mechanisms track the health and performance of the materialized view system, identifying potential issues and triggering notifications for corrective actions.
  • Change Data Capture (CDC): Change Data Capture (CDC) technology enables efficient incremental updates to materialized views when the source data changes. It captures the specific changes made to the source data, rather than rematerializing the entire view.
  • Distributed Materialized Views: Distributed materialized views span multiple nodes or servers, allowing for large-scale data materialization and querying across distributed data sources. It enables efficient processing and analysis of data stored in multiple locations.
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