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Create view redshift1/14/2024 ![]() ![]() During each data load, incoming change records are matched against existing active records, comparing each attribute value to determine whether existing records have changed or were deleted or are new records coming in. Changes are inserted as new active records effective from the date of data loading, while simultaneously expiring the current active record on a previous day. SCD tables contain a pair of date columns (effective and expiry dates) that represent the record’s validity date range. Populating an SCD dimension table involves merging data from multiple source tables, which are usually normalized. As part of data loading, the dimension tables, including SCD tables, get loaded first, followed by the fact tables. However, declaring them will help the optimizer arrive at optimal query plans, provided that the data loading processes enforce their integrity. In the case of Amazon Redshift, uniqueness, primary key, and foreign key constraints are not enforced. This is captured in the form of primary key-foreign key relationships, where the dimension table primary keys are referred by foreign keys in the fact table. In a star schema data model, the central fact table is dependent on the surrounding dimension tables. Therefore, dimensions in a star schema that keeps track of changes over time are referred to as slowly changing dimensions (SCDs).ĭata loading is one of the key aspects of maintaining a data warehouse. Relative to the metrics data that keeps changing on a daily or even hourly basis, the dimension attributes change less frequently. Time travel is possible because dimension tables contain the exact version of the associated attributes at different time ranges. The star schema data model allows analytical users to query historical data tying metrics to corresponding dimensional attribute values over time. Whereas operational source systems contain only the latest version of master data, the star schema enables time travel queries to reproduce dimension attribute values on past dates when the fact transaction or event actually happened. Dimensions provide answers to exploratory business questions by allowing end-users to slice and dice data in a variety of ways using familiar SQL commands. ![]() A dimension is a structure that captures reference data along with associated hierarchies, while a fact table captures different values and metrics that can be aggregated by dimensions. ![]() Star schema and slowly changing dimension overviewĪ star schema is the simplest type of dimensional model, in which the center of the star can have one fact table and a number of associated dimension tables. In this post, we show how to simplify data loading into a Type 2 slowly changing dimension in Amazon Redshift. The star schema is a popular data model for building data marts. Organizations create data marts, which are subsets of the data warehouse and usually oriented for gaining analytical insights specific to a business unit or team. Thousands of customers rely on Amazon Redshift to build data warehouses to accelerate time to insights with fast, simple, and secure analytics at scale and analyze data from terabytes to petabytes by running complex analytical queries. ![]()
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